<p>IBM shares plunged 25 per cent on Tuesday, marking the company's biggest one-day stock decline in 58 years after it released disappointing preliminary second-quarter results. The sharp fall erased between $67 billion and $70 billion in market value, according to Bloomberg estimates. Investors reacted after IBM reported that revenue for the quarter ending in June rose only 1 per cent to $17.2 billion, missing market expectations.</p><p>Chief Executive Arvind Krishna admitted the company had failed to respond quickly enough to changing market conditions.</p><p>"We faltered... and did not adapt and move quickly enough," Krishna wrote in a letter to investors.</p><h2><strong>AI spending is changing customer priorities</strong></h2><p>IBM said one of the biggest reasons for the weak quarter was a shift in customer spending. As companies race to build AI infrastructure, demand for servers, memory chips and storage has surged. With supply remaining tight, many businesses accelerated purchases before expected price increases.</p><p>According to IBM, this meant customers redirected capital spending away from its high-margin mainframe systems and related software.</p><p>The company's infrastructure revenue fell 7 per cent, while software revenue grew 5 per cent, but still failed to meet analysts' expectations.</p><p>At the same time, IBM's server and storage business outside mainframes grew 37 per cent, reflecting strong demand for AI-related hardware.</p><h2><strong>Cybersecurity becomes a bigger priority</strong></h2><p>IBM also said cybersecurity spending increased significantly during the quarter. Businesses shifted budgets towards protecting their systems as more powerful AI models increased concerns about sophisticated cyberattacks.</p><p>The company pointed to growing industry focus on AI-powered security risks, prompting organisations to prioritise cyber defence over some planned technology projects. The trend also benefited cybersecurity companies. Shares of CrowdStrike rose around 12 per cent, while Okta and Netskope gained roughly 11 per cent following IBM's update.</p><h2><strong>What does this mean for software companies?</strong></h2><p>IBM's results have renewed debate over how artificial intelligence could reshape the future of traditional software businesses. Many investors are questioning whether conventional Software-as-a-Service (SaaS) companies can maintain growth as AI increasingly performs tasks that previously required specialised software. However, not every part of IBM struggled. Its Red Hat business reported 11 per cent revenue growth, showing continued demand for hybrid cloud and open-source software.</p><p>IBM also announced Lightwell, a $5 billion initiative backed by major financial institutions including Bank of America, JPMorganChase and Goldman Sachs to strengthen security across open-source software. While IBM's results disappointed investors, they also underline a broader shift across the technology industry. As companies invest heavily in AI infrastructure and cybersecurity, spending priorities are changing rapidly, creating both winners and losers across the enterprise software market.</p>

IBM shares fell 25 per cent in their biggest one-day decline in nearly 60 years after weak quarterly results. The company said customers shifted spending towards AI infrastructure and cybersecurity.

IBM shares plunged 25 per cent on Tuesday, marking the company's biggest one-day stock decline in 58 years after it released disappointing preliminary second-quarter results. The sharp fall erased between $67 billion and $70 billion in market value, according to Bloomberg estimates. Investors reacted after IBM reported that revenue for the quarter ending in June rose only 1 per cent to $17.2 billion, missing market expectations. Chief Executive Arvind Krishna admitted the company had failed to respond quickly enough to changing market conditions. "We faltered... and did not adapt and move quickly enough," Krishna wrote in a letter to investors. AI spending is changing customer priorities IBM said one of the biggest reasons for the weak quarter was a shift in customer spending. As companies race to build AI infrastructure, demand for servers, memory chips and storage has surged. With supply remaining tight, many businesses accelerated purchases before expected price increases. According to IBM, this meant customers redirected capital spending away from its high-margin mainframe systems and related software. The company's infrastructure revenue fell 7 per cent, while software revenue grew 5 per cent, but still failed to meet analysts' expectations. At the same time, IBM's server and storage business outside mainframes grew 37 per cent, reflecting strong demand for AI-related hardware. Cybersecurity becomes a bigger priority IBM also said cybersecurity spending increased significantly during the quarter. Businesses shifted budgets towards protecting their systems as more powerful AI models increased concerns about sophisticated cyberattacks. The company pointed to growing industry focus on AI-powered security risks, prompting organisations to prioritise cyber defence over some planned technology projects. The trend also benefited cybersecurity companies. Shares of CrowdStrike rose around 12 per cent, while Okta and Netskope gained roughly 11 per cent following IBM's update. What does this mean for software companies? IBM's results have renewed debate over how artificial intelligence could reshape the future of traditional software businesses. Many investors are questioning whether conventional Software-as-a-Service (SaaS) companies can maintain growth as AI increasingly performs tasks that previously required specialised software. However, not every part of IBM struggled. Its Red Hat business reported 11 per cent revenue growth, showing continued demand for hybrid cloud and open-source software. IBM also announced Lightwell, a $5 billion initiative backed by major financial institutions including Bank of America, JPMorganChase and Goldman Sachs to strengthen security across open-source software. While IBM's results disappointed investors, they also underline a broader shift across the technology industry. As companies invest heavily in AI infrastructure and cybersecurity, spending priorities are changing rapidly, creating both winners and losers across the enterprise software market.

<p>IBM shares fell 25 per cent in their biggest one-day decline in nearly 60 years after weak quarterly results. The company said customers shifted spending towards AI infrastructure and cybersecurity, wiping out almost $70 billion in market value and raising new questions about traditional software firms.</p>

Biggest crash in nearly 60 years: IBM shares tumble 25% as AI spending reshapes enterprise technology

<p>Anthropic’s Mythos model has drawn global attention after reports suggested it could identify thousands of software vulnerabilities. The release led to concerns among governments, banks and technology firms about the possibility of a new phase of AI-driven cyberattacks.</p><p>However, cybersecurity experts say that the capabilities highlighted by Mythos are not entirely new. According to researchers, similar results can already be achieved using existing AI models, including those from Anthropic and OpenAI.&nbsp;</p><h2>What Mythos revealed about cybersecurity risks</h2><p>Mythos gained attention because of its ability to detect large numbers of vulnerabilities in software systems. Some of these issues had remained unnoticed for years, raising concerns about how widely such flaws may exist.</p><p>The model was released in a limited way to selected organisations, including major companies, to reduce the risk of misuse. This controlled rollout reflects the sensitivity of tools that can identify weaknesses in critical systems.&nbsp;</p><h2>Experts say similar capabilities already exist</h2><p>Despite the attention around Mythos, researchers say that the ability to detect vulnerabilities at scale has been possible for some time.</p><p>Cybersecurity firms have shown that by using a method known as orchestration, multiple AI models can work together to analyse code and find similar vulnerabilities. In tests, older models from both Anthropic and OpenAI were able to detect many of the same issues identified by Mythos.</p><p>Experts note that this suggests the main difference is not just the model itself, but how effectively tools are combined and used.&nbsp;</p><h2>What are ‘zero-day’ vulnerabilities</h2><p>One of the key concerns in cybersecurity is the detection of “zero-day” vulnerabilities. These are flaws in software that are unknown to developers and have not yet been fixed.</p><p>Such vulnerabilities can be exploited by attackers before patches are released, making them particularly risky. Researchers say current AI tools are already capable of identifying these weaknesses, which adds to concerns about how quickly they can be used.</p><p>&nbsp;</p><p>Growing concern among governments and companies</p><p>The release of Mythos has increased discussions around cybersecurity risks at the highest levels. Reports indicate that policymakers and industry leaders are considering stronger oversight of advanced AI systems.</p><p>At the same time, organisations are concerned about the balance between detecting vulnerabilities and fixing them. While AI can identify issues quickly, addressing them still requires time and human effort.</p><h2>Offence may have an early advantage</h2><p>Experts suggest that the current situation may favour attackers in the short term. While AI tools can help defenders identify risks, they also make it easier for malicious actors to discover and exploit vulnerabilities.</p><p>Cybersecurity professionals note that hackers already have the skills needed to use such techniques, and AI could reduce the time required to carry out attacks. This could lead to an increase in the number of attempts on systems that were previously not targeted.</p><h2>Challenges in fixing vulnerabilities</h2><p>Even before AI tools became widely available, organisations faced challenges in managing software vulnerabilities. Fixing issues often requires testing, updates and sometimes taking systems offline, which can be complex and time-consuming.</p><p>Experts say that while AI has increased the number of vulnerabilities being identified, tools to fix them automatically are still limited. This creates a gap between detection and resolution.</p><h2><strong>What this means for the future</strong></h2><p>The discussion around Mythos highlights a broader trend in cybersecurity. AI is making it easier to identify risks, but it is also raising new challenges in how those risks are managed.</p><p>Experts believe that collaboration between companies, governments and researchers will be important in addressing these issues. As AI tools continue to improve, the focus will likely shift towards building systems that can not only detect vulnerabilities but also help fix them efficiently.</p><p>For now, the key takeaway is that the cybersecurity risks linked to AI are not entirely new, but they are becoming more visible and more urgent to address.</p><p>&nbsp;</p>

