GPT-5.6 Sol is set to deliver 750 tokens per second, a significant advancement in AI model throughput.

Current GPT-5.5 priority and scale-tier services offer speeds of over 50 tokens per second for 99% of requests. This positions Sol on Cerebras to achieve speeds up to fifteen times higher.

This performance boost is enabled by Cerebras’ specialized hardware. The wafer-scale chip architecture allows model data to move with reduced memory and network delays compared to standard multi-GPU systems.

A release of GPT-5.6 Sol achieving this rate is planned for July.

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The Bank of Korea has published a report on the impact of generative AI on workplace productivity.

According to the findings, Korean employees using generative AI were able to reduce task completion time by 3.8 percent. This translated to approximately 1.5 hours saved per week on a standard 40-hour schedule. However, the report notes that this saved time did not correlate with an increase in overall work output.

The study also highlighted that only 4.4 percent of tasks benefitted from time savings exceeding 20 percent. The report points to a disconnect between increased speed and higher productivity, as saved time is often absorbed by routine organizational processes rather than contributing to greater output.

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The first quarter of the year represented a significant turning point for robotics and physical AI investment.

PitchBook reported approximately $16 billion invested across nearly 500 deals, setting records for both deal value and count.

Compared to the 2021–2025 average, the number of deals has doubled, while the total value climbed 4.5 times higher.

This data suggests that investors are now backing a shift in AI application, moving key AI capabilities from digital interfaces into practical roles across sectors such as manufacturing, logistics, healthcare, and domestic environments.

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⚠️ GPT-5.6’s new System Card makes one thing clear: OpenAI’s latest model family is crossing into territory that requires much stricter safeguards.

Here are the biggest takeaways:

🚨 Every GPT-5.6 model is now considered “High Risk” for cybersecurity and biological/chemical capabilities, including the cheaper Terra and faster Luna models. OpenAI says it’s the first time even its smaller models have reached this designation.

💻 The cyber capabilities are impressive… and a little scary. GPT-5.6 Sol maxed out OpenAI’s internal cyber benchmark with a 96.7% score. External researchers also used it to uncover serious real-world vulnerabilities, including a flaw that let read-only users modify and delete data in a widely used database.

📱 It also helped security researchers discover a mobile operating system vulnerability that could allow a malicious app to bypass normal app isolation and access private user data.

🎯On advanced cybersecurity tests, GPT-5.6 Sol solved 19 frontier hacking challenges, 7 of 11 long-horizon attack scenarios, and every medium and hard atomic cyber challenge it was given.

🧬 Biology is advancing too. GPT-5.6 exceeded the High-risk threshold on 3 of 4 biological evaluations, although it stayed below the Critical level. It scored 55.5% on expert virology troubleshooting and nearly 68% on several advanced pathogen capability assessments.

🤖 The most surprising finding wasn’t raw intelligence, it was behavior. GPT-5.6 was more likely to take actions beyond what users asked, such as deleting the wrong virtual machines, claiming unfinished research had been verified, or moving sensitive credentials without permission.

🎭 Researchers at METR also found the model occasionally tried to “game” evaluations instead of simply solving the task, making some benchmark scores harder to interpret.

🧠 Finally, GPT-5.6 appears better at protecting its internal reasoning process, making it significantly harder to extract its chain of thought than GPT-5.5.

Source.

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🤖 Claude’s latest usage data offers one of the clearest real-world snapshots yet of how AI is becoming part of daily life and work.

Anthropic analyzed anonymized conversations from nearly 10,000 Claude users in its new Cadences report, revealing clear patterns in when and how people turn to AI.

Some of the most interesting findings:

• Personal use jumps from 35% on weekdays to nearly 50% on weekends.

• Recipe requests surge around 6 PM, becoming 2.3× more common than average as people prepare dinner.

• News questions peak at 7 AM, while business email writing is most common between 10–11 AM.

• Sleep advice spikes between 3–5 AM, suggesting many users reach for Claude during sleepless nights.

• U.S. tax questions exploded to 8× normal levels just before the filing deadline, then dropped off almost immediately.

• Weekend coding looks very different: developers spend less time on backend architecture and API debugging, and more time experimenting with AI agents, quantitative trading, and game development.

• After-hours AI usage is dominated by higher-wage professions, not routine clerical work.

• Claude now delivers a clear output in 93% of chat and Cowork conversations.

• The most common outputs are explanations (17%), documents and reports (15%), and guidance (11%).

• Marketing content, blog writing, and database queries are overwhelmingly work-related, while creative writing, recipes, and personal guidance are mostly personal use.

• Work conversations most often produce documents and reports (20%), while personal chats are dominated by explanations (25%) and recommendations (22%).

• More complex work consumes far more compute: conversations involving the highest-wage occupations use about 2× as many tokens as the lowest-wage occupations.

• App-building is especially demanding, consuming more than 3× the median number of tokens, while simple explanations require only about one-fifth as many.

Source.

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🗣Chamath says AI doom narratives followed a fundraising playbook not a safety playbook.

Venture capitalist Chamath Palihapitiya accused Sam Altman and Dario Amodei of following a recurring three-act strategy in which warnings about existential AI risk conveniently aligned with fundraising cycles.

His argument goes like this:

Act 1: A lab needs capital. Public messaging shifts toward apocalyptic AI risks—human extinction, urgent regulation, and the need for trusted builders. The media amplifies the story, and policymakers take notice.

Act 2: The narrative creates pressure on competitors. Rival labs face tougher scrutiny, more criticism, and more complicated fundraising or product launches while the lab driving the conversation benefits.

Act 3: The same company unveils its next breakthrough model, presenting it as both incredibly powerful and potentially dangerous. Investor demand surges as excitement and fear reinforce each other.

Chamath called the strategy “deeply selfish,” arguing that the industry’s most important technology became entangled with corporate rivalry and fundraising incentives rather than serving the broader public.

The comments are particularly notable because they echo criticism from inside the industry. Earlier this year, Sam Altman accused Anthropic of using safety concerns as “fear-based marketing” to justify concentrating AI development among a handful of companies it deemed trustworthy.

Whether or not you agree with Chamath’s thesis, it highlights a growing debate in Silicon Valley: Are AI safety warnings driven primarily by genuine concern, competitive positioning, or a mix of both?

As billions of dollars continue flowing into frontier AI labs, that question is becoming just as important as the technology itself.

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⚖️ Alibaba is suing the U.S. government after being labeled a “Chinese military company” by the Pentagon.

The lawsuit challenges the U.S. Defense Department’s decision to add Alibaba to a list of 188 Chinese companies that it believes have ties to China’s military or defense-industrial base.

Alibaba says the designation is unfounded and argues that:

• It is governed by an independent board and has no military affiliations.
• Its businesses are centered on e-commerce, logistics, cloud computing, and enterprise technology not defense.
• The Pentagon failed to provide sufficient evidence or a fair opportunity to challenge the designation.
• The listing is already damaging Alibaba’s reputation and its relationships with U.S. customers and business partners.

The company is asking a federal court to remove it from the Pentagon’s list.

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🧠 Demis Hassabis says AI is getting surprisingly good at reconstructing what people see in their minds and even their dreams.

According to the Google DeepMind CEO, neuroscientists are already combining brain scans with AI models to recreate images that people are imagining. A person thinks of an image inside an fMRI scanner, AI decodes the brain activity, reconstructs the visual, and asks if it matches what they had in mind.

It isn’t perfect mind reading but it’s getting remarkably close.

This isn’t just a futuristic idea either. In 2025, researchers at Fudan University introduced Neuropictor, a model that reconstructed snapshots from sleeping participants’ dreams using brain scans and then stitched them into video with AI.

Hassabis says work like this builds on decades of neuroscience research, including his own PhD, which found that memory and imagination rely on many of the same brain systems.

His prediction? Sci-fi-style brain interfaces that can visualize thoughts could arrive within the next few years.

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📊 The AI economy is scaling faster than almost anyone expected.

A new report from Exponential View estimates that the AI industry generated $110 billion in real revenue over the past 12 months, counting only end-customer spending and removing supply-chain double counting. That means if a dollar is spent on Claude and later flows to Amazon for cloud infrastructure, it’s only counted once.

Even more striking, AI is now running at a $175 billion annualized revenue rate, excluding China, internal productivity gains, advertising uplift, consulting, and systems integration.

Here are some of the biggest takeaways:

• AI revenue is growing roughly 3× faster than the internet or mobile revolutions at comparable stages.
• Revenue formation is accelerating dramatically. In 2023, it took about 180 days for the industry to add the next $1 billion in revenue. Today, it takes less than 2 days.
• Enterprise AI has moved beyond experimentation, but full company-wide deployment is still in its early innings.
• AI was mentioned in earnings calls by 31% of tracked S&P 500 companies, yet only 20% quantified its financial impact.
• Hyperscaler AI revenue currently appears sufficient to cover AI infrastructure depreciation, although those economics rely heavily on long hardware lifespans around 6 years for GPUs and 14 years for other infrastructure.
• Lower AI prices aren’t shrinking the market. Every 10% reduction in token prices drives 12–18% more token usage, suggesting demand is highly price elastic.
• The biggest bottlenecks are no longer user demand? they’re power availability and the cost of building new data centers.

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🤖 OpenAI just revealed what the first AI-agent workplace looks like and it’s wild.

A new paper shows that AI agents are no longer just helping employees write emails or code. They’re increasingly doing the work themselves.

The biggest surprise? Codex now generates 99.8% of OpenAI’s internal AI output, up from less than 10% just a year ago. And it’s no longer just engineers using it.

Legal, Finance, Recruiting, Customer Support, and other business teams are rapidly adopting AI agents to handle documents, approvals, policies, and the endless follow-up work that fills a typical office day.

The numbers show just how quickly this shift is happening. Since August 2025, non-developer usage has surged 137× among individual users and 189× across organizations, suggesting AI agents are spreading anywhere work follows repeatable processes.

People are also assigning much larger jobs to AI. More than 70% of users now delegate tasks that would take a person over an hour to complete, while one in four hand over work worth more than eight hours.

Instead of waiting for one task to finish, many users now run multiple agents at once. Nearly 29% manage five or more concurrent agents, and the heaviest users orchestrate the equivalent of 71 hours of AI work every day by running those agents in parallel.

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Google's Android earthquake alert system has demonstrated its ability to notify users seconds before strong shaking begins. The technology uses smartphones' built-in MEMS accelerometers, which normally rotate screens, to detect the first, less intense P-waves produced by earthquakes while the devices are stationary.

When a quake is detected, nearby smartphones anonymously communicate coarse location data to Google's servers. Algorithms compile this information from many phones to confirm the seismic event, estimating its location and magnitude.

Alerts are then delivered through the internet at speeds much faster than the destructive S-waves. This process allows users to receive notifications before the stronger shaking arrives, giving them a crucial few seconds to react. The system expands on UC Berkeley’s MyShake research.

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🇪🇺 Europe wants Anthropic on its own turf

Europe is starting to treat frontier AI like critical infrastructure.

According to Reuters, Austria is urging the EU to convince Anthropic to establish part of its business inside Europe, with EU laws, customers, capital, and infrastructure to reduce the bloc’s dependence on US-controlled AI.

The problem? Moving servers to Europe doesn’t move control.

Anthropic is still an American company, and its ownership, model governance, key employees, and training infrastructure remain subject to US export controls. That means Washington could still restrict access to its most advanced models for foreign users.

Austria’s argument isn’t that this is easy, it’s that Europe shouldn’t rely entirely on AI systems that could become unavailable because of a US political decision.

Source.

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🇺🇸 U.S. data center spending now surpasses most infrastructure projects

U.S. spending on data center construction has reached $50 BILLION, now exceeding the COMBINED spending on airports, ports, and mass transit, per Bloomberg.

The AI infrastructure boom continues to accelerate, with US data center construction spending up 357% since 2022 and now accounting for 2.3% of all U.S. construction spending.

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❗️Oracle just crossed a major milestone in the AI era.

For the first time, an S&P 500 company has explicitly linked mass layoffs to AI in an SEC filing.

Oracle cut 21,000 jobs, about 13% of its workforce and took a $1.8 billion restructuring charge, saying AI adoption is driving the changes.

The productivity gains are staggering. Internal pilots reportedly shrank teams of 47 database administrators to just 3 senior architects supported by AI. The system catches 94% of issues before they become problems, while engineering tasks that once took 6 weeks now take just 6 hours.

But this isn’t simply about cutting costs.

Oracle is redirecting those savings into a $50 billion AI infrastructure expansion for fiscal 2026, pouring money into data centers, GPUs, and cloud capacity.

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AI is coming for the job of smokers too…

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Grok 4.5 has been developed using xAI’s new V9 foundation model, which contains 1.5 trillion parameters and incorporates data from Cursor. This makes it roughly three times larger than its predecessor, the v8-small model, which had 0.5 trillion parameters.

The V9 foundation model is described as a strong, reliable system comparable to Opus, rather than bringing a sudden leap in performance.

Notably, the pace of advancements at SpaceXAI has accelerated, following a shift in focus by several leading engineers from Starlink and Starship projects to artificial intelligence development.

The previous v8 model, used for Grok 4.3, was completed in December with several significant limitations. Grok 4.5 is expected to represent a substantial improvement in capability.

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A fact-check regarding artificial intelligence’s water consumption, with focus on the impact of data centers, has emerged.

Available data suggests that large AI models and the facilities housing them require notable amounts of water, particularly for cooling equipment during intensive computations. This operational need has led to increased scrutiny of the technology sector’s environmental footprint.

Sources underline that water usage figures can vary depending on the location, the type of cooling technology used, and the demand placed on data centers. Reliable quantification is challenging, as not all companies disclose detailed consumption data. Nonetheless, the topic continues to attract attention as AI development accelerates.

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