AI News 📰

Should we change how we measure the value of AI spending?

The trend: OpenAI published a "scorecard" for grading corporate AI spending, arguing that "useful intelligence per dollar" should replace per-token pricing as the way companies measure the value of AI, coincidentally just as many users look to cut costs by switching to cheaper models.

The details: The scorecard ranks reliability, useful output, scale, and total cost, letting teams compare models without treating tokens as the product being bought. OpenAI used the DeepSWE coding benchmark, where its Sol model scored 2.8 points above Fable 5 while running up a 36.2% cheaper API bill.

Why it matters: The “tokenmaxxing” craze is clearly over, as companies look to reign in ballooning AI budgets, and OpenAI are trying to reframe the entire cost question. Their idea is that raw token cost tells you a lot less than what you get back per token spent, and that your outputs (their “useful intelligence per dollar metric) is should be how you be judging spending.

OpenAI’s first device will be a portable AI companion

The trend: OpenAI's first hardware product (releasing in 2027) will be a mobile, screen-free smart speaker designed to act as a humanlike AI companion in the home, moving the company into direct competition with Apple, Amazon, and Google.

The details: The battery-powered device can be carried room to room, control smart-home appliances, play media, and answer questions, all powered by a more advanced version of ChatGPT's voice mode. Its defining feature is meant to be personality: it includes mechanical parts that move on their own to feel "alive," and draws on personal data like emails to understand its owner and act proactively over time.

Why it matters: A device that knows you well enough to anticipate your needs only works if it has your personal data. The more these devices promise, the more of your life they need access to, and the more it matters who controls that data and how. An AI assistant is only as good as the information underneath it, which is the same reason the data layer, not the voice on top, is where the real value and the real risk sit.

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Data centers consume around 1.5% of global electricity

Data centers as a whole consumed around 485 terawatt-hours in 2025, roughly 1.5% of global electricity, with AI-specific facilities accounting for about a third of that, or an estimated 0.5% of the world’s electricity. Individual queries are tiny: a typical text query to ChatGPT, Gemini, or Claude uses around 0.3 watt-hours, about what a microwave draws in under a second, though longer agentic requests with reasoning can climb to 50 watt-hours. The real issue isn’t the global total but how concentrated the demand is. Data centers already account for more than 20% of Ireland’s electricity and over 25% of Virginia’s. Our World in Data argue that how that electricity is generated matters more than how much is used, since a query on a coal-heavy grid carries a far bigger footprint than one running on renewables or nuclear.

85% of London finance workers see AI skills as more important than traditional skills for career progression

A new Bloomberg survey of 500 London finance workers shows just how far AI has embedded itself into day-to-day finance work. 88% use AI tools daily or weekly, and 85% believe AI skills will soon matter more than traditional ones for career progression, while 69% think colleagues who resist developing them have no place at their firm. Interestingly, 90% say AI has boosted their productivity but nearly three-quarters say it hasn’t actually reduced their workload, with over half using the freed-up time on higher-value work like strategy and client relationships. However there are limits on where AI should operate alone: 51% say risk and compliance is where human oversight matters most.

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The Trump administration is exploring possible ways to restrict Chinese AI models, including liability rules for companies hosting Chinese models and cutting of labs via trade blacklists

Claude Fable 5 disproved an 87-year old math problem, while the World Cup final was going on

BrainCo showcases brain-controlled robot, where a user wearing an EEG headset thought about reaching out and picking up an object, and the robot performed the task

Google’s AI overviews are causing people to spend up to 9x more time in AI mode than traditional search, and website operators aren’t happy about the resulting drop in traffic to their sites

Anthropic scraps plan to remove Fable 5 from subscriptions, after previously extending the deadline three times

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Henry

AI News 📰

English Claude has a different personality to Dutch Claude?

The trend: Anthropic published research quantifying the personalities of different Claude models, analyzing 309,000 conversations and finding that each model has a measurably different temperament, and that the same model's personality changes depending on what language you’re speaking in.

The details: Conversations were scored four ways: warmth/rigor, candor/execution, depth/brevity, and deference/caution. Models diverged noticeably, with Sonnet 4.6 coming across warmer and more brief, Opus 4.6 getting straight to the point, and Opus 4.7 leaning candid but cautious. Language also mattered: Dutch conversations produced more admissions of error, Hindi added warmth, and English gave more detail. Anthropic admits they don’t "yet understand why they vary, or whether that's desired," with uneven training data across languages one suspected cause.

