IBM has announced a series of updates to its IBM Bob software development tool, with a particular focus on better managing token consumption.

The move marks the first big batch of updates for the agentic development tool, which rolled out earlier this year. Bob will now offer multi-agent capabilities, the company said, alongside built-in AI cost and usage analytics features.

According to IBM, the new ‘Bobalytics’ feature will enable developers to monitor AI token consumption, streamline resource management, and improve visibility in terms of how AI tokens are consumed by teams.

Sub-agents and multi-agent capabilities will also play a key role in reducing costs, according to IBM. Sub-agents are those that operate under the direction of another to handle specific tasks within a larger workload.

The advantage here is that these dedicated agents will work in an “isolated context” within broader workloads to complete tasks in a more efficient manner, ultimately reducing costs.

Multi-agent features also contribute on this front, according to Michael Kwok, VP of IBM Bob. In a blog post detailing the updates, Kwok noted that Bob will now call on specific tools based on the underlying model, allocating agents depending on the intensity of tasks and associated costs.

“Parallel tool execution can significantly reduce the time required for complex tasks involving multiple searches, file operations and validation steps,” Kwok explained.

“Subagents improve efficiency by performing specialized work in isolated context before returning only the relevant results, while background tasks allow long-running work to continue without interrupting the developer’s workflow.”

AI cost optimization in the spotlight

The move by IBM comes amidst a sharpened focus on AI token consumption - and spiralling costs - by enterprises globally. As ITPro reported in June, the ‘tokenmaxxing’ trend has landed some organizations with eye-water bills.

Uber, for example, blew through its entire annual AI bill in just four months after incentivizing staff to ramp up use of the technology.

Developer teams are also contending with rising costs on this front, as Gartner told ITPro in late June. The consultancy projects that token costs could exceed engineer salaries by 2028 based on current usage rates.

Speaking to ITPro at the time, Nitish Tyagi, senior principal analyst at Gartner, said engineering teams are adopting a range of cost optimization practices to reduce the impact of token consumption.

New ‘premium packages’ for IBM Bob

IBM also unveiled three new specialized workflows for Bob aimed at driving Java, IBM Z, and IBM i operating system (OS) modernization. These capabilities come in separate ‘premium packages’, according to the firm, offering enterprises domain-specific versions of the tool.

“Modernizing a Java application requires different knowledge, tools and workflows than working with IBM i systems or mission-critical mainframe applications. Each environment carries its own architecture, runtime assumptions, integration patterns, validation needs and operational constraints,” Kwok explained.

“Premium Packages bring domain intelligence and decades of enterprise expertise directly into Bob while maintaining a consistent developer experience.”

The premium package for Java, for example, will help automate migration processes, allowing teams to move to more recent versions of the programming language. Bob will essentially guide users through the process, while also assisting with broader modernization practices.

This includes automated compatibility testing and the ability to identify and remediate vulnerabilities.

The premium package for IBM Z, meanwhile, will provide teams with a helping hand during mainframe modernization projects. The Z Code Mode feature, for example, will help “explain, generate, modify, refactor, and transform mainframe application code”.

Efforts to streamline mainframe modernization by the firm come after a high-profile spat with Anthropic earlier this year.

FOLLOW US ON SOCIAL MEDIA

IBM has cut the ribbon on a series of new mainframe offerings as the tech giant eyes data center efficiency gains.

The new IBM z17 and IBM LinuxONE 5 series mark the first time the company is offering rackmount options alongside single frame systems.

According to Tom McPherson, general manager for IBM Z and LinuxONE, the move here aims to help customers consolidate data center footprints without sacrificing performance.

“The number of mission-critical workloads is rising at an incredible pace, forcing organizations to make tough decisions about performance, AI integration, and infrastructure footprint,” McPherson commented.

“With these new IBM Z and IBM LinuxONE systems, we’re making it easier to run workloads where they make the most sense, while opening the door for a wider range of organizations to benefit from these technologies for the first time.”

IBM eyes data center flexibility

The new IBM z17 and LinuxONE 5 configurations support up to 82 cores and 18TB of memory across two processor drawers, according to IBM. The company said this represents a 20% increase in core count alongside a 12% increase in memory capacity.

The IBM z17 single frame is a fully packaged solution in an IBM rack designed to operate as a “complete enclosed unit ready to deploy now”.

With the IBM z17 rackmount, customers will have the option to install IBM Z components within their own industry-standard racks.

The company noted that customers can also co-locate IBM equipment with third-party components to “achieve the best fit-for-purpose installation” based on their needs.

“Each system is designed to help organisations reclaim space, improve energy efficiency, and integrate seamlessly into existing environments,” the company said in a blog post.

LinuxONE upgrades target AI performance

Elsewhere, the new IBM LinuxONE series comes in two dedicated configurations: the Rockhopper 5 and LinuxONE 5 Express options.

The Rockhopper 5 series is a “scalable, multi-drawer” configuration designed specifically for high-density workloads, according to IBM.

This comes equipped with on-chip AI acceleration features, as well as confidential computing and post-quantum cryptography capabilities in both single frame and rackmount configurations.

Meanwhile, the LinuxONE 5 Express option offers the same capabilities, albeit in a compact configuration. IBM said these are designed for smaller workloads, with a particular focus placed on cost-efficiency and scalability.

The z17 single frame and rackmount configurations, IBM LinuxONE Rockhopper 5, and IBM LinuxONE 5 Express series will be generally available for customers from 12 August.

FOLLOW US ON SOCIAL MEDIA

IBM and Red Hat have expanded their Project Lightwell open source security initiative to speed up patch management, bringing Palo Alto Networks into the fold.

The idea is to combine vulnerability intelligence, software remediation ,and network-based protections to help organizations respond more quickly to newly discovered vulnerabilities.

Palo Alto Networks will deploy a virtual patch at the network layer to block exploit attempts, while Project Lightwell follows up with software remediation for open source software that customers can test and deploy in their environments.

"AI has compressed the window between vulnerability discovery and exploit from weeks to minutes. Traditional patching cannot keep pace," said Nikesh Arora, CEO and chairman of Palo Alto Networks.

"By collaborating with IBM and Red Hat, we are shifting the advantage back to defenders. This powerful combination allows us to neutralize threats in the network while providing uninterrupted business continuity for our global clients."

How Project Lightwell works

According to the trio, the deal will provide enterprises with broader vulnerability coverage, with protection across open source software, commercial applications, operational technology (OT) environments, and connected devices.

Organizations can receive virtual patch protections before official software patches become available, helping reduce exposure while remediation is underway. When a new vulnerability is discovered, network-level protections can be deployed the same day, reducing the time from validated discovery to protection.

The companies also said that in future they plan to establish secure processes for sharing vulnerability information across participating software vendors, technology providers, and security teams.

This aims to support coordinated vulnerability disclosure, accelerate protection development, and provide anonymized telemetry on real-world exploitation attempts.

"IBM established Project Lightwell to secure the open source software foundation that enterprises rely on every day. By collaborating with Palo Alto Networks, we are extending that security from the source code directly to the network front lines," said Arvind Krishna, chairman and CEO of IBM.

"This joint solution gives our clients exactly what they need to thrive in the AI era: immediate, automated resilience against emerging threats, combined with the rigorous validation required to safely update their core systems."

Software security in the spotlight

IBM and Red Hat launched Project Lightwell last month with a $5 billion investment and a team of more than 20,000 engineers.

Early adopters include Bank of America, BNY, Citi, Goldman Sachs, JP Morgan Chase, Mastercard, Morgan Stanley, Royal Bank of Canada, State Street, Visa, and Wells Fargo.

The idea was to use advanced AI capabilities, offered through commercial subscriptions, to validate and test fixes across a huge volume of open source code.

While more than nine-in-ten Fortune 500 companies rely on open source software, Black Duck research has indicated that 86% of codebases contain open source vulnerabilities.

Notably, 81% of those were classified as high or critical risk, up from 74% in the previous year.

FOLLOW US ON SOCIAL MEDIA

Most CIOs and CTOs are accountable for systems they don't fully control, as AI adoption outpaces current governance capabilities.

A new global study of 2,000 C-level technology executives by IBM has found that two-thirds are forced to take responsibility for AI systems that they can't realistically supervise, with only 11% saying they're completely prepared for the scale of AI agent deployment.

Respondents said that by 2027, they're expecting a 38% increase in the number of AI agents deployed; and while eight-in-ten report being given transformation mandates driven by the CEO, 77% said their AI adoption is already outpacing current governance capabilities.

"For CIOs and CTOs, the challenge now is scaling AI systems that operate continuously and autonomously, often within governance models and architectures designed for a far slower, more predictable environment," said Matt Lyteson, CIO at IBM.

"It is no longer just about deploying AI faster. It's redesigning how organizations control, govern, and invest in it and embedding control and visibility from the start, so they can scale with confidence."

The firm's analysis revealed that in organizations relying on manual governance, incident risk increases as AI adoption scales, whereas those that embed control directly into their AI systems experience 25% fewer incidents.

Nearly six-in-ten of the CIOs and CTOs surveyed cited security and compliance concerns as top barriers to scaling AI agents.

Organizations experienced an average of 54 AI agent incidents last year in which an unintended and/or harmful occurrence required human correction. And of those, 17% were high severity, requiring more than four hours to contain: 37% resulted in data exposure or security breaches, 33% caused cascading system failures, and 17% triggered compliance issues.

"The pressure is only intensifying. CEOs want AI scaled faster. Business leaders want autonomy. And expectations for accountability continue to rise," said Lyteson.

"Yet many organizations are still operating with architectures, controls, and funding models designed for human-speed decision making, in systems that now operate at machine speed."

While AI spend is projected to grow from just under 15% of IT budgets in 2025 to nearly 25% by 2027, 84% of tech CIOs and CTOs haven't fully operationalized AI financial management, and 85% still lack full visibility into real-time AI spend.

However, organizations that build control into their AI systems deploy 16 times as many AI agents as those relying on manual governance, deliver 18% higher operating margins, and spend a quarter of their AI budget.

Those with strong financial discipline deploy 2.4 times as many AI agents, without driving up their AI/IT budget, and are three times as likely to say they are fully prepared for AI scale.

Meanwhile, those that have designed for adaptability early – keeping workloads portable and models replaceable rather than locked into hard dependencies – reported a 10% higher return on AI investment in 2025.

"The key constraint is adaptability. Most organizations were built for control, standardization, and efficiency," said Lyteson.

"But in an AI-driven landscape, it is now architecture, governance, and investment decisions that establish how fast they can move, what risks they can absorb, and which opportunities remain viable."

IT leaders are carefully assessing the extent to which AI-generated code can make a difference in their business.

On the one hand, AI developers promise their tools can enable faster code deployment and free up time for developers. On the other, it can be difficult to know where to start with AI tools – particularly if you want total reliability in your code.

How can enterprises make best use of AI code? And what do these tools mean for the developer teams of the future?

In this episode, Jane and Rory are joined by Colin Jarvis, head of forward deployed engineering at OpenAI, to discuss internal code generation.

Highlights

"The folks who use [AI] to increase the output of their existing specialists and their existing like experienced staff, are the folks who are seeing the best output. And I think the folks who see it as an excuse to as, sort of a race to the bottom, are the people who who struggle a bit more."

"[Hiring junior developers] has been a big focus of ours [and]... the number of entry level people has increased in the [forward deployment engineering] team over the last three to six months and the reason for that is that we're finding with the right sort of enablement ... they improve very, very fas, and they end up being as productive as some of our more experienced staff very quickly."

Footnotes

• Enterprises keep cutting staff for AI – and they keep regretting it
• As a software developer, you naturally need to have a willingness to learn
• OpenAI's Codex app is now available on macOS
• OpenAI ramps up enterprise AI push with new consultancy launch

Subscribe 

• Subscribe to The IT Pro Podcast on Apple Podcasts
• Subscribe to The IT Pro Podcast on Spotify
• Subscribe to the IT Pro newsletter
• Join us on LinkedIn

IT spend can be incredibly hard to accurately calculate. If you’re reliant on public cloud providers for your AI and software needs, you’re open to price rises, license changes, and other baked in costs.

The past few years have seen some firms choose to repatriate workloads to offset cloud costs – but this comes with its own risks. Surging international oil and gas prices, driven by conflict, are driving up bills for enterprises and consumers alike – bad news for those running AI workloads on premise.

How can leaders begin to get a grip on these costs? And what are some of the major challenges down the road?

In this episode Rory is joined by Greg Holmes, EMEA Field CTO at Apptio (an IBM Company), to discuss managing technology spend in the face of rising global instability.

Highlights

"I think companies are struggling to really understand what the cost per query is, but they may have an understanding of the whole service. They know how big their bill with a certain AI provider is, but I've heard of companies who've blown through a whole year's budget of AI in a couple of months of the year..."

"You notice that there's cheaper periods of the day to use energy, and then there's very expensive periods of the day, and some suppliers even give free energy or pay you to use energy when it's at really high availability. So it can be a good case to say then if workloads don't need to run at a particular time, if there's some flexibility, then having a system that can help you schedule and plan workloads and the running of those workloads, regardless of if they're in public cloud providers or in your own data center, could actually make a big difference as well."

"[I]n the cloud, we call this kind of operation FinOps, right? So FinOps is looking at a very granular level, because a lot of your cloud billing data and a lot of your cloud consumption data can be seen on an hourly basis. We can actually break that down and provide alerting to rapid changes in your consumption, but we can also help organizations use that programmatically, so you can make it available through API back to the developers, so they could actually be looking at their costs as things are going and choose appropriately."

"Yeah, well, we're certainly seeing countries putting the pause on certain developments. So I was reading recently about Denmark halting additional gen AI data centers, partly because of the power consumption demands. So for instance, a country like Denmark has a peak demand at around seven gigawatts. They had a set of requests of about 60 gigawatts for that country, right? So it's ten times, or nine times the peak and AI data centers were taking up about 14 gigawatts of those requests."

Footnotes

• UK firms ponder offshoring AI workloads as energy costs surge
• The power challenges UK data centers face
• UK government to prioritize data center grid access, cut down on speculative applications
• Costly cloud storage fees are pushing IT budgets to breaking point
• What is FinOps, and how can it help to reduce cloud costs?
• Denmark faces data center reckoning as power grid overwhelmed by surging demand

Subscribe 

• Subscribe to The IT Pro Podcast on Apple Podcasts
• Subscribe to The IT Pro Podcast on Spotify
• Subscribe to the IT Pro newsletter
• Join us on LinkedIn

IBM is teaming up with Arm to develop new dual-architecture hardware aimed at streamlining AI and data-intensive workloads for enterprises.

The plan is to combine IBM’s system design capabilities with Arm's expertise in power‑efficient architecture, workload enablement, and broader software ecosystem to build flexible and scalable computing platforms for the future.

"As enterprises scale AI and modernize their infrastructure, the breadth of the Arm software ecosystem is enabling these workloads to run across a broader range of environments," said Mohamed Awad, executive vice president, Cloud AI Business Unit, Arm.

"Our collaboration with IBM builds on this progress, extending the Arm ecosystem into mission-critical enterprise environments and giving organizations greater flexibility in how they deploy and scale these workloads."

The duo said they are exploring how to expand virtualization technologies that allow Arm-based software environments to operate within IBM's enterprise computing platforms.

The plan here is to expand software compatibility and make it easier for developers and enterprises to bring Arm applications into mission-critical environments.

Meanwhile, they're also exploring new ways to support the performance and efficiency demands of modern workloads, including AI and data intensive applications. This will involve enabling enterprise systems to recognize and execute Arm applications.

IBM, Arm eye shared tech layers

Notably, the two firms are focusing on long term ecosystem growth by creating shared technology layers between platforms. IBM said the aim here is to open the door to broader software ecosystems and greater flexibility in how applications are deployed and managed.

IBM noted this could give enterprises more choice, allowing them to adopt new applications and architectures while continuing to leverage existing investments.

Patrick Moorhead, founder, CEO, and chief analyst at Moor Insights & Strategy, said the partnership highlights a “new phase” of enterprise infrastructure investment where flexibility and portability is as important as performance and reliability.

“As AI and data-intensive applications reshape requirements, organizations are looking for platforms that can evolve without forcing disruptive tradeoffs," Moorhead commented.

"What IBM and Arm are signaling here is a meaningful step toward that future that could broaden how enterprises think about deploying and scaling modern workloads. While the full implications will take time to unfold, it's clear this reflects a deeper level of investment in long-term platform innovation and ecosystem expansion than we typically see at this stage."

Arm shaking up the infrastructure market

The partnership with IBM comes hot on the heels of its AGI CPU launch in late March, the company’s first in-house chip.

As ITPro reported at the time, the launch of the chip marked a significant milestone for the company, which aims to cater to the growing industry focus on agentic AI.

CPUs are becoming increasingly important in supporting agentic AI deployment due to the sequential processing power needed for orchestrating workloads.

The company has already secured deals with major industry players for its AGI CPU, including Meta, which plans to use the chip in its gigawatt-scale data centers. OpenAI also revealed plans to use the chip in future projects.

FOLLOW US ON SOCIAL MEDIA

Anthropic believes Claude Code can play a key role in modernizing legacy code like COBOL, but IBM appears to disagree.

In a recent blog post, Anthropic said its flagship coding tool can automate much of the legwork required in COBOL modernization, which is a notoriously difficult process for enterprises.

"With AI, teams can modernize their COBOL codebase in quarters instead of years," the company said.

If as useful as promised, Claude could be the tool that finally breaks the ongoing dependence on COBOL, or Common Business-Oriented Language, a programming language first developed back in 1959.

While it's still widely used, that's largely for legacy operations, and efforts are being made to shift at least some of those workloads to more modern platforms. Anthropic noted COBOL runs 95% of ATM transactions in the US and is used to power critical systems including finance, airlines, and government.

"Despite that, the number of people who understand it shrinks every year," Anthropic noted in its blog post. "The developers who built these systems retired years ago, and the institutional knowledge they carried left with them."

Waning expertise in COBOL is exacerbated by the fact universities no longer teach the language. Indeed, it’s becoming increasingly difficult to find qualified engineers in this domain.

That could have serious implications for major industry players such as IBM, largely because COBOL is a major selling point for IBM systems as the company continues to sell mainframes to customers reliant on the language.

After Anthropic's announcement, shares in IBM fell 13.2% – the biggest drop seen by the company in more than 25 years, according to Reuters.

Claude Code vs COBOL

Anthropic said COBOL modernization is different from standard code, adding "you're reverse engineering business logic from systems built when Nixon was president."

With that in mind, it’s not just about coding, but understanding long lost institutional knowledge that's only represented in the code itself.

That's one reason why "armies of consultants" are now required to update these systems – at high cost and with lengthy timelines.

"Tools like Claude Code can automate the exploration and analysis phases that consume most of the effort in COBOL modernization," the post noted.

Using Claude Code, Anthropic said researchers were able to map dependencies across thousands of lines of code, document long-lost workflows, spot risks more quickly than human analysts, and offer "deep insights" to make key decisions.

"AI excels at streamlining the tasks that once made COBOL modernization cost-prohibitive," the company said. "With it, your team can focus on strategy, risk assessment, and business logic while AI automates the code analysis and implementation."

By going through a step-by-step process that includes validation, the AI can translate COBOL logic into modern languages, creating API wrappers around legacy components and building a system to run old and new code together.

IBM isn't convinced

In an apparent response, IBM published its own blog post about AI coding tools and COBOL – without specifically mentioning Anthropic – that argues there's a clear difference between translating code and modernizing a platform.

"Translation captures almost none of the actual complexity," said Rob Thomas, Senior Vice President, IBM Software and Chief Commercial Officer, later adding: "Decades of hardware-software integration cannot be replicated by moving code."

IBM's mainframe business isn't just about COBOL, Thomas said. Indeed, nearly half (40%) of COBOL doesn't even run on mainframes.

"COBOL on IBM Z is code optimized over decades of tight coupling between software and hardware," Thomas added.

"An analogy is the iOS and iPhone: someone could build an alternative, but it is unlikely to displace a billion iPhones. The performance derives from tight coupling of software and hardware, processor-level acceleration, I/O subsystem optimization, and decades of performance tuning."

Whether that convinces the markets to stop battering IBM's shares remains to be seen, but IBM isn't the first company to see its price fall over an Anthropic post or product launch in recent weeks.

Software and services shares dipped over the launch of Claude Cowork, while another reveal about security tools tanked share in a host of major cybersecurity companies.

FOLLOW US ON SOCIAL MEDIA

IBM has launched a new cloud platform designed to help organizations build, deploy, and manage AI-ready sovereign environments.

Unlike other sovereign solutions, IBM said its Sovereign Core makes sovereignty an inherent property of the software itself.

Rather than relying on policy overlays or provider-managed control planes, it uses always-on controls and user-owned control planes.

Similarly, ongoing compliance capabilities are embedded directly into the software, helping organizations to produce regulator-ready proof on demand and without manual, audit-driven processes.

This, the firm said, allows organizations to maintain direct operational authority over software operations, deployment decisions, and system configurations, with no need to involve out-of-region vendors.

All authentication, authorization, encryption keys, and access management remain within jurisdiction boundaries under customer control.

IBM Sovereign Core will be available in tech preview next month, with full general availability planned for the middle of this year.

What to expect with IBM Sovereign Core

According to IBM, Sovereign Core generates, stores and manages comprehensive operational data, system telemetry, and audit trails within the sovereign boundary, including automated identity.

Elsewhere, AI model deployment and hosting, local GPU clusters, local inference execution and agent operations also take place under local governance, without the need to export data to external providers.

"The sovereign AI conversation has focused on data residency, but that's only part of the equation," said Sanjeev Mohan, principal, SanjMo.

"IBM Sovereign Core addresses the harder question: who controls the system and can you prove it to regulators? IBM takes a holistic approach spanning data, operations, technology, and assurance, with continuous monitoring," the company said in a statement.

"As AI moves into production, that kind of ongoing accountability becomes non-negotiable."

Customers can choose to deploy IBM Sovereign Core in the environment of their choice – whether that's an on-premises data center, supported in-region cloud infrastructure, or through IT service providers.

Approved providers under the scheme include Computacenter in Germany, or Cegeka, which operates in Belgium and the Netherlands.

Sovereignty in the spotlight

The move from IBM is the latest in a mass market pivot toward data sovereignty, with a host of major cloud providers now offering these services.

This week, for example, AWS announced the general availability of its dedicated European Sovereign Cloud service, aimed at allowing European customers to keep data in-region.

Enterprise concerns about data sovereignty have been creeping in across the European Union (EU) in recent years, spurred on by new legislation.

Gartner predicts that by 2028, 65% of governments worldwide will introduce some technological sovereignty requirements to improve national infrastructural independence and protect from extraterritorial regulatory interference.

Additional research from the consultancy shows more than three-quarters of all enterprises will have a digital sovereignty strategy by 2030, often based on sovereign cloud.

Long-term, this will be a booming market, according to Gartner. By 2028, the sovereign cloud market is projected to grow by 4.5 times, reaching $169 billion globally.

"Businesses are facing growing pressure to innovate while meeting tightening regulatory requirements and recognizing the importance of controlling how sensitive data and AI workloads are accessed and operated," said Priya Srinivasan, general manager for IBM software products.

"This shift is creating an urgent need for sovereign solutions that deliver AI-ready environments."

FOLLOW US ON SOCIAL MEDIA

IBM has announced plans to acquire Confluent in an $11 billion deal analysts said will rapidly accelerate the cloud giant’s AI data streaming capabilities.

In a statement confirming the acquisition, IBM described Confluent as a “natural fit” for its hybrid cloud and AI strategy, enabling “end-to-end integration of applications, analytics, data systems and AI agents”.

Confluent is an open source data streaming platform, based on Kafka, providing real-time data flows for applications, analytics tools, and AI. The logic behind the acquisition, according to IBM, lies in surging demand for real-time, high quality data required to underpin AI models and agents.

Analysis from IDC, for example, predicts that by 2028, global data volumes will more than double, with upwards of one billion new applications set to emerge. With this comes the need for seamless data streaming capabilities, the company noted.

This has been a common recurring challenge for enterprises driving AI adoption over the last three years. Data sources are often contained in disparate silos or across various cloud platforms.

Confluent bridges these gaps, IBM said, helping customers to streamline data flows and more effectively harness data “living across all IT environments”.

“IBM and Confluent together will enable enterprises to deploy generative and agentic AI better and faster by providing trusted communication and data flow between environments, applications, and APIs,” said IBM CEO Arvind Krishna.

“Data is spread across public and private clouds, datacenters and countless technology providers. With the acquisition of Confluent, IBM will provide the smart data platform for enterprise IT, purpose-built for AI.”

Confluent acquisition “strategically significant”

Forrester VP principal analyst Noel Yuhanna described the Confluent deal as a “strategically significant acquisition” for IBM – and one that will supercharge the cloud giant’s AI strategy.

“Real-time data is becoming critical for all applications, analytics, and AI,” he said.

“IBM’s purchase of Confluent brings industry-leading, Kafka-based data streaming directly into its hybrid-cloud and AI strategy, giving enterprises the ‘data-in-motion’ backbone required for modern enterprises that need continuously updated data to support AI workloads.”

IBM previously supported streaming through its Event Streams and IBM MQ platforms, Yuhanna noted. But with Confluent the tech giant now has a “unified, enterprise-grade data platform” that boasts real-time capabilities spanning both hybrid and multi-cloud environments.

“The acquisition positions IBM to simplify customer architectures through a real-time data fabric, tighten the integration between batch and streaming data, and power advanced AI systems with fresher, cleaner, and continuously flowing data,” he said.

Long-term, Yuhanna expects the deal to give IBM a “meaningful competitive edge” as demand for data streaming continues to surge.

Analysis from Verified Market Research in October 2025 found the data streaming market is projected to surge in value between 2024 and 2031, recording a compound annual growth rate (CAGR) of 33.56%.

This market was valued at $30.12 billion in 2024, the study noted, and is expected to reach $252 billion by the end of the forecast period. Confluent has rode the wave of growth in this sector in recent years.

Between 2021 and 2025, the company’s total addressable market (TAM) has doubled, from $50 billion to around $100 billion.

Jay Kreps, CEO and co-founder at Confluent, said he welcomed the next stage of the company’s journey under the IBM umbrella.

“We are excited by the potential to join IBM and to accelerate our strategy with IBM’s go-to-market expertise, global scale and extensive portfolio,” he said. “I look forward to the future we will build together as Confluent becomes part of IBM.”

MORE FROM ITPRO

• IBM CEO bangs the drum for smaller AI models
• Growing AI workloads are causing hybrid cloud headaches
• IBM is targeting 'quantum advantage' in 12 months

IBM has issued patches for four major flaws in IBM AIX and VIOS that allow a remote, unprivileged attacker to achieve arbitrary command execution on an exposed IBM Network Installation Manager (NIM).

The four vulnerabilities, tracked as CVE‑2025‑36250, CVE‑2025‑36251, CVE‑2025‑36236, and CVE‑2025‑36096, affect IBM AIX 7.2 and 7.3 as well as IBM VIOS 3.1 and 4.1 environments, with three of the four receiving a critical CVSS score.

All four flaws allow an attacker to 'hijack' unattended operating system installations and updates to deploy malicious payloads onto AIX hosts, move laterally, and persist in broader environments, according to an advisory from Mondoo.

“These four vulnerabilities on IBM AIX present a very serious threat because they allow a remote attacker with no privileges to perform arbitrary commands on an IBM Network Installation Manager (NIM) that’s exposed to the internet (which NIM servers typically are)," said Patrick Münch, Mondoo CSO.

"This means that they could 'hijack' unattended operating system installations and updates to deploy malicious payloads onto AIX hosts, move laterally, and persist in the broader environment."

How the IBM flaws work

CVE-2025-36250 carries a 10.0 CVSS score and affects the NIM service by allowing remote arbitrary command execution through improper process controls.

Researchers warned that an attacker could run commands of their choosing on the target AIX or VIOS system, gain full system control, install malware, create backdoors, move laterally and potentially pivot from the compromised system into other parts of the network.

Similarly, CVE‑2025‑36251 allows remote arbitrary command execution through improper process controls, affecting the SSL/TLS implementation in the NIM service.

With a critical CVSS score of 9.6, it could be used by a remote attacker to execute commands on the system, potentially without authentication. This could lead to a compromise of system integrity, data loss, or service disruption.

CVE‑2025‑36236, meanwhile, is a path-traversal vulnerability in the NIM service, allowing a remote attacker to send a specially crafted URL request to traverse directories or write arbitrary files on the system.

Researchers noted this could allow an attacker to drop malicious payloads in system directories, overwrite or inject into configuration files, or place web shells to facilitate further exploitation.

This particular flaw carries a CVSS score of 8.2, ranking it as high severity.

Finally, CVE‑2025‑36096 is a vulnerability in credential storage with a CVSS score of 9 (Critical). NIM private keys in IBM AIX are stored insecurely, meaning these can be accessed by an attacker via man-in-the-middle (MitM) techniques.

An attacker intercepting these communications or otherwise gaining access to the private keys could impersonate the NIM server or services or decrypt communications, which could result in system takeover.

Worst case scenarios

Researchers point out that, in combination, the four vulnerabilities could allow attackers to gain full access, impersonate services, move laterally, and persist or compromise broader network environments.

Moreover, the use of the operating system is widespread in critical industries, meaning the impact of a successful attack could be devastating.

"What makes this even more concerning is that IBM AIX is widely used in enterprise IT environments in critical sectors such as finance, insurance, retail, and healthcare, where high availability and security are essential," said Münch.

"Patch cycles are often delayed on IBM AIX because uptime is so critical for these enterprises. We haven’t seen any reports of active exploitation yet, but due to the high risk of these vulnerabilities, we strongly advise organizations to patch immediately.”

MORE FROM ITPRO

• Patch management: Why firms ignore vulnerabilities at their own risk
• Threat actors are exploiting flaws quicker than ever – here's what business leaders should do
• Everything you need to know about patch and vulnerability management

IBM has announced plans to cut its workforce as the cloud computing giant targets a small reduction in global headcount.

A spokesperson for the company told ITPro the pending layoffs aim to reshuffle current workforce levels in the wake of a recent review.

"IBM’s workforce strategy is driven by having the right people with the right skills to do the work our clients need," the spokesperson said.

"We routinely review our workforce through this lens and at times rebalance accordingly. In the fourth quarter we are executing an action that will impact a low single-digit percentage of our global workforce."

In a separate statement, a spokesperson told CNBC that the layoffs will affect a limited number of employees worldwide.

“While this may impact some US-based roles, we anticipate that our US employment will remain flat year over year.”

Exact details on the number of works affected by the cuts are yet to be determined. However, the company’s 2024 annual report shows its global workforce stood at around 270,000 employees.

With this in mind, cuts between one and five percent would equate to anywhere from 2,700 to over 13,500 on the higher end of the scale.

IBM recorded a strong third quarter in 2025, with revenue increasing 9% compared to the same period last year.

Notably, CEO Arvind Krishna hailed the company’s AI growth as a significant milestone. Revenue in this business segment has now surpassed $9.5 billion compared to $7.5bn in the prior quarter.

“Clients globally continue to leverage our technology and domain expertise to drive productivity in their operations and deliver real business value with AI,” CEO Krishna said in a statement last month.

IBM layoffs hot on the heels of industry reshuffle

The layoffs, which are expected to impact staff in the current financial quarter, mark the latest in a series of workforce cuts at big tech companies in recent years.

Earlier this month, Amazon revealed plans to cut 14,000 employees globally in a bid to become a leaner, more agile company. A key factor behind these cuts lies in a period of ‘overhiring’ during the pandemic.

A host of tech giants have attributed layoffs over the last two years to this trend, with many now seeking to balance budgets and offload staff who are surplus to requirements.

While IBM hasn’t confirmed whether this is the motivation behind these layoffs, official figures show the company’s workforce has decreased steadily over the last five years.

Its 2020 annual report showed the cloud giant had more than 345,000 staff worldwide, with this dropping to just over 282.000 by the end of 2021.

MORE FROM ITPRO

• IBM CEO in damage control mode after AI job loss comments
• Arvind Krishna bangs the drum for smaller AI models
• IBM and SAP expand partnership to drive generative AI capabilities

IBM CEO Arvind Krishna believes smaller, domain-specific AI models represent a prime opportunity for enterprises to truly capitalize on the technology.

Speaking at IBM’s 2025 Think conference in Boston, Krishna reflected on the course of the generative AI boom so far, noting that the industry has matured significantly compared to last year - as have enterprises and their expectations of the technology.

“As I think about last year to this year, there’s one really big difference that I’m feeling, which is that AI has moved from experimentation to a focus on unlocking business value,” he told attendees.

“People are worrying about what is the use-case? How do I get my business to scale leveraging AI? And I think that's a real big difference, because that means, if I sort of think about hype cycles, that means the hype cycle’s kind of fading, and we are now thinking about adoption, we're thinking about ROI, we're thinking about business value.”

The initial focus on large, cumbersome AI models has waned, Krishna claimed, and is instead shifting towards more finely curated options aimed at tackling specific business challenges.

A key factor in this is businesses adopting AI solutions are dead set on supercharging productivity. While larger models have helped with this, they are often a one-size-fits-all approach.

In contrast, smaller models are now proving vital by enabling enterprises to harness internal data in a more efficient and strategic manner, Krishna noted.

“I think AI is the source of productivity for this era,” he said. “But not all AI is built the same and not all AI is built for the enterprise.

“Why do I make that claim? 99% of all enterprise data has been untouched by AI. So if you need to unlock the value from that 99% you need you need to take an approach to AI that is tailored for the enterprise.

“If you think about the massive general purpose models. Those are very useful, but they are not going to help you unlock the value from all of the data inside the enterprise.”

Smaller models have a key advantage in that they inherently allow enterprises to derive greater value from enterprise data on a case-by-case basis. Ultimately, this not only helps businesses drive productivity, but also allows them to balance costs and target specific areas where the technology has maximum impact.

“To win, you are going to need to build special purpose models, much smaller, that are tailored for a particular use-case that can ingest the enterprise data and then work,” he explained.

“So you'll think about it, and then you’ll say, ‘well what about accuracy?’ - go look at leaderboards. Smaller models are now more accurate than larger models. What about intellectual property? That is where the open nature of some of the smaller models comes in.”

When assessing specific domains in which to deploy AI solutions, Krishna added that it’s “a lot easier to build a smaller model that can go after that”.

“So as we begin to go forward in that, you can think about these models that are three, eight, 13, 20 billion parameters, as opposed to 300 or 500 and then you begin to think about, well what advantage do I get? They're incredibly accurate. They are much, much faster,” he said.

“They're much more cost effective to run, and you can choose to run them where you want.”

IBM can find its niche with small AI models

It’s in this approach that Krishna believes IBM has a major advantage. The company’s watsonx platform, for example, allows enterprises to build applications designed to tackle specific challenges and areas of the business.

Similarly, Krishna pointed to IBM’s Granite model range, which he said is “a lot cheaper than some of the other alternatives”. These products have become core focus areas for IBM, and it intends to continue investing heavily in both moving forward.

As the industry matures and progresses, Krishna believes this focus on smaller model ranges will have a profound impact, opening up opportunities for enterprises in a trickle-down-style effect.

“As technology comes down the cost curve, it opens up many, many more opportunities,” he told attendees.

“It opens up a huge amount of aperture in terms of the problems you can afford to solve. That’s kind of been the curve it is on. We sometimes capture it in the phrase of, well, that's how we democratize technology, or rather, make it accessible to all, because the cost has come down.

“That is what we are very, very focused on. There is no law of computer science that says that AI must remain expensive and must remain large.”

Small language models are all the rage

Krishna isn’t the first to suggest that small language models will make all the difference for enterprises in the coming year. As organizations weigh up the benefits of the largest models over the lowest cost models that meet their needs and speed up the returns on their AI investments, developers are pivoting to providing lighter models that are performant at the edge.

The likes of Google’s Gemma 3 and Meta’s Llama 4 Scout can be run on a single Nvidia H100 GPU, while in the public cloud OpenAI’s 4o nano is its most cost competitive model for text-only tasks.

By leaning into this burgeoning field with its lightweight Granite model family, IBM could finally carve out a meaningful place for itself in the AI market.

Ensuring that each of its SLMs has a clear, competitive, domain-specific use case will be the key here. While there’s little chance of taking on the AI leaders at their own game – namely general purpose models – there’s clear demand for models that simply do one kind of task very well.

Mixture of experts (MoE) models, an architecture in which an AI model is made up of several smaller sub-models which are ‘experts’ at specific tasks and are only activated when needed, complicates the picture when it comes to how ‘small’ SLMs actually are.

The MoE approach allows models which, on paper, have many billions more parameters than the smallest models available on the market to run at a similar latency and cost per token. Take Llama 4 Scout, for example. Meta’s smallest frontier model has a total of 109 billion parameters, but only 17 billion active parameters at any given time, giving it a more lightweight computational footprint.

As these grow in popularity, developers will have to demonstrate concrete benefits to sticking with their approach or adapt to deliver a hybrid model offering. The jury’s still out on what the best approach or lowest latency is for AI output so there’s all to play for here.

For example, OpenAI boasts that GPT-4o nano takes just five seconds to generate its first output tokens when given an input of up to 128,000 tokens. If IBM could demonstrate a faster response time, or even anything like it available through its open source licensing, enterprises would be able to rely more heavily on AI at the edge.

The more domain-specific IBM gets, the more potential for wins in very niche markets.

MORE FROM ITPRO

• IBM eyes Oracle expertise gains with latest acquisition
• IBM’s CEO just said the quiet part out loud on AI-related job losses
• IBM and SAP expand partnership to drive generative AI capabilities

IBM has revealed 15 contracts agreed with the US government have been shelved amid a cost-cutting drive implemented by the Trump administration.

The tech giant said the dropped contracts were worth about $100 million in total, less than 1% of its consulting group's planned work, according to Reuters.

IBM CEO Arvind Krishna said in a call with investors: "Consulting is also more susceptible to discretionary pullbacks and DOGE-related initiatives."

Since the beginning of Trump's second term, the administration has sought to slash spending, led by Elon Musk's Department of Government Efficiency (DOGE). Those cuts, alongside global tariffs, have hit the US tech industry particularly hard.

Despite the contract situation, IBM reported a slight rise in revenue for its quarterly results, up 1% to $15.5 billion — better than expected. But it raised eyebrows by issuing a forecast for the second quarter, something the company normally doesn't include with its results.

That forecast was positive — revenue between $16.40 billion and $16.75 billion, above analysts' expectations — and was intended to calm nerves amid the tumult caused by government cuts and trade disruption initiated by the administration.

"We've chosen now, in light of the very unprecedented dynamic of uncertainty going on in the market, to give a second-quarter revenue guidance range," CFO James Kavanaugh said.

"We felt incumbent upon ourselves to give as much transparency as possible to our investor group."

However, despite the positive results, IBM's shares slid 5%.

Big tech gets political

Krishna made sure to thank the US administration despite the market turmoil, saying the company appreciated the focus on economic growth and rational regulation.

"We believe this will result in long-term value creation and make it easier for technology to contribute to economic growth," he said in an investor call after the results were announced.

Kavanaugh noted that goods imported to the US make up less than 5% of IBM's overall spend, so the tariff policy will have a minimal impact on the company, though it is seeking alternative sources where possible.

Forrester senior analyst Dario Maisto said that IBM's technology and services was diversified, so the impact of US tariffs, and retaliation against them, would differ across its portfolio:

"Guidance for the second half of the year will take tariffs and other countermeasures into account,” he said. “In general, the big tech play is becoming increasingly political.”

"For example, the fact that entire industries and countries depend on US-owned cloud infrastructures gives ample room for negotiation to both the US administration and its overseas counterparts,” Maisto added.

“Big Tech is not sitting idle either and we will see more measures put in place to reassure non-US clients on business continuity and resilience despite any geopolitical tension moving forward."

IBM results

For the first quarter, IBM posted $14.5bn in revenue, $1.7bn in operating pre-tax income, and generated $2bn in free cash flow, which Kavanaugh said was IBM's highest in the first quarter for many years.

"While sentiment and the operating environment have been rapidly shifting, our performance reflects the continued success of our focus strategy around hybrid cloud and AI, especially where clients are looking for cost savings, productivity gains, and trusted partners to help them move fast and scale," Krishna said in the investor call.

He added that IBM's book of business — which includes sales and future bookings — now holds $6 billion in pending work, up $1bn this quarter.

"Approximately one-fifth of this book of business comes from software and the remaining four-fifths is consulting," he added.

MORE FROM ITPRO

• The impact of tariffs on tech
• Nvidia braces for a $5.5 billion hit as tariffs reach the semiconductor industry
• IDC warns US tariffs will impact tech sector spending

IBM has released the latest version of its mainframe, the z17, aimed at supporting AI workloads and inferencing.

The company said its research shows 88% of global IT executives believe that application modernization is crucial, and that more than three-quarters reckon that mainframes will continue to be central to digital transformation.

According to IBM, the z17 has been designed with the direct input of more than 100 clients and in close collaboration with the IBM Research and Software teams.

The new system allows enterprises to score 100% of their transactions in real-time, and is targeted at more than 250 new AI use-cases, such as mitigating loan risk, managing chatbot services, supporting medical image analysis or tackling retail crime.

"The industry is quickly learning that AI will only be as valuable as the infrastructure it runs on," said Ross Mauri, general manager of IBM Z and LinuxONE, IBM.

"With z17, we're bringing AI to the core of the enterprise with the software, processing power, and storage to make AI operational quickly. Additionally, organizations can put their vast, untapped stores of enterprise data to work with AI in a secured, cost-effective way."

Under the hood of the z17 mainframe

Powered by the new IBM Telum II processor, the z17 can process 50% more AI inference operations per day than its predecessor, the z16, IBM said.

It has multi-model AI capabilities, new security features to bolster data security, and tools designed to leverage AI for improving system usability and management.

In particular, its AI inferencing capabilities are powered by a second-generation on-chip AI accelerator built into the Telum II processor. This, IBM noted, provides better frequency and compute capacity and a 40% boost to cache, enabling more than 450 billion inferencing operations in a day and one millisecond response times.

The IBM Spyre Accelerator - expected to be available via PCIe card by the end of the year - will bring additional AI compute capabilities aimed at supporting multi-model methods of AI.

This has been specially engineered to bring generative AI capabilities to the mainframe, including running AI assistants by leveraging enterprise data contained in the system.

AI assistants and AI agents are included, such as IBM watsonx Code Assistant for Z and IBM watsonx Assistant for Z. Watsonx Assistant for Z will also, for the first time, be integrated with Z Operations Unite to provide AI chat-based incident detection and resolution using live systems data.

The company also previewed z/OS 3.2, the next version of its operating system for IBM Z, which is expected to be released by the end of the year.

It's designed to support hardware-accelerated AI capabilities across the system and operational AI insights for system management capabilities, along with support for modern data access methods, NoSQL databases and hybrid cloud data processing.

MORE FROM ITPRO

• The end of the mainframe in financial services?
• IBM and SAP expand partnership to drive generative AI capabilities
• Cloud computing or mainframe? Why the pendulum might be swinging back in the age of generative AI

IBM has completed its acquisition of HashiCorp and unveiled plans to integrate the company's automation and security technology into its cloud offerings.

The $6.4 billion deal - which is being finalized a couple of months later than expected - was held up by the need to gain approval from the Competition and Markets Authority (CMA) and the US Federal Trade Commission (FTC).

HashiCorp is best-known for its Terraform platform, which provides lifecycle management for infrastructure and security through a single workflow, creating a system of record for hybrid and multi-cloud environments.

Customers can automatically provision infrastructure, network, and virtual components across multiple cloud providers and on-premises environments.

Following the acquisition, the plan is to integrate HashiCorp’s automation technology into IBM's Red Hat, watsonx, data security, IT automation, and consulting businesses.

"Organizations globally are looking to deploy modern, hybrid cloud-ready apps, which require automated cloud infrastructure at significant scale," said Rob Thomas, senior vice president, IBM Software, and chief commercial officer.

"With this acquisition, IBM is committed to continuing to invest in and grow the HashiCorp capabilities, and together, with HashiCorp's leading technology and extensive developer community, IBM's global reach and R&D resources, our aim is to infuse HashiCorp technology in every data center."

HashiCorp serves more than 4,400 clients, including Bloomberg, Comcast, Deutsche Bank, GitHub, JP Morgan Chase, Starbucks, and Vodafone, along with 85% of Fortune 500 companies.

"HashiCorp has been helping the world's largest enterprises succeed in hybrid and multi-cloud environments for over a decade. We've seen the cloud grow from its origins, and have participated first-hand in the change it has brought to the industry," said Dave McJannet, CEO of HashiCorp.

"With IBM's history, global scale, and customer relationships, HashiCorp can now expand our reach to help our community of customers, practitioners, and partners automate, secure, and optimize their cloud infrastructure, providing an end-to-end platform for hybrid environments."

IBM touts HashiCorp platform synergy

There's a powerful synergy between Terraform and the Red Hat Ansible Automation Platform, according to IBM, with Terraform automating foundational infrastructure creation across multiple cloud providers, and Ansible automating application configurations and middleware deployments on top.

Meanwhile, there are plans for HashiCorp's Vault, a tool that provides an encrypted environment for storing secrets.

Combined with Red Hat OpenShift, Vault will provide secrets management and security capabilities across hybrid cloud environments, with Terraform enabling the building and deployment of IBM Z applications in hybrid cloud environments.

"Integrating Terraform for provisioning with Ansible for configuration management will enable an end-to-end approach to infrastructure automation as code, while integrating Terraform with Cloudability will bring native FinOps capabilities to manage and optimize cost at scale," said Armon Dadgar, CTO and Co-founder of HashiCorp.

"Vault integration with OpenShift, Ansible, and Guardium will bring world-class secrets management to those platforms and reduce the integration burden on end users."

The deal is just the latest in a series of acquisitions by IBM aimed at bolstering its hybrid cloud offerings, including Red Hat in 2019 and Apptio in 2023.

MORE FROM ITPRO

• Interested in IBM's channel partner program? Here's everything you need to know
• IBM looks set to freeze hiring for roles ripe for automation
• How the Watsonx.governance platform promises to build trust in generative AI

IBM has announced plans to acquire global Oracle consultancy Applications Software Technology in an effort to bolster its Oracle expertise.

Applications Software Technology specializes in driving business transformation with Oracle Cloud Applications, particularly for clients in the public sector such as local government and education.

IBM said the deal will enhance its Oracle solutions for clients across North America, the UK, and Ireland, helping them deal with issues such as transitioning legacy systems, the scarcity of skills, or security and compliance requirements.

The agreement builds on IBM's acquisition of Accelalpha, a global provider of Oracle Cloud Applications consulting across advisory, implementation, and managed services, in September last year.

"Public sector clients’ cloud transformations often require a consulting partner with industry, domain and technology expertise," said Kelly Chambliss, senior vice president, IBM Consulting, Americas.

"The acquisition of Applications Software Technology will boost IBM’s public sector and Oracle Cloud Application skills to help clients confidently navigate their business transformations."

Applications Software Technology has been an Oracle partner since 1996, providing Oracle solutions in areas such as business process redesign and Oracle Cloud deployment.

Headquartered in Lisle, Illinois, the company has teams across the US, UK, Canada, and India. Its past acquisitions include Symatrix, 9EDGE, and Computer Technology Resources.

It's currently a portfolio company of funds managed by Recognize Partners; after the IBM deal is closed it will join IBM Consulting.

Along with its public sector presence, Applications Software Technology also serves commercial clients in the manufacturing, energy and consumer packaged goods industries.

It partners with Salesforce and MuleSoft, with its consultants working across the Oracle Cloud Applications Suite, particularly around Oracle Fusion Cloud Enterprise Resource Management (ERP).

"We are excited to join IBM and open up new opportunities for our people and expand transformative solutions to deliver business outcomes for our customers," said CEO Justin Winter.

"Applications Software Technology and IBM have complementary capabilities, client relationships, service offerings and values around our people, innovation and commitment to client success. Together, we will continue to grow in the market for cloud transformation solutions."

The deal, for undisclosed terms, is expected to close in the first quarter of 2025, pending customary closing conditions and regulatory approvals.

Almost half of companies have seen a return on investment (ROI) on their AI strategy, according to new research, and those using open source tools are more likely to see a positive result.

In a recent survey from IBM and Morning Consult, the vast majority (89%) said their organization plans to increase or maintain their AI investments in 2025, a vote of confidence in the technology despite growing complaints about the costs and utility of AI.

Indeed, the report found that only 5% of respondents intended to reduce spending on AI, and none planned to make cuts of more than 50%.

Instead, some companies plan to refocus their AI efforts, choosing to focus on IT operations, data quality management, and then product or services innovation, the report found.

Half of respondents said their strategic changes included shifting to managed cloud, hiring AI-skilled specialist talent, and ramping up the use of open source tools.

"Companies now recognize the value of defining specific use cases and optimizing AI projects," said Maribel Lopez of Lopez Research, which developed the study.

"They are leveraging hybrid cloud strategies and open source to drive AI innovation and deliver financial returns.”

The appeal of open source AI is growing

The study found nearly half (47%) of respondents reported ROI on their AI investments to date, which was more likely for those using open source tools.

Of those surveyed, 51% of open source users reported positive ROI versus 41% using proprietary systems.

No wonder then that two-in-five respondents not yet embracing open source plan to turn to it for AI projects this year.

But even those already making use of open source ecosystems plan to expand their use of AI, with 38% saying they plan to launch 21 or more pilots this year versus 26% for companies using proprietary AI tooling, the report said.

Open source is already heavily used by organizations when it comes to AI, the research suggests. Six-in-ten respondents said they turn to open source ecosystems as an AI tool source, for example.

The study also predicted a slight rise in the use of open source AI solutions in the coming year.

More than 80% of those surveyed said at least a quarter of AI solutions or platforms are based on open source systems, and that's particularly true for the largest companies, with half of their AI solutions likely to be open source.

Not all about ROI

The report suggests that ROI isn't the only metric for progress with regard to AI adoption and deployment.

Companies are also considering how AI speeds up software development and innovation, with those metrics ranked as most important by 25% and 23% of respondents, followed by productivity at 22%. Direct financial benefits were ranked fourth, at 15%.

"As organizations begin to implement AI at scale, many are placing greater stock in success metrics such as productivity gains, in part because traditional hard dollar ROI benefits have yet to show up on the balance sheets,” said Lopez.

"Yet, companies continue to rapidly advance their AI strategies, with no sign of slowing down."

That said, while a third of companies said their AI investments are driven by innovation, 28% said AI investment targeted boosted revenue — and 41% said they desired both.

AI spending outside traditional IT operations is increasing at retail organizations, according to new research, with executives saying they want to expand into more sophisticated AI use cases.

A report from the IBM Institute for Business Value found that 81% of surveyed executives and 96% of their team are already using AI to a moderate or significant extent.

However, executives indicated that they want to expand their use of AI into areas such as integrated business planning, where they plan to increase usage by 82% this year.

"AI is no longer just a tool; it's a strategic imperative," said Dee Waddell, global industry leader, consumer, travel and transportation industries at IBM.

"Retail and consumer product companies are at a tipping point where embedding AI across their operations can help define not just productivity gains, but the future of brand relevance, engagement and trust."

The use of AI in customer service, particularly for personalized responses and follow-ups, looks set to rocket, growing by 236% in the next 12 months. More than half of these improvements are expected to involve human-AI collaboration, while only three-in-ten would be fully automated.

As a result, executives also said they expect that around a third of employees will need to learn new skills to work with AI in the next year, increasing to 45% within three years.

Investment in ecosystem platforms — tools that facilitate exchange of data and AI models — could also skyrocket, the survey found.

Respondents indicated they expect growth to rise from 52% today to 89% within three years as their companies look to blend AI capabilities with business and technology partners to accelerate innovation and drive efficiencies.

Meanwhile, despite nearly nine-in-ten executives claiming to have clear AI governance frameworks, fewer than a quarter said they have fully implemented tools to manage risks like bias, transparency, and security, and continuously review their use.

IBM reckons that brands should evolve from viewing AI as merely a productivity booster to positioning it as a core driver of enterprise innovation - but said that this will require rethinking traditional governance and reskilling strategies.

"Retailers should tailor AI initiatives to align with their brand priorities and collaborate with strategic partners, including start-ups and technology companies. Equally important is breaking down silos between finance, technology, and business leaders," said the researchers.

"By fostering cross-functional collaboration, these stakeholders can jointly build strong business cases that demonstrate how AI can help deliver a long-term competitive advantage."

Late last year, research from McKinsey also identified the retail sector as one where the use of AI was driving innovation. Predictive AI systems, for example, are being used to forecast demand and optimize stock levels to cut wastage.

Similarly, AI is also being used to analyze customer behavior data and suggest products tailored to the individual’s preferences.

The UK’s Competition and Markets Authority (CMA) has launched a preliminary investigation into IBM's acquisition of HashiCorp.

The regulator said it's concerned that the $6.4 billion deal would lead to a substantial lessening of competition. It has put out a call for comment, set to close on 16 January, and revealed it will decide whether to refer the merger for a phase 2 investigation by 25 February.

The merger proposal was first agreed last April and was approved by HashiCorp stockholders in July. The plan is for HashiCorp to operate as a division inside IBM Software.

"By uniting two leading technology businesses with complementary visions, this transaction provides an incredible opportunity to accelerate HashiCorp's mission," said CEO Dave McJanne at the time.

San Francisco-based HashiCorp was founded in 2012 by Mitchell Hashimoto and Armon Dadgar.

The firm provides tools to help manage cloud infrastructure and security, offering infrastructure lifecycle management and security lifecycle management capabilities to help enterprises automate hybrid and multi-cloud environments.

HashiCorp serves more than 4,400 clients, including Bloomberg, Comcast, Deutsche Bank, GitHub, JP Morgan Chase, Starbucks, and Vodafone, along with 85% of Fortune 500 companies.

The announcement marks the latest in a slew of preliminary investigations launched by the CMA in recent years. The regulator has focused heavily on the UK’s cloud services markets, and raised concerns about a number of high-profile acquisitions and deals across 2024.

In particular, the CMA has launched an investigation into the state of competition in the UK cloud computing sector. The probe was launched in the wake of a report from Ofcom which raised concerns over customer flexibility and choice.

"Our market study has identified features that make it more difficult for UK businesses to switch and use multiple cloud suppliers. We are particularly concerned about the position of the market leaders Amazon and Microsoft," Ofcom said in a statement.

The CMA said it would look for technical barriers that could hinder customers’ ability to set up a multi-cloud architecture and switch cloud providers, along with the egress fees and discounts being offered by providers, as well as software licensing practices.

In September, it announced that it was planning to extend the inquiry by four months, reportedly to continue its investigation into Microsoft.

Founded in 1911 by financier and businessman Charles Ranlett Flint, International Business Machines (IBM) - the Computing Tabulating Recording (CTR) company until 1924 - remains an elder statesman of IT, reporting $61.9 billion revenues for full-year 2023 and maintaining a strong innovation record from AI and quantum computing to industry-specific cloud solutions and patents.

Chair and chief executive Arvind Krishna steers roughly 280,000 employees from its Armonk, New York headquarters. IBM's current vision is trained on AI and data platform Watsonx and hybrid-cloud-focused Red Hat OpenShift in particular - having acquired Red Hat in 2019. 

Key business use cases include code modernization, customer service, and digital labor. Cloud market share in Q3 2024 near level-pegged with Oracle, matching Salesforce, Tencent, and Huawei with 2% of the market.

Although Big Blue won't reveal its partner numbers, estimates have suggested around 55,000 partners, including 12,000 in the US, ranging from NTT Data Business Solutions, Wipro, and Tata Consultancy Services (TCS) to MySQL, AgilOne, Cohesive, Insight, Shi International and more.

Key offerings

IBM's latest Watson-lineage platform Watsonx is billed as a productivity, code quality, and customer experience (CX) facilitator, incorporating core AI, data, and governance. 

Its Granite AI large language models aim at high-performance enterprise tasks from cybersecurity to retrieval augmented generation (RAG).  

Meanwhile Red Hat OpenShift, following IBM's acquisition of Red Hat in 2019, targets an estimated two-thirds of organizations struggling to manage complex hybrid and multi-cloud environments - especially as more businesses grapple with genAI in the cloud.

The on-demand enterprise platform IBM Cloud helps build out many solutions. For instance, Watsonx AI and IBM Cloud, complemented by ML, assist complex analyses of population, infrastructure, urbanization, elevation, land use, and satellite data for electricity-access forecasting out to 2030.

Along similar AI and hybrid cloud-focused lines, the "VM as a service" IBM Power Virtual Server was announced at IBM Think 2024 as a platform for workloads including AI and for extending on-premises environments into the cloud.

Recent news: 

• Just before Christmas 2024, IBM announced a tie-up with Lockheed Martin, integrating Granite into its AI Factory tools for aerospace and defense innovation.
• October 2024 saw IBM launch the third version of high-performing Granite LLM AI for enterprise data. 
• In January 2024, Krishna said in an earnings call that its genAI and Watsonx "book of business" had doubled from its Q3  "low hundreds of millions", with demand continuing to rise. 

IBM for partners

Channel chief is Kate Woolley, whose official title is General Manager, IBM Ecosystem. Reportedly, IBM hopes to generate 80% of revenues via its partner ecosystem in three to five years - up from 40% of revenues in 2023 and 15% two years before that. 

In the same IBM Think 2023 interview for Partner Plus day, IBM senior vice president of software and chief commercial officer Rob Thomas suggested it seeks new customers spending $25,000-$50,000, to grow to $250,000 over three or four years post-acquisition.

While Big Blue was initially slow to monetize AI, it's since been targeting the recruitment of hundreds of partners for Watsonx alone. In October, Woolley said it "takes a village" to bridge AI skills and cost gaps. "AI is projected to unlock $16 trillion in value by 2030, generating opportunity for technology providers of all shapes and sizes."

Why partners matter

According to Woolley at TechXChange, its partner-first strategy reflects its recognition that business transformation needs can't be met by vendors alone. "The power of AI technology is only as great as its ability to get into the hands of developers, open-source communities, and business users," she said.

For instance, IBM plans to continue building agent capabilities across its portfolio in 2025, including pre-built agents for specific domains and use cases. 

Introducing the new Partner Plus program Services Track in January 2024, Woolley wrote about the ongoing importance of IBM’s partner-first focus. 

"Consultancies, advisory firms, systems integrators (SIs) and managed services providers (MSPs) are a vital part of the IBM Ecosystem," she said.

Partner programs, tiers, and partner types

IBM Partner Plus replaced PartnerWorld as the linchpin partner program in 2023. With Silver, Gold, and Platinum tiers and a new-partner accelerator program, it has refreshed incentives, co-marketing, and demand generation approaches. 

For instance, meeting Silver criteria, accepting IBM Partner Plus Program Benefits terms, and the Know Your IBM Attachment means they can use the IBM Software Access Catalog, and allow customers to evaluate IBM Software for up to 90 days. 

A Blue designation exists separately for IBM strategic partners -- typically cloud service providers and infrastructure or software vendors such as Cisco or SAP working with both IBM Consulting and IBM Technology solutions. 

Partner Plus has Build, Sell, and Service tracks:

• IBM Build partners - independent software developers (ISVs) and MSPs - integrate and extend IBM technologies. AI and hybrid cloud, unsurprisingly, are foci.
• IBM Sell partners funnel into data and AI, hybrid cloud, automation, sustainability, and storage areas. 
• IBM Service partners - SIs or consultancies - can build practices and Centers of Excellence for IBM technologies.

Further benefits, rewards, and incentives

As usual, a wealth of resources exist, including a partner portal.

• Partner demand generation programs, including the demand engine with marketing kits, joint-marketing support, and access to incremental marketing funds.
• Auto deal share to eligible partners for opportunities of less than $250,000.
• Real-time visibility into earnings and deal eligibility.
• AI deal share engine for lead generation.
• Insider programs, IBM education and training.
• Competitive pricing, deal protection, and special-bid access. 
• Tutorials. For instance for registering cloud services with the IBM Cloud Partner Center.
• Value seller tool for preferred pricing.
• Global technical resources.
• Personalized support via the partner support desk.

IBM offers to answer questions via the partner login. An IBMid is needed to log in to the partner portal - go here to create an account.

How to get on board

You can sign up here to enquire about a partnership. Again, you'll likely need an IBMid to tell the vendor about your organization and how you'd like to partner. 

Once registration is approved, new partners should select an onboarding distributor or tech broker to further guide them through the process. After accepting the terms, IBM emails confirmation of the next steps. New partners are onboarded with the Partner Accelerator for six months, which offers direct assistance, onboarding training, and marketing and sales materials.  

IBM has launched the latest versions of its Granite AI models, claiming they match or surpass everything else currently available on the market.

The new models are open source, released under the Apache 2.0 license, and the Granite 3.0 8B and 2B language models in particular are being pitched as 'workhorse' models for tasks such as Retrieval Augmented Generation (RAG), classification, summarization, entity extraction, and tool use.

While many large language models (LLMs) are trained on publicly available data, IBM has gone for enterprise data, which it said can deliver better task-specific performance than rivals larger models at a much lower cost.

The models can be fine-tuned with enterprise data and seamlessly integrated across a range of business environments or workflows, with IBM providing an IP indemnity for all Granite models on watsonx.ai for enterprise clients.

Meanwhile, the Granite Guardian 3.0 models allow application developers to implement safety guardrails by checking user prompts and LLM responses for a variety of risks.

"The Granite Guardian 3.0 8B and 2B models provide the most comprehensive set of risk and harm detection capabilities available in the market today," IBM claimed.

"In addition to harm dimensions such as social bias, hate, toxicity, profanity, violence, jailbreaking and more, these models also provide a range of unique RAG-specific checks such as groundedness, context relevance, and answer relevance."

The tech giant said the Granite 3.0 3B-A800M and Granite 3.0 1B-A400M MoE models deliver high inference efficiency with a minimal trade-off in performance.

Trained on more than 10 trillion tokens of data, the company is pushing them for on-device applications, CPU servers, and situations requiring extremely low latency.

IBM doubles down on AI assistant commitments

As part of the announcement, IBM also promised new developments in AI assistants, saying it's paving the way for future AI agents that can self-direct, reflect, and perform complex tasks in dynamic business environments.

This sharpened focus will include the next generation of watsonx Code Assistant, powered by Granite code models, to offer general-purpose coding assistance across languages like C, C++, Go, Java, and Python, and with advanced application modernization capabilities for Enterprise Java Applications.

It's also planning to release new tools to help developers build, customize and deploy AI more efficiently via watsonx.ai, including agentic frameworks, integrations with existing environments and low-code automations for common use cases like RAG and agents.

Over the rest of the year, IBM said it will expand all model context windows to 128K tokens, improve multilingual support for 12 natural languages, and introduce multimodal image-in, text-out capabilities.

Data breach costs have surged over the last year, new research from IBM shows, with organizations globally experiencing the highest increase in remediation costs since the COVID pandemic.

The tech giant’s Cost of Downtime Report studied 604 organizations across 17 industries and 16 countries and regions that had been impacted by a data breach between March 2023 and February 2024.

The research, conducted by Ponemon Institute, included interviews with over 3,000 security professionals with firsthand knowledge of the incidents, covering a range of breaches from 2,100 to 113,000 compromised records, 

IBM found the average cost of a data breach jumped to $4.88 million from $4.45 million over the course of 2023, marking a 10% increase – the largest since the pandemic.

Speaking to ITPro, Sam Hector, global strategy lead at IBM Security, noted one of the primary drivers of this spike was an increase in the scale of business disruption a breach has on an organization.

“There has been a really significant increase in the business disruption that breaches have on an organization. So it’s not just the actual impact of the breach and recovering from it, but often it’s tangential things that happen that are attached to the breach,” he explained

“If you look at the MGM casinos and resorts hack that happened last year, for example, not only was that bad in terms of the actual impact of the attack, but also the associated business disruption was huge. People couldn’t get into their hotel rooms, restaurants were taking orders on paper.”

Another factor exacerbating data breach costs are widening skills shortages, with more than half of the organizations surveyed by IBM said they faced “severe” security staffing issues. 

IBM found security skills gaps increased by just over 26% since 2023, which corresponded to firms incurring increased recovery costs from a cyber intrusion, by an average of  $1.76 million.

Accordingly, one of the most cost-efficient ways businesses can drastically reduce the cost of a data breach is by investing in employee training, which IBM calculated reduced the average breach cost by $258,629.

Data breach costs are paired with longer remediation times 

Another aspect that is increasing the impact of data breaches on firms is the amount of time it takes for them to recover, meaning their business operations are back to normal in affected areas, they have met any compliance obligations, and customer confidence and employee trust have been restored.

IBM found only 12% of organizations said they fully recovered from their cyber incident, with the majority reporting they were still working towards total recovery.

Hector said even those that had fully recovered reported it took them a significant amount of time, with only 3% of firms able to fully recover in less than 50 days.

“I think the other thing we’re seeing is breaches are getting more difficult to recover from,” he told ITPro.“The average recovery took more than 100 days, quite significantly, and the majority of businesses we interviewed hadn’t recovered from that breach. They were still going through the process of trying to recover.”

Industrial sector hardest hit with data breach costs

One area Hector highlighted where data breaches are particularly costly was the industrial and manufacturing sector. The combination of legacy infrastructure and complex, interwoven supply chains makes recovery far more complicated.

“Those industries are being much more heavily targeted by attackers these days, and when you attack critical infrastructure obviously the knock-on effects to other businesses, to the economy, and to the wider ecosystem around those businesses is huge,” he explained.

“[Operational technology] OT means that inherently the environments of industrial organizations are more complex, and when you get additional complexity, it's harder to unwind things when it goes wrong. Not only do they have more complex systems, they’re also a higher vulnerability to disruption, and they’re being targeted more, which exacerbates the problem.”

Hector added that he was interested to find that the industrial sector was increasingly being targeted by sophisticated, potentially state-sponsored threat groups targeting intellectual property, citing a 27% year-on-year rise in IPtheft.

“For sophisticated attackers, the very highly funded, competent, advanced types of organizations that are malicious, were targeting intellectual property theft a lot more than we’ve ever seen in the past,” Hector noted.

He outlined why he thinks firms in the sector, in particular, are struggling to deal with this increased attention from cyber criminals, suggesting the shift to the hybrid cloud has not been easy for  industrial organizations with a larger number  of legacy systems.

“The reason for that is that industrial firms have sensitive intellectual property and a competitive advantage to protect in terms of what they produce and how they produce it. What they’re struggling with is that data is proliferating across a hybrid cloud.”

“It used to be very neatly contained. Even if it was a complex system it would normally be on premise. But these days, most firms are using multiple cloud providers, SaaS applications, and so keeping track of all that sensitive intellectual property becomes even more difficult.”

The last five years of IBM’s long-standing partnership with Wimbledon has seen AI take ‘Centre Court’ as the latest technology the tournament can leverage to drive fan engagement and enhance customer experiences.

IBM has been the official technology partner for the Championships for over 30 years since the initial partnership was signed in 1990, and this year tournament organizers, the All England Lawn Tennis Club (AELTC), launched its latest generative AI feature, Catch Me Up. 

Unveiled on 17 June 2024, Catch Me Up promises to offer a richer and more engaging fan experience throughout the tournament. The tool provides AI-generated content for player cards with recent stats and play styles, which are continually updated throughout the tournament. 

These player cards will be personalized according to user preferences using location data and their myWimbledon profile, and will feature both pre-match and post-match content. 

The pre-match content will include information on the player’s career and their recent form, as well as win probabilities, while the post-match content will feature key statistics from the contest and a highlights package to catch up on the match.

Catch Me Up is powered by Granite, IBM’s flagship LLM foundation model available through its Watsonx AI platform.

This foundation model is then trained on years of structured data collected during previous tournaments and unstructured statistics from external sources to provide accurate content about the player’s recent form or career history.

Chris Clements, digital products lead at the AELTC explained the organization centers fan experience when developing their AI implementation strategy, and Catch Me Up will help fans stay engaged throughout the tournament.

“We are committed to offering fans the highest quality Wimbledon experience, whether it's in person or digitally,” Clements stated.

“Generative AI allows us to scale our ability to provide different types of content for fans wherever they are in the world in a way that's personalized for them. This year's new Catch Me Up feature will make it easier for fans to follow the key storylines as they emerge throughout The Championships."

Catch Me Up integrates last year’s AI-generated commentary 

Each Spring, IBM and the AELTC coordinate on how the user experience at the tournament could be enhanced, looking back on what worked and what didn’t from last year’s event.

This year-round innovation workstream begins with identifying the fan personas that they are trying to cater to, with some fans preferring in-depth statistics, while others favor video content or ‘snackable’ content distributed through social media.

These personas are then fed into an ideation workshop each Spring where IBM and the AELTC discuss current trends in sport and technology as well as the two organizations’ priorities to begin designing the features themselves that can realize these goals.

Each Autumn the two come together once again to decide which of these features will be implemented from those ideas in the following year’s championships.

Last year, Wimbledon introduced a generative AI commentary feature that used AI-generated audio commentary and captions  for post-match video content. 

The feature drew criticism from pundits expressing concerns about it being used to replace human commentators, and has reportedly been scrapped for this year's tournament. However,according to Kevin Farrar, head of sports partnerships  at IBM UK, it has been integrated into the Catch Me Up tool.

Speaking to ITPro, Farrar said the feature would not impact its commentary teams and is aimed at improving the AELTC’s ability to provide expanded coverage at the Championships.

“What we wanted to do and what Wimbledon wanted to do was to broaden the aperture, to tell more stories about more matches, and pre-match as well as post-match. So that was the goal and that ended up being rendered as Catch Me Up.”

He added that tennis is in a transitional stage at the moment, with a flood of new talent taking up the reigns of previous legends like Nadal, Djokovic, and Willams, and these features will help introduce fans to the new stars as well as other parts of the game that previously have not enjoyed the same attention.

“It’s about telling more stories that aren’t being told at the moment potentially. You’ve got a lot of new players up-and-coming, a lot of the old guard coming to the end of their careers, how can we tell stories about those other players?” he said.

“These features are enabling that, they’re trying to get stories out there about some of the new players that aren’t being written about at the moment by the content team or journalists - it’s filling a gap.”

Catch Me Up’s debut experiences a few teething problems

Catch Me Up has exhibited some growing pains since it was rolled out at the Championships on 1 July, with some fans pointing out on X that it had made a series of errors in various player cards.

For example, former US Open Champion Emma Radacanu’s profile erroneously listed her as the current British number 1 and having racked up 11 wins this year, despite being ranked British number 3 and winning 14 matches.

All AI-generated content undergoes human checks, according to IBM, so it appears these were missed by Wimbledon’s content team, but as users on X noted, the errors were quickly updated in the Wimbledon app.

Similarly, despite the model being trained on Wimbledon’s house style, fans noted a series of US spellings in the copy generated by the Catch Me Up engine.

Farrar said this was to be expected at this early stage of implementing generative AI solutions, but stressed the importance of recognizing these shortcomings and dealing with them quickly.

“It is a process of continuous improvement. So the model is trained and then there are checks and balances in there to make sure that we can minimize those errors,” he explained.

“That’s the nature of AI. What’s important is that we have ways to recognize those and deal with them quickly, and that’s what we do as you saw.”

He added that as the tournament progresses, both the model and the human teams working on the Catch Me Up content will improve, and the frequency of mistakes should decrease.

“As we go through the tournament that will get refined and improved. The team’s learning as well as the AI model is. It’s a pilot and it's working together to try new things, and each year we try to push the boundaries and get that balance right between tradition and heritage and tech and innovation.”

Nearly two-thirds (61%) of CEOs are adopting generative AI more quickly than workers are comfortable with, a new IBM study suggests, with many pushing hard on maintaining a competitive edge. 

While CEOs acknowledge this discomfort, the study found a sizable amount (64%) said their organization must take advantage of technologies that are outpacing employee’s ability to adapt. 

By contrast, less than half (44%) of respondents reported that they had “assessed the impact” of generative AI on their workforce.

With 62% claiming a willingness to take more risk to maintain a competitive edge, the report highlights a clear disconnect between the broader workforce and C-suite decision makers.

CEOs are firing on all cylinders, IBM said, with 43% set to “increase the tempo” of organizational change and 62% accepting the need to rewrite their “business playbook” to succeed. Staff, by comparison, don’t have the same mindset.  

“Part of the issue is that many people think they’re training their replacement. Despite the fact that business leaders consistently say this technology will support human employees — not replace them — employees remain skeptical,” the report stated. 

There is a lack of direction from a C-suite level, meaning CEO enthusiasm is not translating into clear rationales or objectives for individual staff members.

While 81% of respondents said that “inspiring their team” with a common idea is better at producing results than having precise targets, 36% acknowledged that employees don’t “fully understand” the effect that strategic decisions have on them. 

Similarly, although three-quarters of CEOs said trustworthy AI is “impossible” without organizational governance, only 39% stated that they had such governance in place.

Nearly two-thirds (64%) noted, however, that success with AI will depend more on the adoptive process of people than the technology itself, highlighting that more work needs to be done on a practical level to respond to employee needs.

“If people understand how generative AI can make their jobs easier—and more rewarding—organizations could see a major uptick in adoption,” the report stated. 

What can CEOs do to solve the issue? 

According to the study, there are three key ways in which businesses can more effectively adopt their strategies to foster more enthusiasm and direction in their workforces. 

Eliminating friction in the employee experience is vital in the first instance, IBM said. CEOs must locate the paint points creating “employee pushback” on the adoption of AI tools to deliver improvements and create an aligned strategy.

Similarly, CEOs should focus on the tools that will make staff workloads easier and more rewarding, paying close attention to processes that are “slow, inefficient, or create unnecessary work for employees”.

There should also be a shift towards investment that will “inspire change,” IBM said, with the priority needing to be put on incentivizing adoption through rewards for those who alter their roles to use the technology. 

Training is important, particularly that which teaches employees how best to use generative AI “to their advantage”, and CEOs need to ensure that “governance, technology, and talent are prepared” to optimize the value of generative AI. 

Magnetic tape storage might be one of the oldest storage technologies, but it appears to have found a new lease of life thanks to the generative AI boom.

A record 152.9 exabytes of total (compressed) tape capacity was shipped in 2023, according to the report from the companies behind the Linear Tape-Open Technology (LTO) program, namely Hewlett Packard Enterprise (HPE), IBM, and Quantum.

That’s growth of just over 3% compared to 2022, which the report puts down to rapid growth in data generation and the increased infrastructure needs of hyperscaler cloud providers and enterprise customers.

It seems that tape is turning out to be useful for storing the huge amounts of unstructured data that result from the adoption of new technologies such as AI.

“As AI and related technologies cause increases in storage requirements and costs, LTO tape technology makes the management of vast amounts of data more cost-effective and sustainable while delivering comprehensive protection against data breaches, which continue to grow in number and sophistication,” the report said.

The vast majority of enterprise data is unstructured and often ignored; however, the rise of generative AI has made this sort of data potentially extremely valuable, either for training AI models or to mine through with AI to find insights.

Some tech investment firms have even suggested that desire to get hold of this unstructured data could lead to a corporate acquisition boom. Either way, finding a way to host these gigantic datasets has become more important for enterprise customers.

Why magnetic tape storage is still in vogue

Magnetic tape has been used to store data since the 1950s, although the LTO format itself only dates back to the 1990s. 

Even if it’s an old technology, it is widely used for good reason. Tape is considered to be low cost, highly portable, and can store very large amounts of data. It’s an extremely durable storage choice which can last for up to 30 years.

LTO also features error correction algorithms with an error rate of 1 in 10^19. This, the LTO companies said, would mean it would take a whole year, with 130 tape drives writing data continually, to encounter an error that could not be fixed by LTO’s error correction technology.

The current, ninth generation, technology can store up to 18 terabytes (TB) of data on a single cartridge (45TB compressed); the LTO roadmap plans to increase capacity up to 144TB by generation 12. Since the first LTO products arrived, over 5.6 million drives, 371 million cartridges and 517,958 billion GB of media capacity have been shipped, the companies said.

The companies argue that tape has an 86% lower total cost of ownership than disk and a 66% lower total cost of ownership with cloud storage over a ten-year basis. 

The major disadvantage of tape is, of course, that it’s much slower at transferring data which is why it is often used for offline backup situations where reliable storage is more important than speed.

“The probability of losing data through degradation, however, is far less than it being maliciously encrypted in a ransomware attack, or destroyed by fire, flood or acts of war in the data center,” the companies note.

Indeed, one of the other reasons for the ongoing popularity of tape is as a backup technology, something that has become essential as a result of the rise of ransomware. Many companies will use the 3-2-1, or even the 3-2-1-1 backup rule, which means having at least three copies data stored on two different types of storage mediums, one of which is off-site and (for addition peace of mind) one that is off-line: tape is usually the ‘1’ in either case.

“LTO tape will endure as a critical component of storage architectures across the enterprise, especially as tape technology itself continues to improve to handle even larger volumes of data and more intensive workloads,” said Tom Coughlin, storage analyst and president of Coughlin Associates, quoted in the LTO companies’ press release

“The latest annual tape capacity shipment media report signifies the continued importance of LTO tape technology in the digital storage hierarchy not only today, but also far into the future as storage needs evolve in complexity and cost,” he said.

IBM and SAP have announced a new collaboration that aims to equip clients with new generative AI capabilities and industry-specific cloud solutions.

Building on the companies’ existing ties, the new Value Generation partnership will see IBM’s consultancy business, IBM Consulting, work with SAP to deliver AI-based services through Rise with SAP ERP, a subscription-based solution for migrating on-premises enterprise resource planning (ERP) to the cloud.

In an announcement, IBM said the move reinforces the two organizations’ commitment to client needs and meeting market demand.

“IBM and SAP’s shared approach to generative AI, built on an open ecosystem, trust and purpose-built models, will help empower clients to optimize business outcomes,” commented John Granger, senior vice president at IBM Consulting.

“Our new Value Generation partnership initiative will enable clients to accelerate the path to innovation, competitive advantage and become a next generation enterprise through generative AI.”

Initially, IBM said it plans to extend AI capabilities across SAP’s cloud solutions and applications, including Rise with SAP, Grow with SAP, supply chain management solutions, human capital management offerings, and more.

The firm said it also plans to implement a proof-of-concept adoption program that leverages SAP Signavio and SAP’s Business AI solutions.

The pair will also build intelligent, data-driven industry use cases into end-to-end business processes, initially focused on industrial manufacturing, consumer packaged goods (CPG), retail, defense, automotive, and utilities industries.

IBM has already started development of more than 100 industry and product delivery-focused AI solutions, and plans to develop prescriptive value maps for business processes, all which will be available via IBM Innovation Studios and the SAP Experience Center.

The collaboration will also focus on creating next-generation reference architectures, with IBM to leverage SAP’s BTP, Signavio, and LeanIX offerings, while IBM’s AI platform watsonx is being made available through SAP’s Generative AI Hub.

Additionally, IBM and SAP said they plan to partner around their respective employee network groups and communities, such as their women’s networks, the SAP University Alliances program, and their respective Veterans to Work initiatives.

“It makes perfect sense to expand our partnership with IBM to help more customers accelerate their cloud journey leveraging RISE with SAP and realize the transformative benefits of generative AI for business in the cloud,” said Scott Russell, CRO and executive board member of SAP’s customer success division.

“This expanded partnership will help more of our joint customers reach new heights by innovating through the cloud, data and business AI to grow and transform their businesses.”

Nearly 70 leading US software companies have agreed to join a voluntary pledge drawn up by CISA urging developers to incorporate secure by design principles into their products.

CISA director Jen Easterly said she has seen real change in the software ecosystem since the initiative’s announcement in April 2023, and on 8 May CISA announced the first round of commitments from high-profile companies at the RSA Conference in San Francisco. 

Easterly said the goal of the project is to promulgate better built in security to counter both the ongoing “scourge of ransomware” as well as a concerning rise in state-sponsored threat campaigns focussed on disrupting critical national infrastructure.

“[Nation-state threat actors] are burrowing into our critical infrastructure not for espionage, not for data theft, not for intellectual property theft, but specifically to launch disruptive and destructive attacks in the event of a major conflict”.

Easterly said this threat was “different in kind” to anything she has observed over the course of her career, and it is why the US government is prioritizing cyber resilience and implementing secure by design principles across as many digital products as possible.

The pledge consists of seven goals each with core criteria that defines what the manufacturers are pledging to work towards, which include examples of how they can demonstrate measurable progress towards achieving these targets.

Similarly, the pledge sets out a number of means by which the signees can demonstrate quantifiable progress towards their goals, but will give the companies some discretion to decide how best they can go about demonstrating their progress.

The first round of commitments included major players signing up to the pledge including Microsoft, IBM, AWS, CrowdStrike, GitLab, Sophos, Lenovo, NetApp, and HP.

Easy wins to bolster cyber resilience across the board

First up is increasing the use of multi-factor authentication (MFA) across software products, which it describes as the best defense against popular password-based attacks such as credential stuffing.

Companies that sign on to the pledge will be asked to take efforts to reduce the use of default passwords in their products, suggesting they switch to random, instance unique passwords or requiring the user create a strong password as soon as they begin the product’s installation process.

Within one year of signing the pledge, companies will also be expected to demonstrate the actions they have taken to significantly reduce the prevalence of one or more vulnerability classes in their products.

By eliminating vulnerabilities by class, CISA argues companies can prevent these flaws at scale which could significantly improve the efficiency of their efforts to keep their products secure.

The pledge wants software manufacturers to take ownership of the security outcomes of their customers, even after the product has been shipped. 

As such, it targets increasing the installation of security patches by asking companies to make it easier to install the updates. This could be achieved through introducing automatic update mechanisms or by providing patch support, for example.

Timely vulnerability disclosure is another important aspect to ensuring companies stay secure. By next year, signees are expected to publish their own vulnerability disclosure policy (VDP) that provides a clear channel to report flaws.

Moreover, the pledge hopes to boost transparency further asking its signatories to commit to demonstrating material improvements in the accuracy of their vulnerability reporting by providing an accurate Common Weakness Enumeration (CWE) and Common Platform Enumeration (CPE) fields in every CVE record for their products.

Finally, CISA wants companies to get better at recognizing and reporting unauthorized access to their internal systems. It states assenting companies should be able demonstrate a measurable uptick in their ability to gather evidence of cyber security instructions affecting their products.

“The only way we can make ransomware and cyber attacks a shocking anomaly”

The EU received formal approval on its own approach to boosting security postures across the board, the Cyber Resilience Act, in March 2024, which is a legal framework that sets out cyber security requirements for both hardware and software products sold in the region.

The framework targets similar problems currently rife among digital products such as improving the security of the software supply chain, and better vulnerability reporting from manufacturers, but importantly this approach is legally binding and those who fall foul could be prosecuted.

The UK has also introduced legislation looking to eradicate common security issues with its Product Security and Telecommunications Infrastructure (PSTI) act which, like the Secure by Design pledge, wants to get rid of default passwords - but in this case it's for smart devices, unlike software products.

CISA’s Secure by Design pledge does not incorporate hardware, but added that companies that wish to demonstrate progress in those areas are welcome to do so. 

More importantly, the goals outlined in the pledge are not legally binding, and it is not clear if the signatories can pick and choose which targets they want to try to meet, as the agency will have little recourse if they were to take this approach.

Speaking to this concern, Easterly argued that the strength of the approach centers around transparency, where customers will be able to see which vendors are taking security seriously.

“It is a voluntary pledge but the great thing is we have a platform to be able to advance radical transparency and so consumers that have to make decisions about what technology they buy will see whether these technology manufacturers actually took those steps”, she explained.

“I think it is the only way we can make ransomware and cyber attacks a shocking anomaly, and that is to ensure that the technology is more secure.”

The fact the pledge already has 68 high-profile software manufacturers signed on suggests software developers are more than happy to signal to regulators they care about improving the baseline security of their products, and as such could prove to be a useful way of getting industry buy-in without having to resort to the threat of legislation.

IBM has open sourced a set of generative AI models for coding which it said offer the right combination of features to support enterprise developers.

The tech giant is releasing a series of models for code generation tasks, trained with code written in 116 programming languages.

IBM said there are plenty of use cases for tools built on these models, ranging from agents that could write code for developers, to tools that can explain why code isn’t working - and how to fix it.

“Many of the other quotidian but essential tasks that are part of a developer’s day — from generating unit tests, to writing documentation or running vulnerability tests — could be automated with these models,” the company said.

Tools for developers trained on software code have been one of the breakout successes of generative AI. These tools aim to improve the efficiency of developers by offering coding advice and even proposing actual snippets of code. Within a few years, three-quarters of developers will be using these assistants according to Gartner.

IBM already has its own suite of generative AI coding assistants – the watsonx Code Assistant (WCA) family which includes WCA for Ansible Lightspeed for IT automation, and WCA for IBM Z for application modernization. WCA for Z, for example, uses IBM’s 20-billion parameter Granite LLM to help developers turn COBOL applications into services for IBM mainframes.

IBM is now open-sourcing four variations of the IBM Granite code model.

These models range in size from three to 34 billion parameters and are optimized for enterprise software development workflows. That means tasks including code generation, fixing, and explanation, and could have use cases including application modernization or running on devices with limited memory.

IBM is confident about model performance

The company said these Granite code models consistently match state-of-the-art performance among open source LLMs currently available.

The models are available on Hugging Face, GitHub, watsonx.ai, and RHEL AI. The underlying base code models are the same as the one used to train WCA.

The generative AI market has been expanding rapidly, but it’s not always easy for business users to find the right way forward.

Many of the biggest LLMs have now grown to tens or even hundreds of billions of parameters in scale.

While these might be good if you are looking to build a chatbot that can cover a wide range of subjects, these models are computationally expensive to train and run, IBM said.

“For enterprises, massive models can become unwieldy for more specific tasks, full of irrelevant information and running up high inferencing costs.”.

On top of this, many enterprises have been reluctant to adopt LLMs for commercial purposes because the licensing of these models is often unclear, and the details of how these models were trained, or how the data was cleaned and filtered for things like hate, abuse, and profanity are often unknown.

Flexible capabilities

Ruchir Puri, chief scientist at IBM Research said that for many enterprise use cases, the 8B Granite code model variant released by IBM will be the right combination of weight, cost to run, and capability.

“With generative systems built on Granite models, developers can create new ways to translate legacy codebases like COBOL into more modern languages like Java. It’s one of the major uses for code models that IBM saw when first diving into the world of AI for code, and remains one of the most important,” the company said.

These Granite code models are released under the Apache 2.0 license.

During testing on benchmarks including HumanEvalPack, HumanEvalPlus, and RepoBench, IBM said it saw strong performances on code synthesis, fixing, explanation, editing, and translation, across most major programming languages, including Python, JavaScript, Java, Go, C++, and Rust.

“Our models can outperform some twice their size, such as with Code Llama, and while some other models may perform slightly better in some tasks like code generation, no one model could perform at a high level at generation, fixing, and explanation — apart from Granite,” it said.

IBM has also released a full technical paper detailing the code models. It said there are important gaps in the current field of LLMs for code, especially in the context of enterprise software development.

“First, while very large, generalist LLMs can achieve excellent coding performance, their size makes them expensive to deploy. Smaller code-focused models can achieve excellent code generation performance in a smaller and more flexible package, but performance in coding tasks beyond generation (e.g. fixing and explanation) can lag behind code generation performance,” the researchers wrote.

The researchers said they planned to release updates to these models to improve their performance, and in the near future plan to release long-context as well as Python- and Java-specialized model variants.

IBM has revealed plans to acquire HashiCorp in a deal valued at more than $6 billion, marking a significant expansion of its hybrid and multi-cloud solutions portfolio for enterprise customers. 

The confirmation from IBM follows intense speculation over a proposed deal for HashiCorp, first reported by the Wall Street Journal on Tuesday 23rd April. 

Under the terms of the agreement, IBM said it will acquire HashiCorp for $35 per share in cash, representing a total enterprise value of $6.4 billion. 

At its IPO in December 2021, HashiCorp was valued at over $14 billion and currently has a market cap of $5.8 billion.

HashiCorp chief executive Dave McJannet said IBM represents the "ideal home" for the company as it enters is next phase of growth. 

"Today is an exciting day for our dedicated teams across the world as well as the developer communities we serve," he said. 

"I'm proud of the work we've done as a standalone company, I am excited to be able to help our customers further, and I look forward to the future of HashiCorp as part of IBM."

HashiCorp acquisition makes sense for IBM

The HashiCorp deal marks the latest in a string of major acquisitions by IBM, having completed two deals already so far in 2024, and underlines the growing enterprise appetite for hybrid and multi-cloud solutions in the age of generative AI. 

It’s a topic that’s been bubbling to the surface over the last year amid questions over how enterprises can maximize the use of generative AI applications by leveraging hybrid cloud setups.

Speaking to ITPro in October last year, NetApp chief executive George Kurian suggested generative AI and hybrid cloud are converging technologies that would eventually amalgamate into a singular strategy - and there are several key reasons for this, including flexibility, cost considerations, and safety.

Hybrid cloud enables businesses exploring the use of generative AI tools to fine-tune strategies before taking the plunge to market, Kurian said. Similarly, the flexibility of hybrid cloud enables them to shift applications and workloads on and off the cloud based on their unique individual circumstances.

Experimentation in public cloud comes first, he noted, but the long-term goal for many enterprises would be to host generative AI workloads in a hybrid environment.

“I think the hybrid cloud is a classic AI business use case,” he told ITPro. “Customers will want to try proof of concepts on the public cloud where all the modern tools are available, and once they understand which makes sense, they can scale it in their own data center.”

With HashiCorp, IBM appears to have found a match made in heaven to deliver on this idea long-term. IBM chief executive Arvind Krishna specifically highlighted the “global excitement surrounding generative AI” as a factor in the deal, alongside evolving enterprise infrastructure needs.

"Enterprise clients are wrestling with an unprecedented expansion in infrastructure and applications across public and private clouds, as well as on-prem environments,” he said.

"HashiCorp has a proven track record of enabling clients to manage the complexity of today's infrastructure and application sprawl. Combining IBM's portfolio and expertise with HashiCorp's capabilities and talent will create a comprehensive hybrid cloud platform designed for the AI era," he added. 

The shift to hybrid and multi-cloud setups - which involves a combination of on-prem and public cloud services - has raised infrastructure challenges for enterprises, particularly in terms of complexity.

It's an issue that IBM specifically highlighted in its 2023 annual report, noting that nearly two-thirds of companies “report difficulty managing these complex environments”.

It also predicted these challenges will be exacerbated as firms ramp up the deployment of generative AI across multiple clouds.

In this context, the HashiCorp acquisition can be seen as IBM putting its money where its mouth is. While the company has strong foundations on the AI and hybrid cloud fronts, and bringing HashiCorp’s capabilities into the equation will likely create a closer sense of alignment between these two aspects of the business.

HashiCorp certainly appears to be a "strong strategic fit", as IBM puts it.

What you need to know about HashiCorp

HashiCorp is a cloud infrastructure company that has a suite of tools aimed at helping IT professionals manage and automate multi-cloud environments, such as infrastructure as code (IaC) provisioning, and workload scheduling and orchestration. 

It lists customers including Vodafone, Deutsche Bank, and Starbucks.

HashiCorp started with an open source model, which has evolved over time to include free, enterprise, and managed service versions. All HashiCorp projects are accessible through a source-available license which the company said allows broad copying, modification, and redistribution.

It offers community, enterprise, and cloud offerings with free and paid tiers across its portfolio of products which include HashiCorp Terraform, Vault, Boundary, Consul, Nomad, Packer, Vagrant, and Waypoint.

A group of tech sector heavyweights headed by Cisco has launched a consortium aimed at allaying fears over AI-related job losses.

The AI-Enabled ICT Workforce Consortium, which includes Accenture, Eightfold, Google, IBM, Indeed, Intel, Microsoft, and SAP, aims to upskill and re-skill workers whose jobs are most likely to be impacted by the rising adoption of the technology.

As part of the move, the consortium will work alongside advisors from the American Federation of Labor and Congress of Industrial Organizations, CHAIN5, Communications Workers of America, DIGITALEUROPE, the European Vocational Training Association, Khan Academy, and SMEUnited.

"AI is accelerating the pace of change for the global workforce, presenting a powerful opportunity for the private sector to help upskill and reskill workers for the future," said Francine Katsoudas, executive vice president and chief people, policy and purpose officer at Cisco.

"The mission of our newly unveiled AI-Enabled Workforce Consortium is to provide organizations with knowledge about the impact of AI on the workforce and equip workers with relevant skills. We look forward to engaging other stakeholders — including governments, NGOs, and the academic community — as we take this important first step toward ensuring that the AI revolution leaves no one behind."

The first project will be to produce a report with practical steps that business leaders can take to prepare their workers for AI-enabled environments, enabling workers to find and access relevant training programs and connecting businesses to skilled and trained staff.

The consortium will evaluate the impact of AI on, and provide training recommendations for 56 ICT job roles.

These jobs, according to Indeed Hiring Lab, include 80% of the top 45 ICT job titles garnering the highest volume of job postings over the last year in the US and five of the largest European countries by ICT workforce numbers - France, Germany, Italy, Spain, and the Netherlands.

The consortium members said they'll create skills development and training programs for more than 95 million people around the world over the next 10 years.

Cisco will train 25 million people with cyber security and digital skills by 2032, while IBM says it will train up 30 million people in digital skills, including two million in AI, by 2030.

Intel has promised AI skills training for 30 million people, Microsoft will train and certify 10 million people from underserved communities, and SAP will upskill two million people worldwide by 2025.

Meanwhile, Google recently announced €25 million in funding to support AI training and skills for people across Europe.

"Our collective responsibility as industry leaders is to develop trustworthy technologies and help provide workers — from all backgrounds and experience levels — access to opportunities to reskill and upskill as AI adoption changes ways of working and creates new jobs," said Gian Luigi Cattaneo, vice president, human resources at IBM EMEA.

There's growing concern about the impact of AI on existing jobs, with recent research from the UK's Institute for Public Policy Research (IPPR) finding that up to eight million people in the UK could be impacted.

Long-term, it found, this could result in AI carrying out 59% of tasks.

Similar research from Goldman Sachs in early 2023 suggested that up to 300 million jobs could be lost to automation, prompting widespread concerns over the long-term impact of AI on the global labor force.

"Our research shows that virtually every job posted on Indeed today, from truck driver to physician to software engineer, will face some level of exposure to GenAI-driven change," said Hannah Calhoon, head of AI innovation at Indeed.

"The companies who empower their employees to learn new skills and gain on-the-job experience with evolving AI tools will deepen their bench of experts, boost retention and expand their pool of qualified candidates."

Security experts have warned of a concerning rise in cloud security attacks over the last year as threat actors ramp up attempts on cloud-based systems using stolen credentials. 

Cloud computing adoption across business continues to rise, but firms are not doing enough to protect these systems against attackers who are often using legitimate - but stolen – account details, according to CrowdStrike.

“Cloud adoption is exploding as companies realize the potential for innovation and business agility that the cloud offers. Due to this growth, the cloud is rapidly becoming a major battleground for cyber attacks,” the firm said in its 2024 global threat report.

“Businesses need full cloud visibility, including into applications and APIs, to eliminate misconfigurations, vulnerabilities and other security threats,” it said.

According to CrowdStrike, cloud environment intrusions increased by 75% from 2022 to 2023, with an even bigger rise in incidents where the attackers were able to take advantage of cloud features for their own purposes.

Cyber crime group Scattered Spider was specifically highlighted as a notable threat group with regard to cloud attacks.

“Throughout 2023, Scattered Spider demonstrated progressive and sophisticated tradecraft within targeted cloud environments to maintain persistence, obtain credentials, move laterally and exfiltrate data,” CrowdStrike said.

The security company said attackers mostly relied on valid credentials to achieve their initial access. These are credentials acquired in any number of ways, perhaps by accidental leakage, brute-force attacks, phishing and social engineering, or via access brokers (who gain access to systems in order to sell on), or insecure self-service password-reset services.

The CrowdStrike research aligns closely with a recent study from IBM, which also found the abuse of valid accounts is on the increase.

“As defenders increase their detection and prevention capabilities, attackers are finding that obtaining valid credentials is an easier route to achieving their goals”, IBM revealed. 

It said cloud account credentials make up 90% of the for-sale cloud assets on the dark web, making it “easy for threat actors to take over legitimate user identities to establish access into victim environments”.

Using stolen credentials, threat actors often attempt to escalate privileges to gain greater access, perhaps by modifying policies or adding identities to privileged groups or roles. For example, during an intrusion at a software company, Scattered Spider attackers escalated their privileges by attaching a new administrator access policy to an existing cloud user.

Attackers have also been known to harvest credentials from password stores and were also spotted moving back and forth between on-prem and cloud environments.

Scattered Spider attackers often used their access to Microsoft 365 environments to search for VPN set-up instructions which they then used to log onto the VPN and move across on-premise servers too. They were also spotted using the open source S3 browser to exfiltrate data to a cloud storage bucket they controlled.

Some hackers are also learning as they go along. CrowdStrike said that in February last year it responded to a ransomware incident where the attackers exfiltrated credentials from cloud-based credential manager Azure Key Vault.

Logs show that the ‘Indrik Spider’ attackers also visited ChatGPT while interacting with the Azure Portal, presumably in an attempt to understand how to navigate in Azure, as well as browsing search engines and searching on GitHub.

“Using search engines and visiting ChatGPT indicate that though Indrik Spider is likely new to the cloud and not yet sophisticated in this domain, it is using generative AI to fill these knowledge gaps,” CrowdStrike said.

Cloud security attacks are gaining pace

The CrowdStrike report also found that attackers are moving faster, wherever they find a breach.

After gaining their initial access to a network, adversaries want to move on from that first compromised device. The time it takes for them to do this – the “breakout time” — is key because the first device the attackers land on is rarely the one they need to achieve their goals, whether that’s stealing data or deploying ransomware.

For defenders, then, reacting within the breakout time window can stop an attack before it has a chance to get established. However, that window is shrinking.

CrowdStrike said the average breakout time decreased from 84 minutes in 2022 to 62 minutes in 2023. The fastest observed breakout time was only 2 minutes and 7 seconds.

More generally, how attackers get into networks in the first place is changing too.

Nearly 90% of attack time was dedicated to breaking in and gaining access. By reducing or eliminating this time, attackers can free up resources for more attacks.

As a result, they are moving away from malware toward quicker ways of gaining access – like phishing, social engineering, and exploiting vulnerabilities. Malware-free attacks accounted for 75% of detections in 2023 — up from 71% in 2022.

Defending against this shift in attacks is challenging. CrowdStrike said companies need to implement phishing-resistant multifactor authentication and extend it to legacy systems.

“Addressing sophisticated access methods such as SIM swapping, MFA bypass and the theft of API keys, session cookies and Kerberos tickets requires proactive and continuous hunting for malicious behavior,” it warned.

Companies also need to have a better understanding of their cloud infrastructure.

“Adversaries often use valid credentials to access cloud-facing victim environments and then use legitimate tools to execute their attack, making it difficult for defenders to differentiate between normal user activity and a breach,” the security firm said.

“To identify this type of attack, you need to understand the relationship between identity, cloud, endpoint and data protection telemetry, which may be in separate systems.”

Earlier this year, a separate survey found a 72% increase in cloud infrastructure incidents across 2023, with stolen or leaked credentials responsible for two-in-five incidents.

IBM and mobile industry body the GSMA are launching a training program to support the development of generative artificial intelligence (AI) skills across the telecoms industry.

The new GSMA Advance's AI Training program aims to give telco leaders the skills and knowledge needed to help them get the most out of generative AI technologies, and features Watsonx, IBM's AI and data platform.

IBM said the training program will cover topics ranging from fundamental AI principles through to specialized generative AI applications in telecoms. These sessions will take place at IBM offices in Dubai, London, Mexico, New York and Seoul this year. An online training program will also be available in multiple languages.

A digital version of the program will be available to help tackle the business strategy and technology basics of Generative AI and uses watsonx to deliver hands-on training to equip architects and developers with practical AI know-how.

A second program, the GSMA Foundry Generative AI program, will provide members of the industry group with access to IBM's watsonx. The aim is to help telecoms industry players explore industry-specific use cases of generative AI, looking at improving cost leadership, revenue growth and enhancing customer experience.

Research from IBM accompanying the announcement found that 40% of telecoms firms surveyed are exploring or experimenting with generative AI, and 45% have accelerated the rollout of AI.

Separate research from GSMA Intelligence found that 56% of operators surveyed are actively trialing generative AI solutions – at a rate higher than any other priority technology – but adoption is less widespread in mid-sized and smaller operators surveyed.

AI can provides the telecoms industry with huge opportunities to launch new services, improve connectivity and customer experience said Alex Sinclair, Chief Technology Officer at the GSMA.

"However, it's critical that AI is democratized to ensure that all parts of the connectivity industry and their customers, wherever they are in the world, benefit. Bringing operators access to AI tools and knowledge, alongside the necessary skills, access and training, is key to achieving this," he said.

Research by Amazon Web Services last year found that integrating generative AI into a customer-facing chatbots is part of the first wave of AI adoption by telcos; 92% of survey respondents said they were highly likely to implement generative AI chatbots, and among those, 63% said this was already production.

However, there are likely to be many opportunities for telcos to use generative AI beyond this. For example, AWS said, because generative AI can ingest data from manuals that operators rely on when installing network element, when combined with chatbots, this data can provide interactive guidance based on prompts to speed up and simplify installation tasks.

Alternatively telecoms operators could train large language models to understand network behaviour to help with planning capacity or to help spot potential problems ahead. Ericsson recently detailed some of the areas of potential development for AI in telecoms, such as to support customer incident management, from using raw log data such as alarms or program traces to help detect incidents through to drafting texts for customer support requests or trouble reports.

It said generative AI could be used network planning and deployment and configuration of network nodes, and for jobs like generation of estimated radio maps even with sparse data, and estimating cell load and traffic routing scenarios - as well as malware and rogue device detection.

Software giant SAP has announced plans to invest more than $2 billion as part of a restructuring program aimed at integrating generative AI in key business areas – but it won’t be cutting jobs en masse. 

The corporate restructure will affect around 8,000 roles in total, the company confirmed in a statement on Tuesday. This program aims to drive efficiency through the integration of AI tools and deliver significant revenue boosts.

“In 2024, SAP will further increase its focus on key strategic growth areas, in particular Business AI,” the firm said in its statement. “It also intends to transform its operational setup to capture organizational synergies, AI-driven efficiencies, and to prepare the company for highly scalable future revenue growth.”

The German software firm recorded strong revenue growth in 2023, exceeding expectations. In the year ahead, it also forecasts double-digit growth in revenue from cloud services, begging the question of why it’s embarking on such a comprehensive restructuring initiative.

Much like counterparts in the global tech industry, generative AI has offered an opportunity for the firm to automate roles, cut headcount, and drive profits.

AI-related job cuts were a key concern during the early onset of the generative AI ‘boom’, with warnings of widespread labor market disruption. As of yet, this hasn’t quite panned out, but there have been initial signs that the technology is viewed among business leaders as a means to reduce staff numbers.

PwC’s latest CEO outlook study found that leaders expect to reduce headcount “by at least 5%” in the year ahead due to the integration of generative AI tools.

Tangible examples of this in action last year saw BT announce plans to cut thousands of roles in a bid to automate positions while IBM also said it will replace thousands of back office staff with AI.

In December, reports also emerged that Google was replacing staff in its ad sales division, a key business segment, and replacing them with AI.

SAP eyes staff reskilling and 'voluntary leave programs'

But SAP’s approach here appears measured. The company has been keen to emphasize that it won’t be embarking on a ruthless spree of job cuts, marking a significant change in tone to other industry stakeholders in recent months.

SAP said the majority of affected positions are “expected to be covered by voluntary leave programs”. It has yet to provide additional details on what this will entail, but it suggests the company wants to avoid the negative press of abrupt Zoom meetings and the optics of disgruntled staff leaving offices with boxes.

“Internal re-skilling measures” will be a key focus of this program, SAP said, also suggesting that the software firm intends to train staff in relevant AI skills with an eye for embedding them within different departments.

The rapid adoption of generative AI over the last year has presented significant skills challenges for firms across a range of industries, with research showing that an emerging ‘AI skills gap’ will be a key hurdle for firms in the coming years.

Notably, SAP said that it expects to exit 2024 “at a headcount similar to current levels”. The firm has more than 100,000 employees across global operations.

Revenue boosts as a result of this restructuring won’t be an instant win for SAP, either. All told, the costs of the initiative will be substantial, which it expects to have a negative impact on 2024 earnings.

“Restructuring expenses are preliminarily projected at around €2 billion [($2.18 billion)], the vast majority of which is expected to be recognized in the first half of 2024, impacting IFRS operating profit,” the company said. “Excluding restructuring expenses, the program is expected to provide only a minor cost benefit in 2024.”

With long-term forecasts, however, the benefits do become clearer. SAP said it expects to unlock €500 million ($544 million) in operating profits in 2025 alone due to “efficiency improvements”.

European businesses are lagging behind Asian counterparts in AI adoption, according to IBM’s new Global AI Adoption Index, amid lingering concerns over regulation and safeguards for the technology.  

The findings show a clear gap in adoption rates between the Asian and European markets. Among the nations with the highest levels of AI adoption are India (59%), the UAE (58%), Singapore (53%), and China (50%).

This is compared to the UK where 37% of enterprise-scale companies reported having actively deployed AI, and even fewer in Spain (28%) and France (26%).

The difference between the two regions’ approaches to AI is made more stark when comparing the proportion of companies actively deploying or exploring AI that have accelerated their efforts over the previous 24 months.

For example, 40% of IT professionals based in the UK said their company has accelerated AI roll-outs or investments over the last two years.

Whereas a significantly larger proportion of professionals working in China (85%), India (74%), and the UAE (72%), reported their organizations were intensifying efforts.

Booming budgets are driving Asian AI adoption rates

Speaking to ITPro, Jaeger Glucina, managing director of AI legal platform Luminance, said the adoption gap can be partly understood as the result of expanded technology budgets in Asian companies compared to their European counterparts.

“Asia has been a longstanding leader in terms of intellectual and data assets. If we take China as an example, the scale of the science and technology budget combined with the determined ambition to be a ‘major AI innovation center’ by 2030 goes some way in explaining the adoption gap we see today” Glucina commented.

Glucina warned a hesitant approach to AI adoption from European businesses could stifle innovation in the region, giving other markets an upper hand in an eventual “AI arms race”.

“As more and more countries attempt to compete in the AI arms race, we have seen Europe take a comprehensive but precautionary approach in terms of legislation. While this is understandable, applying overly precautionary principles could end up stifling innovation and leaving Europe lagging behind in the new era of AI adoption.”

The more circumspect approach towards AI from legislative bodies in Europe was also emphasized by John Barber, VP for Europe at Infosys Finacle, who told ITPro this is a key factor behind the adoption gap.

“Europe has been traditionally cautious when it comes to usage of data. In general, there is conservatism that partly stems from its highly regulated environment,” he said.

“We see regulators here [in Europe] taking a more proactive approach compared to Asian markets, and that is true for all emerging tech, including AI. The European Parliament, for example, has moved to adopt the AI Act, which is the first comprehensive law we’ve seen on AI.

“Asian countries such as Singapore, on the other hand, have embraced a more flexible approach to AI regulations, emphasizing innovation over stringent controls. But no approach can be termed right or wrong, and it is the prerogative of the regulators to strike a balance between innovation and consumer rights.”

Barber also pointed out the disparity between the two regions’ consumer behavior and market dynamics could be influencing adoption rates.

“There is also a noticeable difference in consumer behavior and market dynamics between Asia and Europe,” he said. “In Asia, there’s a significant push towards digital-first approaches driven by a young and growing tech-savvy consumer base that readily adopts new technologies.”

“This consumer-driven demand accelerates the adoption of AI and other digital innovations by banks and financial institutions in Asian markets.”

“While Europe is home to many world-class AI research institutions, the region faces a shortage of skilled AI professionals to translate research into practical applications. The situation is similar in some Asian countries and hinders the development and deployment of sophisticated AI solutions.”

Making AI tools more accessible is key to driving adoption in skills-depleted UK

IBM’s findings align with recent data released by the Office for National Statistics detailing the extent to which UK firms are exploring the integration of AI solutions in their day-to-day operations. 

Echoing Barber’s analysis, 38% of respondents said limited expertise and skills deficits relating to AI technologies were the most significant factor holding businesses back.

Almost a quarter of UK organizations lack employees with the required skills in place to leverage new AI tools. Similarly, 19% said they are struggling to find new staff with the requisite skills to address this deficit, IBM’s research shows.

Recent research shows in order to fill vacancies firms are having to relax their criteria for new hires and rely on upskilling them once they are in the role.

The high prices of AI tools (31%) and increasing data complexity (29%) were also explained to be inhibiting AI adoption in the UK.

When asked about the factors driving AI adoption, 51% of respondents said making AI tools more accessible was the most important change that would support transformation efforts.

How does a government have a technical disaster that leaves employees out of pocket seven years on? If you ask the Public Service Alliance of Canada, the union tasked with representing federal government workers, the fault lies squarely at the feet of the Canadian government.

“From overpayments and underpayments to missed paycheques, delayed retirement severance and botched pay grades, more than half of all federal public service workers continue to suffer pay issues because of a broken pay system,” says Public Service Alliance of Canada (PSAC) president Chris Aylward. 

“Being paid properly, and on time, is a fundamental aspect of the employment relationship. Why can’t the federal government – Canada’s largest employer – get this right, when other employers can?”

Their gripe is with the Phoenix pay system, an initiative started in 2009 under then prime minister Stephen Harper, implemented by IBM, and rolled out in 2016 by what had become a Liberal government led by Justin Trudeau. Much has been made of the faults of the system, and large-scale investigations by national media have followed, but the back end root of the problems has been less clear.

Phoenix pay: Colossal IT issues

The scale of the problem was large. PSAC stated that 418,000 cases were still in the backlog as of March 2023, while CBC reported government figures that showed a backlog of 209,000 in April 2023. The issues are wide-ranging and have cost the Canadian government more than $2 billion. As authors from the University of Regina wrote, “The short history of the Phoenix Pay System is a study in public project mismanagement. It illustrates the dangers of rolling out policy changes when substantial political pressure is involved and the challenges of combating multiple and mounting negative outcomes.”

The goal, similar to many federal-level projects of the time, was to cut costs and create a simpler and more financially effective way of paying employees. The initial contract with IBM and the surrounding development and programming costs totalled $309 million. Fast forward 14 years, in 2022, IBM was paid CA$106 million (£63 million) to extend its contract and has repeatedly defended itself against accusations that it is to blame for Phoenix’s issues.

“IBM’s responsibility was the technology component,” then IBM head of innovation, corporate citizenship, and governmental affairs Regan Watts told the Toronto Star in 2018.

“The software is working as it was designed…The responsibility for change management, for training, for business processes, those are outside of our scope. Those are the responsibility of the government of Canada.” 

By 2018, senate hearings had already been initiated and the Canadian government has handed out contracts to vendors in an attempt to stabilize the system, including an eight year CA$16.9 million contract with Ceridian. As of June, 2022, IBM had been paid CA$545 million and another extension has been exercised since then. That addition cost the Canadian government CA$119.37 million and the contract includes ten years-worth of one year options that can be deployed as needed.

Phoenix pay: what does the future hold?

Whoever is to blame—and whether Ceridian can implement a solution with fewer issues— the delays aren’t small. CBC has reported that cases have led to workers being compensated for the equivalent of 4.3 million hours over payment issues, meaning the government has had to recoup more than CA$500 million in overpayments, and employee and public confidence has shattered. 

In the meantime, Aylward claims that those problems are rising, rather than falling.  

“There continues to be a massive backlog of pay problems, both old and new. Despite commitments from the government to reduce the backlog of issues, the pay problems continue to pile up, growing by nearly 10% per year since 2018…The Phoenix pay fiasco is a national disgrace that still affects the lives of tens of thousands of Canadian workers, and this government has consistently failed to do what it takes to make this right. It is time for the government to take urgent action to resolve the Phoenix pay backlog and prioritize solutions to end this disaster.”

The government has its own version of events—along with entire reports detailing what has been learned. Much of their attention has been paid to the business and use-case side of the project, but what can IT professionals learn from the debacle?

The Treasury Board of Canada Secretariat, which authored a document reflecting on Phoenix pay, suggested six key areas where things fell down: accountability, governance, change management, outcome management, and capacity management. In other words, those looking to implement similar systems should be really clear on what they’re trying to accomplish, how the structures of accountability are going to meet that goal, and how much work their organization can handle when implementing a new project. The clear warning from this debacle is that failing to make sure your project stays within budget, within scope, and within capacity is a governance problem as much as a technological one. Digital transformation projects of this magnitude require the most careful implementation possible, no matter how strong the technical foundation.

IBM has entered into a definitive agreement to acquire SoftwareAG’s StreamSets and webMethods platforms for $2.3 billion (€2.13 billion), as the tech giant looks to expand its hybrid cloud and AI product portfolios. 

The German enterprise software firm’s super integration platform-as-a-service (iPaaS) offerings tackle application integration, API management, and data integration, and currently serve more than 1,500 clients around the world.

Acquired by Software AG just last year, cloud native DataOps and data ingestion platform, StreamSets, enables enterprises to leverage consistent access and delivery of data across a wide spectrum of data sources and types, as well as facilitates the design of smart data pipelines.

Integration and API management platform, webMethods, which has been in the company’s hands since 2007, provides B2B integration, managed file transfer, and enables the management, monitoring, and monetization of APIs.

Upon expected completion of the deal in Q2 2024, StreamSets will bring data ingestion capabilities to IBM’s watsonx AI platform, while webMethods will serve up additional integration and API management tools for hybrid multi-cloud environments.

In an announcement, Rob Thomas, IBM’s senior vice president for software and chief commercial officer said the solutions will help clients “unlock the full potential” of their applications and data.

“This powerful combination helps drive innovation while preparing businesses for AI, no matter where applications or data reside," he said. 

"Today's news will give IBM clients and partners one of the most modern and comprehensive application and data integration platforms in the industry."

The move follows the acquisition of Software AG by Silver Lake earlier this year. Since the initial purchase, the private equity firm has increased its stake in the software business to 93% and has revealed plans to take the company private.

For IBM, the acquisition of StreamSets and webMethods is the culmination of a more than 20-year relationship with the Darmstadt-based company and marks the next step in its continued investment in artificial intelligence and hybrid cloud.

The technology giant said it expects the platforms to “drive synergy” with its existing portfolio, which includes watsonx, Red Hat, its range of IT automation offerings, as well its IBM Consulting business.

"IBM is the ideal home for webMethods and StreamSets, the products at the heart of our Super iPaaS vision," commented Sanjay Brahmawar, CEO of Software AG.

"Combined with IBM's global scale and focus on hybrid cloud and AI, our people will have a fantastic opportunity to develop while helping enterprises everywhere get the most out of their applications and data."

StreamSets and webMethods will be acquired with available cash on hand, IBM said, with the completion of the acquisition subject to customary closing conditions.

Created as a spin-off from IBM’s IT infrastructure services, Kyndryl (meaning ‘kyn’ kinship and ‘dryl, referring to tendril) is the result of a separation of BM Global Technology Services from the company’s primary operations.

Focusing on building or updating IT infrastructures, Kyndryl specializes in cloud, digital working spaces, and network infrastructures, including the edge and associated applications for data, AI, and security.

Kyndryl's company history

Kyndryl was created as a reaction to the falling revenue IBM was seeing in its IT infrastructure business. Reported revenues were in decline from $21 billion in 2018 to $17 billion in 2023, but more recently, as Kyndryl continues to develop new services and products for the IT infrastructure space, revenues are expected to grow. Currently, Kyndryl customers include 75 of the Fortune 100 companies.

It's estimated that the company could tap into what is expected to be a $415 billion market that includes more advanced cloud technologies, digital security, and AI-based automation.

Kyndryl is also forging more partnerships to deliver its portfolio of products and services to customers. In 2021, IBM named Microsoft as its first partner to provide cloud infrastructure services, and later that year, a similar deal was struck with Google. And to round out the current leaders in cloud deployment, Kyndryl also has Amazon Web Services as a partner.

What does Kyndryl sell?

The company is currently structured into several managed service providers. Kyndryl approaches each technology separately, including data and AI, application development, enterprise operations with zCloud, digital workplaces, mainframe as a service, and cyber security.

In 2022, Kyndryl launched two new products as branded services – Bridge and Vital. Kyndryl Bridge, as its name suggests, connects together siloed data sources and isolated applications.

Antoine Shagoury, Kyndryl’s chief technology officer, explained at the launch: “It provides greater access and control over critical tools, and Kyndryl experts in cloud, data and AI, security and resiliency, mainframe modernization, and other important aspects of the IT estate. Then, we use our engineering expertise and AI to analyze the results in real-time to provide operations personnel the intelligence they need to keep systems running at peak performance.”

“As customers accelerate their digital transformation, Kyndryl’s new digital integration platform can help provide rich data insights, solutions automation, and services delivery expertise,” said Nick Holden, vice president, Cisco Global Strategic Partners and Co-sell. “Kyndryl Bridge is a big step forward to enable Kyndryl and Cisco to deliver business outcomes that our joint customers are demanding.”

Kyndryl Vital is, in essence, a consultancy service that connects businesses to existing Kyndryl services or helps them create the infrastructure or applications they need to innovate. 

“Kyndryl Vital is part of a continuum that extends through Kyndryl Bridge, a new open integration platform which gives business leaders real-time insights into their complex IT estates and unprecedented control over customizing their mission-critical operations,” Kyndryl explained at the launch of this service.

Kyndryl's mergers and acquisitions

Since the creation of Kyndryl, IBM has favoured the development of new products and services over any direct mergers or acquisitions. The company’s strategic partnerships with Amazon, Google, and Microsoft deliver to Kyndryl the expertise it needs to service its customers and the diverse IT infrastructure required to support its ambitions.

However, IBM’s own acquisitions speak volumes about which areas of technology they see as expanding, and that Kyndryl would be ideally positioned to exploit. Data companies have formed a large part of their purchases, including Databand.ai, Blue Solutions Group, Nordcloud and Sentaca.

Kyndryl's key people

As Kyndryl is a spin-off from IBM, the staff that worked on these projects moved over to the new company. All the critical points of contact for their customers have remained the same.

When the new company was established, IBM brought in new people, most notably Martin Schroeter (pictured), who became CEO. Schroeter was a previous IBM employee (SVP of global markets), leaving the company in 2020. 

Maria Bartolome Winans became CMO in 2021, and the group president is Elly Keinan, another IBM veteran. Una Pulizzi, who was appointed global head of corporate affairs, was moving over from GE.

On the technical side of the business, Michael Bradshaw moved from being CIO at NBC/Universal to become Kyndryl’s CIO. An ex-CIO at US bank State Street, Antoine Shagoury was appointed CTO.

IBM and Meta are set to lead a new AI alliance focused on the responsible, open development of artificial intelligence (AI), but notable industry competitors will not participate.

The coalition’s stated goal is to support a transparent and collaborative approach to AI development that the duo said will help produce evaluation standards and tools for building AI systems, as well as educational content to inform public discourse on the technology.

The two technology giants are joined by over 50 founding members and collaborators, including hardware and infrastructure producers, model builders, research institutions, and open source foundations.

Other industry stakeholders pledging support for the alliance include AMD, Intel, Hugging Face, Dell Technologies, Cerebras, Sony, and Oracle.

A number of research institutions have also joined the coalition including notable institutions  such as Harvard University, Yale University, Imperial College London, the University of Tokyo, UCLA Berkeley, and Notre Dame.

IBM AI Alliance: What will the coalition do?

The focus of the alliance appears to be on ensuring the future of AI development is open and retains considerations around safety and trust. 

In a blog post announcing the partnership IBM said an open, collaborative approach to AI innovation will be critical to advancing the technology. 

“Open and transparent innovation is essential to empower a broad spectrum of AI researchers, builders, and adopters with the information and tools needed to harness these advancements in ways that prioritize safety, diversity, economic opportunity and benefits to all.”

IBM and Meta hope the coalition will bring together influential members in the sector to pool resources and knowledge to ensure safety concerns are adequately addressed during the ongoing wave of AI development.

This will see the alliance launch or enhance AI projects that satisfy a list of core objectives centered around fostering a healthy environment for responsible AI innovation.

One of these objectives aims to develop and implement evaluation standards for AI systems, produce a list of vetted safety and trust tools, and advocate for the widespread adoption of these standards and tools.

These projects should also facilitate wider AI skills development on a global scale and produce resources to inform public discourse on AI, the duo said, which long-term will enable more precise and productive regulations on the technology.

Does this alliance represent a new or false hope?

Notable omissions on the list of alliance members include Google, Amazon, Microsoft, Anthropic, and OpenAI, which make up the most influential AI model developers in the sector.

Microsoft, Google, OpenAI, and Anthropic all joined the Frontier Model Forum in July 2023, perhaps explaining their disinclination to join this new alliance.

The stated goals of the partnership run along the same lines as the coalition announced by IBM and Meta, but retain a particular focus on safeguarding the development of frontier models – defined as large language models (LLMs) that surpass the most advanced models available today.

Some experts expressed pessimism about how realistic the partnership was in achieving its stated goals of cooperation with the Forum’s members being major competitors.

Is open AI collaboration a pipe dream?

There’s no doubt that new methods for improved model training - or frameworks through which models could circumvent unwanted hallucinations or harmful output entirely - are welcomed. Collaboration between the public and private sector will be a necessary part of this.

Ultimately, however, these kinds of partnerships come second to the guardrails of the market as defined by legislation and competition. AI developers will continue to collaborate on best practices and enter into dialog with world leaders over the potential risk of AI implementation, but the bar for transparency will always lie with regulators.

The ‘black box’ approach to AI development, in which training weights and the nature of data used to train AI models are kept largely secret by each company, is likely to be a sticking point for open AI development. If you don’t know the precise decisions and information that has gone into making a model, evaluation systems can only go so far.

With its focus on open source AI, Meta is arguably in a better position to engage in the kind of meaningful collaboration with academia that will be necessary to lay the groundwork for ethical AI. 

But even Meta has set limitations according to market competition. The firm’s open LLM, Llama 2, has been advertised as an open source model but can only be used by firms with more than 700 million monthly active users with permission from Meta. 

IBM has unveiled its new watsonx.governance platform in a bid to help organizations to responsibly manage risk and compliance when deploying large language models (LLMs).

Watsonx.governance is the third arm to IBM’s watsonx artificial intelligence (AI) and data management platform, designed to support organizations leveraging and scaling AI tools.

The new solution, generally available on 5 December 2023, will help organizations deploy generative AI tools responsibly by ensuring they adhere to their own internal AI governance policies and prepare for upcoming regulations.

The platform will provide insights into the ethical considerations of deploying LLMs as organizations look to get the most out of generative AI without exposing themselves, or their clients, to the risks AI can pose.

“Watsonx.governance is a one-stop-shop for businesses that are struggling to deploy and manage both LLM and ML models, giving businesses the tools they need to automate AI governance processes, monitor their models, and take corrective action, all with increased visibility”, said Kareem Yusuf, senior vice president, product management and growth at IBM Software.

“Its ability to translate regulations into enforceable policies will only become more essential for enterprises as new AI regulation takes hold worldwide.”

What users can expect from watsonx.governance 

The new software will enhance AI governance by meeting the intersection of three key business needs around AI deployment, according to IBM. 

This includes lifecycle management, model evaluation, and risk assessment.

Lifecycle management consists of providing automated alerts to stakeholders when models operate in ways they were not designed to and producing documentation on how models were developed and implemented. 

IBM said this will be vital in order to demonstrate organizations are complying with regulations. 

Watsonx.governance will evaluate models by producing a variety of metrics associated with the accuracy, drift, or bias of the model, IBM said. 

This includes metrics indicating the safety of the model by assessing the use of potentially harmful language, or how the input and output of personally identifiable information (PII) is handled.

Finally, the software will provide metrics on the health of the model based on the size of the dataset it was trained on, as well as the model’s throughput and latency.

The risk assessment component of watsonx.governance introduces the human element to governing the roll-out generative AI, according to IBM.

The tool will help automate many of the basic functions and tasks involved in AI governance while also identifying areas where human intervention is required to ensure accountability and transparency.

This includes creating auditable documents that demonstrate which agents carried out what checks relating to specific model behaviors. 

Promising quarterly earnings results at a host of major tech firms suggest the maturation of artificial intelligence (AI) is becoming a key pillar for revenue generation. 

Amazon, IBM, Microsoft, Alphabet, and Intel have all credited recent revenue successes in part to AI products driving the performance of key business segments. 

IBM recorded a year-on-year revenue increase of 4.6% to $14.8 billion this week despite lingering economic uncertainty. The firm’s strongest performing segment was software with 7.8% revenue growth, driven by a strong performance in its cloud and automation services. 

Revenue in its data and AI solutions also increased by 6% year-on-year.  

On an earnings call, IBM chief executive Arvind Krishna said increased adoption of the firm’s watsonx AI and data platform alongside its hybrid cloud services were underpinning solid growth in its software and consulting businesses. 

Krishna added that growing interest in AI and cloud services points to a renewed optimism for the year ahead.

In a similar vein, Amazon views AI and cloud, as well as its advertising business, as key elements to their future growth strategy. AWS revenue grew 12% year-on-year to $23.06 billion, helping stabilize a decline in sales growth from previous quarters. 

The tech giant’s generative AI framework, Bedrock, has garnered immense excitement in recent months. IBM recently announced the general availability of Bedrock to customers globally, and CEO Andy Jassy suggested the firm's continued focus on generative AI is delivering significant benefits for customers. 

Microsoft and Intel cash in on generative AI promise

Microsoft, which struck an early lead in the generative AI race this year, also surpassed expectations in its quarterly earnings call. The firm recorded revenue of $56.5 billion, marking an increase of 3.6% and exceeding analyst forecasts. 

Again, AI’s role in this can be seen in the 19% revenue increase for Intelligent Cloud to $24.3 billion.

Microsoft’s Intelligent Cloud department includes Azure and other cloud services which underpin the company’s AI solutions. Year-on-year revenue growth of 29% at Azure reflects the key role generative AI is playing to bolster financial performance, the firm said. 

Intel’s shares are up 7%, aided in part by a rebound in PC chip sales and also by the wider implementation of its AI technologies, according to company execs. 

The company’s foundry revenues increased by 299% year-on-year to $311 million, with its Datacentre and AI segment representing just over a quarter of the total $14.2 billion revenue. 

CEO Pat Gelsinger attributed the firm's "outstand quarter" to the ongoing momentum surrounding AI. 

“We delivered a standout third quarter, underscored by across-the-board progress on our process and product roadmaps, agreements with new foundry customers, and momentum as we bring AI everywhere," he said. 

Concerns for Alphabet

Alphabet, parent company of Google, saw its total revenue increase by 11% year-on-year to $76.69 billion, but did report poorer than expected performance in its cloud services segment with operating income from $440 million to $266 million. 

Alphabet CEO Sundar Pichai said this was due to external challenges meaning “customers [are] looking to optimize spend”. 

However, this does potentially signal that Microsoft’s impressive cloud performance might be impinging on Alphabet’s cloud customer base. 

Despite the dip in its cloud segment, Alphabet was bolstered by gains in income from Google services, with a 26% increase year-on-year.

In his statement, Pichai emphasized the importance of AI-driven innovations across the company’s divisions, suggesting that current investment is positioning generative AI as a core aspect of its broader cloud computing strategy.

IBM has announced an expansion of its partnership with AWS as the tech giant looks to help mutual clients harness generative AI solutions and services. 

The pair will infuse their existing range of joint AI solutions and services with generative AI to help customers integrate the technology swiftly into their business and IT operations.

The collaboration will initially focus on three specific offerings. The first, Contact Center Modernization with Amazon Connect, now features generative AI powered summarization and categorization functions for voice and digital interactions, which allows for transfers between a chatbot and live agent to improve resolution times. 

Platform Services on AWS has also been given the generative AI treatment in a bid to better manage the entire cloud value chain and speed up repair times.  

The offering now includes tools that are designed to improve business serviceability and availability for AWS-hosted applications through intelligent issue resolution and observability techniques, IBM said.  

Thirdly, Supply Chain Ensemble on AWS will feature a virtual assistant to help clients tackle challenges across the supply chain, including reduction of costs, streamlining of logistics, risk assessment, and more. 

"Enterprise clients are looking for expert help to build a strategy and develop generative AI use cases that can drive business value and transformation – while mitigating risks," explained Manish Goyal, senior partner, global AI and analytics leader at IBM Consulting. 

"Paired with IBM's AI heritage and deep expertise in business transformation on AWS, this suite of reengineered solutions with embedded generative AI capabilities can help our mutual clients to scale generative AI applications rapidly and responsibly on their platform of choice."

As part of the expanded partnership, IBM Consulting is also planning to integrate AWS generative AI services into its IBM Consulting Cloud Accelerator, which the firm said will aid with reverse engineering, code generation, and code conversion through the cloud transformation process.

IBM Consulting also announced plans to train 10,000 consultants on AWS generative AI services by the end of 2024, via a new partner-only program that focuses on top use cases and best practices.

Additionally, IBM is also making watsonx.data available on AWS as a fully managed SaaS solution and accessible via AWS Marketplace, while watsonx.ai and watsonx.governance are slated for AWS availability by 2024.

Chris Niederman, managing director of AWS’ global systems integrators segment, said the company’s customers are increasingly looking for AI expertise and support to "build and implement a generative AI strategy that drives business value from their entire cloud value chain.” 

"We are excited to be working with IBM to include embedded generative AI capabilities that assist our mutual customers scale their applications – and help IBM consultants deepen their expertise on best practices for customer engagement with AWS generative AI services," he said.

IBM CEO Arvind Krishna appears to be in a state of damage control following recent controversial comments on AI-related job losses. 

Speaking at an event in the US this week, Krishna said IBM has no intention of laying off tech staff, such as developers or programmers, and instead plans to ramp up hiring for roles in these areas. 

“I don’t intend to get rid of a single one,” he said. “I’ll get more.”

Krishna added that the company aims to increase the number of software engineering and sales staff over the next four years to accommodate for its heightened focus on generative AI. 

Instead, the hammer will fall largely on staff working in back-office operations, aligning closely with what we’ve heard previously from the exec.

Earlier this year, IBM announced plans to cut nearly 8,000 staff working in positions spanning human resources in a bid to automate roles. 

The move means that anywhere up to 7,800 jobs at the tech giant’s HR division could be cut, equivalent to around 30% of the overall workforce in the unit.

IBM also said at the time that it would halt hiring for roles in the division on account of positions being automated.

Krishna has been among the most outspoken big tech executives on the topic of AI job losses in recent months. While industry figureheads have repeatedly shirked the topic, Krishna, to his credit, has been candid on the subject. 

In an interview with CNBC in August, Krishna suggested “we should all feel better” about the influx of generative AI tools, much to the ire of critics worried about its impact on the labor market. 

Krishna also told the broadcaster that organizations can deliver marked improvements to productivity through generative AI, but that will come at the expense of human roles.  

“That means you can get the same work done with fewer people,” he said at the time. “That’s just the nature of productivity. I actually believe that the first set of roles that will get impacted are - what I call - back office, white-collar work.”

This latest incident is the second minor ‘backtrack’ from Krishna in recent months. Earlier comments in the wake of IBM’s HR layoffs decision saw Krishna rush to clarify his position on the matter.

In an interview with Barron’s, the IBM CEO said he believed his comments were “taken out of context” and that “AI is going to create more jobs than it takes away”. 

AI-related job losses

The prospect of AI-related job losses have been a recurring talking point over the last year amid the rapid acceleration of generative AI tools globally.

Goldman Sachs predicted that “up to 300 million” jobs globally could be lost to automation over the next decade in a report that sparked concerns about widespread disruption to the labor market. 

Similarly, a study from McKinsey in July found that certain administrative roles, such as customer service, office support, and HR, will also decline due to automation.

While various studies agree that AI-related job losses could be an issue in the coming years, much of these losses will be in back-office positions, suggesting a reprieve for those working in frontline tech roles. 

Krishna told attendees this week that while AI could offer support for workers carrying out “lower level tasks”, the prospect of a role being replaced entirely is unlikely.

“The first thing you can automate is a repetitive, white-collar job,” he said.

Although these latest comments may lead to a sigh of relief for programmers and developers, the reality may not be quite so reassuring for some. 

Research from KPMG in June revealed that IT support technicians and programmers could in fact be among the worst hit by automation in the coming years.  

In the study, “programmer and software development professionals” were specifically highlighted as being at risk due to generative AI, with up to 26% of their tasks potentially being automated in the future.

The study once again plunged the topic of job cuts back into the news cycle. 

However, there is a distinct caveat here. The degree to which roles will be ‘automated’ doesn’t necessarily point to said roles being rendered obsolete, more the fact that developers and IT practitioners will be supported by automated tools in their daily tasks. 

Generative AI tools such as GitHub Copilot or Amazon’s Code Whisperer AI assistant have been framed by enterprises as potentially game changing tools to improve productivity for workers.  

 Research from Slack in May found that workers believe generative AI tools will “revolutionize” productivity. 

More than three-quarters (77%) of respondents told the firm that the ability to automate routine tasks, such as expense reports, would drastically improve productivity. 

Additionally, the study found that those currently using automation in the workplace estimate they save an average of 3.6 hours’ worth of work every week. 

IBM has announced a host of new enhancements for its watsonx platform, which include new generative AI foundation models and new capabilities. 

Designed to help enterprises scale and accelerate the impact of artificial intelligence, watsonx is IBM’s AI and data platform that combines AI capabilities with analytical software.

The offering’s upcoming enhancements include a technical preview for watsonx.governance, new generative AI data services coming to watsonx.data, as well as a planned integration of watsonx.ai foundation models across select software and products.

“As demonstrated by the ongoing rollout of the watsonx platform within just a few months since launch, we are here to support clients through the entire AI lifecycle” said Dinesh Nirmal, senior vice president of Products at IBM Software. 

“As a transformation partner, IBM is collaborating with clients to help them scale AI in a trustworthy way – from helping to institute foundational elements of their data strategies to tuning models for their specific business use cases to helping them govern models beyond that.”

Generative AI models

The new IBM and third party generative AI models coming to watsonx.ai include its Granite series models, planned for later this month. These use the Decoder architecture of current large language models (LLMs) to predict the next word in a sequence, and can support enterprise natural language processing (NLP) tasks such as summarization, content generation, and insight extraction. 

IBM said it plans to provide a list of the sources of data as well as a description of the data processing and filtering steps that were performed to produce the training data for the Granite series of models – with estimated availability in Q3 2023.

As for third-party models, the company is also offering Meta’s Llama 2-chat 70 billion parameter model, as well as the StarCoder LLM for code generation in watsonx.ai on IBM cloud.

IBM said it has established a training process for its foundation models that’s centred on principles of trust and transparency for enabling responsible deployments of models and applications.

New watsonx capabilities

Freshly revealed capabilities for watsonx.ai include the first iteration of IBM’s Tuning studio. Expected to be available in Q3, the tool will include prompt tuning for adapting foundation models to unique downstream tasks. 

Available now, there’s also a new synthetic data generator for assisting in the creation of artificial tabular data sets from custom data schemas or internal data sets for extracting insights for AI model training.

IBM revealed it plans to “infuse” watsonx.ai capabilities into watsonx.data to help users process data for AI through a “self-service experience” that’s powered by a conversational natural language interface. The company is also planning to integrate a vector database capability into watsonx.data to support watsonx.ai retrieval augmented generation use cases. Tech previews are planned for Q4.

The firm is also launching a tech preview for watsonx.governance, which will enable users to explore capabilities for automated collection and documentation of foundation model details and model risk governance capabilities. This will allow stakeholders to view relevant metrics in dashboards of their enterprise-wide AI workflows with approvals so humans are engaged at the right times, IBM said.

AI assistants

Additionally, the watsonx platform is gaining a new set of AI assistants. For application modernization, watsonx Code Assistant products - coming later this year – will use tailored foundation models to convert code and generate code recommendations for developers. 

Meanwhile, the new watsonx Assistant will help users deliver consistent and intelligent customer service solutions with conversational AI, IBM said, while watsonx Orchestrate aims to help HR professionals automate repetitive, high-friction tasks and back office processes via a conversational interface.

IBM added it plans to embed its watsonx.ai innovations across its hybrid cloud software and infrastructure products, including intelligent IT automation, and developer services for watsonx.

Developers will be able to try out many of these new capabilities and models at IBM’s TechXchange technical learning event in Las Vegas, from 11-14 September.

IBM has announced it plans to increase prices for cloud computing services from the beginning of next year.

A GitHub release note on pricing changes for the tech giant’s infrastructure as a service (IaaS) and platform as a service (PaaS) offerings shows that some customers worldwide could see an uplift of up to 29%.

In this instance, uplift refers to the price of services compared to US base costs. 

“Prices for…infrastructure services vary from location to location, based on a percentage uplift from US base prices,” the note reads.

IaaS price increases will apply to bare metal services, virtual service instances, file and block storage, and networking infrastructure, according to the release note.

Similarly, global prices for accelerated archive storage and deep archive storage will surge by 25% and 26% respectively.

According to the release note, there are no planned changes to prices for IBM power systems virtual servers, third-party software, or network bandwidth.

IBM price hikes by region

IBM customers operating in the firm’s Sao Paolo region are set for the largest price uplift at 29% - and European counterparts can also expect sizable increases.

The London data center region will see a 13% uplift while those in Frankfurt, Madrid, Milan, and Paris are set for a 16% hike.

IBM’s PaaS offerings are also set for a 3% price increase across the board globally.

This increase will apply to users of Red Hat OpenShift, IBM Cloud databases, and the firm’s Kubernetes service.

Cloud costs on the rise across the globe

These latest pricing changes could further raise concerns among global businesses reliant on cloud services, which have been steadily increasing over the last 18 months.

Many firms have reported struggling with rising cloud costs amidst challenging economic conditions, surging inflation, and rising energy costs.

Cloud costs have prompted many organisations to reassess their relationship with the cloud, exploring optimization practices such as FinOps, or even going so far as to repatriate workloads to on-prem hardware.

IBM isn’t alone in its decision to increase prices. Analysis from Liftr in February showed that the average price of cloud services at AWS had increased by 23% over the previous year until that point.

Elsewhere in 2023, a host of other major firms have hiked prices, including Salesforce. In July, the firm revealed that it planned to increase prices by 9% across its Sales Cloud, Service Cloud, and Marketing cloud offerings.

The price changes marked the first list price increase in seven years for the tech giant.

In July, Microsoft revealed changes to its fees in line with its consistent global pricing policy for cloud services.

The policy, which bases prices on currency exchange rates on the US dollar, means that customers in Australia and New Zealand are set for an increase of up to 9% for services.

IBM has announced plans to take part in a $235 million series D funding round of the open source and open science artificial intelligence platform Hugging Face.

Other companies involved in the investment included Google, Amazon, Intel, and Salesforce. The funding round was led by Salesforce Ventures, according to Salesforce CEO Marc Benioff. Hugging Face CEO Clem Delangue said the round has taken Hugging Face’s value to $4.5 billion, more than double 2022’s valuation of $2 billion.

The investment and valuation is an indicator of the appetite for AI companies in the marketplace and the development of the technology by investors.

Delangue used the investment round to celebrate the crossing of 1,000,000 models, datasets, and apps on the platform.

Of the investors, he said: “These partners alone shared over 1,000 open models and datasets and have over 10,000 users on Hugging Face already. It takes a village to democratize good machine learning with open source and we’re just getting started!”

Delangue’s plan for the investment is growth. He said: “My priority for the next month? Hiring, hiring, hiring with a big focus on diversity!”

Hugging Face is an interesting organization in the AI space. Where some companies take care to protect their AI models and charge for access, the Hugging Face model is more about collaboration, with datasets and applications shared among the community.

That said, Hugging Face has chargeable options. Its Enterprise Hub, for example, starts at $20 per user per month and provides SSO and SAM support as well as storage location options - a significant feature for customers facing regulatory oversight.

Charging extra for access to more compute power and customer support represents an important revenue stream for the company, even if its revenues are far below its current valuation.

Its Hub, however, remains “forever” free and includes unlimited models, datasets, and spaces.

The news follows IBM - in collaboration with NASA - open sourcing a large geospatial AI foundation model available on Hugging Face. IBM and Hugging Face also announced plans in May to collaborate on watsonx.ai, an enterprise studio aimed at AI builders to train, validate, and tune both traditional machine learning models and newer generative AI capabilities powered by foundation models.

Hugging Face has also collaborated with other tech giants. For example, in May 2023, it announced plans to bring open source models from the Hugging Face Hub to Aure Machine Learning. Earlier in August, a partnership with NVIDIA was announced to give developers access to NVIDIA DGX Cloud AI supercomputing within the Hugging Face platform.

IBM chief executive Arvind Krishna’s latest comments on generative AI-related job losses paint a worrying picture of the fate awaiting workers globally as enterprises look to adopt the technology and streamline productivity.

Krishna shed more light on how the company views recent developments in the generative AI space in an interview with CNBC, noting that the productivity benefits afforded by the technology should not be overlooked by enterprises.

Long-term, he said, generative AI has the potential to “make every enterprise process more productive” and unlock marked benefits for organizations - but that will likely come at the expense of human-held jobs.

“That means you can get the same work done with fewer people,” he said. “That’s just the nature of productivity. I actually believe that the first set of roles that will get impacted are - what I call - back office, white-collar work.”

Krishna’s comments come as no surprise given the widely-documented risks to human workers through generative AI over the last nine months.

“Up to 300 million” jobs globally could be lost to automation in the coming years, according to a report from Goldman Sachs, while a study from McKinsey in July found that administrative roles, such as office support, customer service, and HR will see a decline in roles due to generative AI.

What Krishna’s comments do point to is that the veneer of positive marketing positioning generative AI as a mere support tool for human workers is showing cracks.

IBM has been bullish on generative AI throughout 2023, and Krishna has been outspoken on the company’s push to deeply integrate these tools within day-to-day operations. In May, the chief exec revealed that around 7,800 staff in non-customer-facing roles could be replaced by AI, equivalent to roughly 30% of staff currently occupying these positions.

The firm has gone so far as to freeze hiring for roles in human resources and assorted “back office” positions in anticipation of automation on these fronts.

IBM isn’t alone in its focus on AI integration and cutting human workforces, however.

Earlier this year, BT announced it plans to lay off tens of thousands of workers in the coming years and automate a slew of roles. The cuts will see the telecoms giant lay off around 40% of its workforce by the end of the decade.

The firm framed this as a strategy to create a “leaner business”, but the focus on integrating AI that forms a key aspect of this strategy will see up to 10,000 roles cut and replaced with AI.

Administrative and “back office” roles aren’t the only positions in the crosshairs amid the generative AI boom either. KPMG research in July found that tasks performed by programmers and software developers are at risk, along with IT support technicians.

Despite this, Krishna appeared insistent that augmenting human workers is still the key focus, and not workforce consolidation and automation.

“It’s absolutely not displacing, it’s augmenting,” he told CNBC. “The more labor we get, especially if it’s not human based at all, we can create more GDP. We should all feel better about it.”

This position does align with recent analysis from the International Labor Organisation (ILO), which suggested that most jobs and industries are “only partially exposed” to automation, meaning roles are “more likely to be complemented rather than substituted by AI”.

“The most important impact of the technology is likely to be of augmenting work,” the ILO study stated.

Open source giant Red Hat has announced the appointment of Andrew Brown as its new senior vice president and chief revenue officer. 

The former IBM executive joins the business immediately and will succeed current CRO Larry Stack from October 1. Stack will work alongside Brown through Q3 to ensure a smooth leadership transition, Red Hat said.

In his new role, Brown will oversee commercial and public sector sales, channel and alliance sales, consulting services, as well as Red Hat’s global go-to-market strategy.

“Red Hat has an enormous opportunity to fuel hybrid cloud transformation globally, but this requires seamlessly integrating our product vision with our sales approach,” said Matt Hicks, president and CEO of Red Hat. 

“Andrew understands the intricacies of Red Hat’s business and our culture, and brings vast experience in delivering customer value at scale across a wide range of technology areas as we seek to address evolving customer requirements.”

A software industry veteran, Brown arrives with more than 28 years’ experience in hybrid cloud and transformational technology. He began his career at IBM as an intern, before working his way up to a variety of senior leadership roles across its software business.

Most recently, he served as general manager of IBM Technology Sales for the United Kingdom and Ireland. In that role, he was responsible for leading, transforming, and expanding the company’s Technology go-to-market organization, as well as overseeing the sales, strategy, management, and delivery of IBM offerings – including systems, public cloud, data and AI solutions, and more. 

Commenting on his new role at Red Hat, Brown said he was attracted by the open source company’s innovative approach to its operations.

“I have followed Red Hat’s growth and progress over the last few years and have been impressed by the agility, foresight, and innovative approach that the company and its culture embrace,” he said.

“I am hugely excited about joining this unique organization and working with our customer-facing teams to innovate, collaborate and scale the hybrid cloud solutions Red Hat possesses in its open source platform approach.”

Brown will replace outgoing CRO Larry Stack from October 1, who moves on to become a senior advisor within the IBM Global Government team.

“Being a part of Red Hat, particularly working with passionate and exceptional Red Hatters, is a highlight in my career as a servant leader,” Stack said. “I am proud of everything that we have been able to accomplish using a ‘Mission First, People Always’ motto. 

“I know that there is still so much to come and I look forward to seeing what Red Hat can achieve in the next phase of open source.”

Oracle has waded into the Red Hat Enterprise Linux (RHEL) source code furor in a stunning turn of events, claiming it’s “committed to Linux freedom”. 

Oracle’s words will doubtless cause users who remember OpenSolaris to raise an eyebrow or two. Organizations dealing with the pain arising from Oracle’s changes around Java licensing – the introduction of the Java SE Universal Subscription – will also be taken aback by the company’s championing of open and free principles. 

Still, as far as Linux is concerned, Oracle’s intervention comes in the form of a blog post headlined “Keep Linux Open and Free”. It’s reaffirmed, over the last quarter of a century, its goal has been to “help make Linux the best server operating system for everyone, freely available to all, with high-quality, low-cost support provided to those who need it”. 

In fact, Oracle’s principal goal over the last 25 years has been driving shareholder value. Oracle Linux – itself derived from RHEL – exists only to drive revenues. Furthermore, seeing the words “low-cost” and “Oracle” in the same paragraph will come as a novelty to users faced with calculating the cost of their licenses. 

Like other RHEL-compatible distributions, such as Rocky Linux and Alma Linux, Oracle Linux has been affected by Red Hat’s decision to stop publicly releasing the RHEL source code.  

While both the former distributions have devised ways to remain compatible, Oracle has cast doubt on where its own distribution stands, saying Oracle Linux “will continue to be RHEL-compatible to the extent we can make it so”. It also warned after version 9.2, there may be “a greater chance for compatibility issues to arise”. 

Paying developers and supporting coders 

Oracle was quick to blame Red Hat’s owner, IBM, for the current debacle and pointed out the excuse for not releasing source code – somebody has to pay RHEL developers – did not really hold water. After all, Red Hat had managed to do so for many years before IBM’s $34 billion acquisition. 

However, Oracle has a chequered history when it comes to supporting developers and it’s difficult not to consider the company’s behavior with regard to Java.

In 2010, Oracle acquired Sun Microsystems and, over the following decade, introduced paid licensing options. In 2019 it declared that security updates for Java 8 couldn’t be downloaded without a subscription. 

This year saw the firm introduce the controversial Oracle Java SE Universal Subscription. It could result in an organization having to purchase a license for the entire employee population, even if Java is only required for a single user or server.  

As such, Oracle’s critique, particularly the line about “more revenue opportunity for IBM”, is a little on the nose considering its own revenue-raising moves. One must also consider the fate of OpenSolaris. After it acquired Sun Microsystems in 2010, the firm discontinued in-house development of the open source OpenSolaris operating system – although a fork, OpenIndiana, continued. Oracle instead focused its efforts on the proprietary Oracle Solaris. 

Oracle’s “Keep Linux Open and Free” line is all the more of an about-face. 

What about the competition? 

Alongside Oracle’s pronouncements, the veteran Linux distribution SUSE has thrown its chameleon into the ring. Unlike Oracle’s blustering, SUSE has come up with some concrete proposals: fork RHEL, maintain a RHEL-compatible distribution without restrictions, and invest $10 million into the project. 

SUSE introduced Liberty Linux last year, aimed at customers needing CentOS and RHEL support, but insisted it remained fully committed to SUSE Linux Enterprise, the Adaptable Linux Platform, and openSUSE. “Becoming more proprietary should not be the basis for competition between open source companies,” CEO Dirk-Peter van Leeuwen said. 

SUSE’s move has been welcomed by Gregory Kurtzer, CEO of CIQ and founder of Rocky Linux, with Kurtzer saying SUSE “has embodied the core principles and spirit of open source”. He went on to commend the collaboration opportunities afforded by SUSE’s move. 

Oracle, on the other hand, has been quick to criticize IBM and Red Hat’s actions while painting itself as one of the custodians of Linux freedom. Developers who remember its history, and recent activities, will find the company’s newfound fervor around open source operating systems hard to take. 

Customers wondering where to go next for their RHEL-compatible needs must also pay attention to the lack of detail in Oracle’s post beyond the fact its software will continue to be developed and tested on Oracle Linux, not RHEL. 

Oracle has warned that compatibility issues might arise, but it’s also laid the blame for problems at the door of IBM and Red Hat. And, of course, its own software will carry on working anyway. Regardless of how noble Oracle’s intervention may be, it will always have its own revenues to worry about.  

The National Institute of Standards and Technology (NIST) has published research into a ‘toggle switch’ aimed at making quantum computers more versatile and help cut down on noisy circuits.

Noise has bedevilled designers of quantum computers and is essentially random behaviour that can create errors in qubit calculations.

IBM recently published research revealing how errors can be mitigated to the point where a quantum computer could outperform classical supercomputing methods.

Rather than suppressing errors, IBM’s researchers explored a portfolio of mitigation techniques, including probabilistic error cancellation and Twirled Readout Error eXtinction (TREX).

The NIST approach relies on what is referred to as a “toggle switch”, a device that connects the qubits to a circuit called a “readout resonator”. When the switch is off, the elements are isolated from each other, but when on, the qubits can interact and perform calculations. Once complete, the switch can then connect a qubit to the readout resonator for result retrieval.

The toggle switch and readout circuit must be made of superconducting components and the switch itself is made from a superconducting quantum interference device. Interactions between the qubits and the readout resonator are induced by driving a microwave current through a nearby antenna loop.

While currently at the research stage, the approach also has the benefit of introducing programmability to otherwise static architecture.

Does this mean that quantum computers are just around the corner?

Possibly. While the research from NIST only deals with two qubits and a single resonator and must be operated at very low temperatures, plans are afoot to add more qubits and resonators.

However, actually constructing a quantum computer using such techniques with enough qubits to solve problems that are currently insurmountable remains in the future.

That said, quantum computing does seem to be nearing utility. Following its error mitigation research, IBM expects to complete its Quantum systems over the next year. The machines will be powered by a minimum of 127 qubits and, according to IBM, will provide access to computational power “large enough to surpass classical methods for certain applications”.

IBM has coined the term ‘utility-scale’ - the point at which quantum computers could serve as scientific tools to explore a new scale of problems that classical systems may never be able to solve. With the company’s IBM Quantum systems due for completion by the end of 2024, that point is now in sight.

Other companies, such as Intel and Microsoft have similarly made strides toward usefulness.

What could this technology be used for?

Quantum computing has historically been long on promise and short on result. However, the IBM team has been able to generate large, entangled states that simulate the dynamics of spins in a model of material and accurately predict properties such as its magnetization.

Other uses include healthcare and life sciences, where researchers are looking into using quantum chemistry and quantum machine learning to accelerate molecular discovery. There are potential applications in high energy physics and optimization as well as further work in materials simulation.

The words “quantum machine learning” might make some observers concerned. Dell CTO John Roese recently argued that the technology would prove far more disruptive than the current upheaval caused by generative technologies.

There is also concern that quantum computing, once useful, could be used to decrypt data secured with, for example, an RSA algorithm.

Quantum computing has long been a technology that has proven elusive. Recent developments have shown that a useful computer, capable of outpacing classical approaches, is now years rather than decades away.