Anthropic’s Mythos model has drawn global attention after reports suggested it could identify thousands of software vulnerabilities. The release led to concerns among governments, banks and technology firms about the possibility of a new phase of AI-driven cyberattacks.


Anthropic’s Mythos model has drawn global attention after reports suggested it could identify thousands of software vulnerabilities. The release led to concerns among governments, banks and technology firms about the possibility of a new phase of AI-driven cyberattacks. However, cybersecurity experts say that the capabilities highlighted by Mythos are not entirely new. According to researchers, similar results can already be achieved using existing AI models, including those from Anthropic and OpenAI. What Mythos revealed about cybersecurity risks Mythos gained attention because of its ability to detect large numbers of vulnerabilities in software systems. Some of these issues had remained unnoticed for years, raising concerns about how widely such flaws may exist. The model was released in a limited way to selected organisations, including major companies, to reduce the risk of misuse. This controlled rollout reflects the sensitivity of tools that can identify weaknesses in critical systems. Experts say similar capabilities already exist Despite the attention around Mythos, researchers say that the ability to detect vulnerabilities at scale has been possible for some time. Cybersecurity firms have shown that by using a method known as orchestration, multiple AI models can work together to analyse code and find similar vulnerabilities. In tests, older models from both Anthropic and OpenAI were able to detect many of the same issues identified by Mythos. Experts note that this suggests the main difference is not just the model itself, but how effectively tools are combined and used. What are ‘zero-day’ vulnerabilities One of the key concerns in cybersecurity is the detection of “zero-day” vulnerabilities. These are flaws in software that are unknown to developers and have not yet been fixed. Such vulnerabilities can be exploited by attackers before patches are released, making them particularly risky. Researchers say current AI tools are already capable of identifying these weaknesses, which adds to concerns about how quickly they can be used. Growing concern among governments and companies The release of Mythos has increased discussions around cybersecurity risks at the highest levels. Reports indicate that policymakers and industry leaders are considering stronger oversight of advanced AI systems. At the same time, organisations are concerned about the balance between detecting vulnerabilities and fixing them. While AI can identify issues quickly, addressing them still requires time and human effort. Offence may have an early advantage Experts suggest that the current situation may favour attackers in the short term. While AI tools can help defenders identify risks, they also make it easier for malicious actors to discover and exploit vulnerabilities. Cybersecurity professionals note that hackers already have the skills needed to use such techniques, and AI could reduce the time required to carry out attacks. This could lead to an increase in the number of attempts on systems that were previously not targeted. Challenges in fixing vulnerabilities Even before AI tools became widely available, organisations faced challenges in managing software vulnerabilities. Fixing issues often requires testing, updates and sometimes taking systems offline, which can be complex and time-consuming. Experts say that while AI has increased the number of vulnerabilities being identified, tools to fix them automatically are still limited. This creates a gap between detection and resolution. What this means for the future The discussion around Mythos highlights a broader trend in cybersecurity. AI is making it easier to identify risks, but it is also raising new challenges in how those risks are managed. Experts believe that collaboration between companies, governments and researchers will be important in addressing these issues. As AI tools continue to improve, the focus will likely shift towards building systems that can not only detect vulnerabilities but also help fix them efficiently. For now, the key takeaway is that the cybersecurity risks linked to AI are not entirely new, but they are becoming more visible and more urgent to address.

<p>Anthropic’s Mythos model has drawn global attention after reports suggested it could identify thousands of software vulnerabilities. The release led to concerns among governments, banks and technology firms about the possibility of a new phase of AI-driven cyberattacks.</p>

‘AI cyber risk’: Why existing AI models are already powerful

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