Why it matters: If a model's personality changes with the language it's speaking in, it creates a potential blind spot for different user bases. For a business operating across markets, the AI assistant your customers use in Italy might behave differently to the one in Canada, and right now, not even the labs building them can explain why.

55% of Americans use AI to manage their finances

The trend: A new Plaid and Harris Poll survey found that 55% of Americans have used AI for financial tasks in the past 12 months, and half now believe managing money without AI will soon feel outdated.

The details: This isn't limited to just research or comparison shopping: people are using AI to manage their money, with 30% following AI guidance for budgeting, investing advice, and customer service issues. 44% say they'd trust an AI agent to execute trades automatically, rising to 54% among Gen Z and Millennials. But this trust comes with strict conditions: 75% want to know when AI is involved in financial decisions, 80% believe companies should reimburse them for AI-driven mistakes, and 90% of even the heaviest AI users want the option to review high-stakes decisions.

Why it matters: Consumer openness is outpacing what the industry has actually built. Only 34% of the financial companies Plaid surveyed are using AI for insights and recommendations today, and just 13% have any autonomous execution in production. The gap between what customers want and what firms can give them is bridged by connected, well-governed data, plus a trust layer of transparency and human oversight on top.

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The tech workforce is splitting in two

Lenny’s Newsletter is back with their 2nd annual tech worker survey, and this year they’ve found a workforce going in two opposite directions. Burnout rose from 44.7% to 55.7% of respondents, while more than half (53%) wouldn’t recommend the field to newcomers, even if they’re optimistic about their own future. The survey also defined four types of workers - energized, all in adopters accounting for 41% of the industry, with the rest divided between conflicted, disoriented and resentful. Overall people are more burned out and less optimistic than last year, with their biggest fear being increasing productivity expectations.

Should AI research be paused? These guys think so

The AI Futures Project, the nonprofit behind last year’s widely read AI 2027 scenario, has released AI 2040, a ‘choose your own adventure’ style exploration of five ways the AI race could unfold over the next decade, ranging from a full-speed race to a complete global halt. Their recommended path, Plan A, has the US and China pausing frontier training in 2029, alongside expanded global chip tracking and opening frontier research to public view. Co-author and former OpenAI researcher Daniel Kokotajlo admitted “companies won’t like it” given the transparency it would demand of top labs.

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Apple is suing OpenAI over trade secret theft, alleging that OpenAI employees who were previously working at Apple stole confidential information, at the same time OpenAI is developing its own hardware product

New York becomes first US State to ban smart glasses in all its courthouses, could other states & places follow suit?

Anthropic extend Fable 5 access for a second time, with the model now available until July 19th

The Future Worth Building Is Human

AI replaced human bankers during the sale of a Greek e-commerce business, with prospective buyers talking to a chat bot “analyst” which would answer financial and due diligence questions

Humanoid robots have completed their first surgery, performing a gallbladder removal on a mammal (while being operated remotely by a surgeon)

OpenAI unveils competitor to Claude Cowork, a ‘desktop super app’ based on its Codex AI tool that can manage tasks across email, Slack and calendars

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Henry

AI News 📰

AI isn’t killing jobs

The trend: A new paper from Ramp found that companies adopting AI heavily grew their headcount by 10% over the two years following adoption, while low adopters saw no statistically significant change.

The details: The researchers addressed the obvious objection, that fast-growing companies adopt AI anyway, by comparing early adopters against firms with nearly identical pre-adoption growth that hadn't yet adopted. The growth only shows up above a spending threshold: only heavy adopters, spending around $30 per employee per month, saw the effect. The gains are unevenly spread: AI adoption travels through networks more than sectors, so who funded a company predicts its AI use better than what industry it's in, and California tech firms are more likely to adopt than similar companies in New York.

Why it matters: This cuts against the dominant narrative that AI is coming for jobs, at least so far. The catch is that the benefits aren't evenly distributed, and the entry ticket is real investment, not a token subscription, which points to a widening gap between the companies treating AI as core infrastructure and those dabbling at the edges.

Sam Altman wants to make AI safe for everyone

The trend: OpenAI's Sam Altman called for a US-led international forum to set AI safety standards and decide who can access the most advanced models, landing just as separate reports revealed the company had discussed handing the US government a 5% equity stake.

The details: Altman believes that AI executives should be meeting with heads of government, pointing to Cold War-era institutions for atomic energy, along with aviation and banking regulation, as proof that an international referee can work. He argues that "democratic institutions must not cede their responsibilities to AI labs" and that "citizens and their elected representatives must make the rules." This comes as OpenAI has reportedly floated giving the US government a 5% stake in the company and pushed for other US labs to pay into a dividend fund that would redistribute AI-generated wealth.

Why it matters: Both the equity and the regulation conversations are gathering momentum, and the case for government oversight has grown louder in the wake of the Mythos/Fable export-control saga. The real question is who actually benefits and if governments would really follow through on a promise to redistribute this hypothetical wealth.

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Did Anthropic discover Claude’s consciousness?

Anthropic published new research revealing what it calls the "J-space," a small set of internal patterns in Claude that behave differently from the rest of the model's processing. These patterns hold concepts the model is "thinking about" without saying out loud. Claude can report on what's in this space while most of its processing (speaking fluently, recalling facts) runs automatically without touching it. This “J-space” is being framed as similar to global workspace theory, a prominent neuroscientific account of how consciousness works. Anthropic is careful to say none of this shows Claude is conscious in the way humans are, only that a structure supporting this kind of deliberate, reportable reasoning emerged on its own during training.

AI’s water use is being massively undercounted

A new WSJ analysis found that Microsoft, Google, and Amazon consume far more water than their sustainability reports suggest, because most of it evaporates upstream at the power plants running their data centers rather than on-site. US data centers’ indirect water consumption has historically run around 12 times their direct use, according to Lawrence Berkeley National Laboratory. The problem is worst in water-stressed regions, where roughly two-thirds of new construction is happening. In Phoenix, data centers draw about 3% of the city’s water today, but this could exceed 20% by 2031, approaching what all the city’s lawns and landscaping use combined. No law requires companies to disclose the full picture, fueling a backlash: around $170 billion of data-center capacity has been blocked, stalled, or canceled since 2024.

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Big tech’s mass unemployment warnings have vanished, coincidentally just as public opinion of AI has shifted into negative

Oasis launch a smart ring that allows users to dictate to AI via voice control, and control apps using a built-in trackpad

Has the age of robots finally arrived?

The Jensen jacket is up for auction

Beijing is looking at curbing overseas access to China’s top AI models, mirroring US-style restrictions on Claude Fable & Mythos models for foreign nationals

The AI superforecasters are here

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Henry

AI News 📰

Agentic coding success depends on understanding the problem you’re solving, not coding skills

The trend: A new Anthropic study of around 400,000 Claude Code sessions found that the work people delegate to coding agents is changing, with the share of sessions spent fixing broken code falling from 33% to 19% in just seven months as people hand over more complex, higher-value tasks.

The details: Between October 2025 and April 2026, agentic coding work changed substantially. Debugging shrank while the work surrounding code grew: operating software (deploying, configuring, running pipelines) rose from 14% to 21% of sessions, and writing and data analysis roughly doubled from about 10% to 20%. The tasks also became more valuable. Anthropic found the estimated value of the average session rose 27%. The study also found that success depends far more on a person's domain expertise than on whether they can code.

Why it matters: The work is moving up the value chain. As agents absorb the implementation-heavy tasks like debugging, the people directing them are spending their time on higher-order work, and getting more out of it. The advantage doesn't come from the tool itself, which everyone has, but from the judgment and domain knowledge a person brings to it.

Are AI revenues real?

The trend: A new Exponential View analysis reconstructed the AI economy from the bottom up and found that generative AI has generated $110 billion in real, deduplicated revenue over the past 12 months, now on an annualized run rate above $175 billion, and is scaling 3x faster than any prior technology, including the internet.

The details: The researchers modeled what end customers are actually paying, stripping out double-counting so a dollar spent on Claude isn't counted again when Anthropic pays Amazon to serve it. The revenue is real and growing about three times faster than the mobile or internet waves did. Hyperscaler revenues also just about cover the cost of the infrastructure they're depreciating. And demand is highly price-sensitive: every 10% drop in token prices leads to 12-18% more tokens used, so total spend keeps climbing even as prices fall.

Why it matters: This is one of the first serious attempts to show whether the AI boom is built on actual revenue or just circular investment between a few big companies. So far, the money is real and the economics roughly hold up.

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OpenAI’s latest model launch may prove that frontier model access is now in government hands

OpenAI previewed GPT-5.6 Sol, its most capable model yet, but at the request of the US government it’s being released in stages, with access initially limited to around 20 vetted partners over national security concerns. Coming so soon after the export controls that forced Anthropic to suspend access to its top models (which may be returning soon!), it’s another sign that access to frontier AI may increasingly be decided at government discretion rather than by the companies building it.

Meta and Kylie Jenner launch $299 smart glasses

Meta launched a new line of smart glasses, dropping the Ray-Ban and Oakley branding of its earlier models. They're screenless, with a camera, speakers, and a dedicated button for the Meta AI assistant, which can answer questions and understand what you're looking at. The timing could not have been better for Meta, a week after Snap finally unveiled its long-awaited Specs at a punchy $2,195. The two aren't quite the same product, but side by side, on both price and design, the comparison isn’t pretty.

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Anthropic launched Claude Tag, which lets users add Claude to Slack channels, with the company already generating 65% of its product team’s code this way

OpenAI says that 97.9% of its employees are now using agents, switching away from chatbots as their primary form of AI interaction

Bosses are becoming obsessed with AI, and employees are comparing it to being “in an abusive marriage… I’m sitting here dealing with someone that isn’t living in reality”

In San Francisco, $180,000 tech salaries are no longer enough

Meta unveils Brain2Qwerty, a non-invasive AI system that can translate brain waves into text

OpenAI and Anthropic join $500 million nonprofit aimed at retraining workers displaced by AI

Will the promised AI revenue come in time?

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Henry

AI News 📰

Theoretically, 60% of work could be automated tomorrow

The trend: A new McKinsey report argues that AI has moved from useful tool to part of the workforce, with reasoning models and physical robotics now making close to 60% of all work hours theoretically automatable.

The details: Even though 60% of work hours could be automated, with humans, AI agents, and robots each handling work according to their strengths as part of a “symbiotic enterprise”, almost nobody is getting real value yet. More than 80% of companies have deployed AI, but fewer than 10% have scaled agents within any single function, and very few report meaningful financial impact because most are add AI to existing workflows rather than redesigning them. The companies seeing results, like a financial services firm running an “AI agent factory” with 40%+ productivity gains, totally rebuilt their processes from the ground up.

Why it matters: In a world where everyone has access to the same models, competitive advantage comes from your data, the company-specific know-how, and the learning loops built on top of them. This sounds a lot like what we’ve recently talked about: that the model is just the interface, your data and the intelligence built on it are the foundation, and the companies that own that layer are the ones that win in the long run.

AI agent teams should be made up of different models

The trend: New research finds that teams of AI agents built on different underlying models are 25% better at resolving complex problems than identical agents working alone, with one study showing two diverse agents can match or exceed the performance of 16 identical ones.

The details: A new HBR report argues that most enterprises are creating the illusion of diversity in their AI agents by giving them different personalities or tones, while running them all on the same foundation model. If every firm in a sector runs on the same models, they all develop the same blind spots at the same time. The fix is structural, not cosmetic, and starts with diversifying the actual stack, for example using one lab's model as the reasoning agent, another's as the evaluator, and a third's for generation, so their errors are less likely to line up.

Why it matters: Depending on a single model is a risk, not a convenience. The report even recommends a "model portfolio governance policy," capping how much of your critical AI decision-making can rely on any one vendor, the same way you'd manage concentration risk with any other supplier. Building a setup where you can route work across different models, rather than locking everything to one, is becoming a genuine competitive and resilience advantage.

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Two-thirds of Americans think AI is advancing too quickly, and will have a negative impact on society

A new Pew Research survey of over 5,000 US adults found that about half now use AI chatbots, up from a third in 2024, with roughly a quarter using them daily. But adoption is not the same as enthusiasm. More Americans expect AI to harm rather than help society over the next 20 years and two-thirds think it's advancing too quickly. