🔥 MindOn demonstrated Mind-0, a shared AI model designed to control robots with different body types.

In the demonstration, two Unitree G1 humanoid robots and two dual-arm robotic systems worked together on a logistics task.

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⚠️Trump Administration reportedly asked OpenAI to delay its next AI model

The Trump administration has reportedly asked OpenAI to slow the rollout of its next frontier AI model over national security concerns.

Instead of launching broadly, OpenAI is expected to begin with a small, invite-only preview, giving the U.S. government time to evaluate the model’s cybersecurity risks before wider access.

The concern? As AI becomes more powerful, officials worry it could also become a more capable tool for cyberattacks if released too quickly.

If true, this would mark one of the clearest examples yet of the U.S. government influencing when a major AI model reaches the public, not by banning it, but by asking for a phased rollout first.

Source.

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🤖 OpenAI may delay its IPO until 2027

OpenAI is reportedly leaning toward pushing its IPO to next year, despite earlier plans to go public as soon as Q3 or Q4 this year.

According to reports, Sam Altman urged advisers to find a path to a $1 trillion valuation. But advisers warned that public markets may not be ready, citing weaker demand for tech stocks and caution following SpaceX’s volatile post-IPO trading.

The numbers tell an interesting story:

• OpenAI generated roughly $13B in revenue in 2025
• It’s now bringing in about $2B every month
• The company aims to triple revenue this year
• But it’s still spending heavily on AI infrastructure, chips, talent, and marketing

Meanwhile, the competition isn’t standing still. ChatGPT’s growth has leveled off at around 900 million users, while Anthropic is gaining enterprise momentum with Claude Code, and Google Gemini continues to improve as a consumer AI product.

Source.

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Maybe AGI won’t be that soon…

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🤖OpenAI is previewing GPT-5.6 Sol with a very different release pattern

Trusted partners first, broader access later, and U.S. government coordination up front.

The new GPT-5.6 family includes Sol, Terra, and Luna. OpenAI says Sol is its strongest model yet, with a new max reasoning effort and an ultra mode that uses subagents for complex work.

The sensitive part is cyber. OpenAI says Sol improves long-horizon security tasks, but “does not cross the Cyber Critical threshold” under its Preparedness Framework.

Source.

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🤖 OpenAI has priced GPT-5.6 Sol, its flagship model, much closer to Claude Opus 4.8 than to Anthropic’s premium Mythos 5, signaling that it’s competing on both performance and cost.

API pricing (per 1M tokens):

• GPT-5.6 Sol: $5 input / $30 output
• Claude Opus 4.8: $5 / $25
• Claude Mythos 5: $10 / $50
• GPT-5.6 Terra: $2.50 / $15
• GPT-5.6 Luna: $1 / $6

OpenAI is also positioning its lineup aggressively:

Terra is described as delivering performance comparable to GPT-5.5 while costing 50% less. Luna is aimed at developers who need strong capabilities at the lowest price point.

The company is pairing lower prices with faster inference. OpenAI announced that GPT-5.6 Sol will run on Cerebras hardware, delivering speeds of up to 750 tokens per second starting in July, one of the fastest frontier-model deployments announced so far.

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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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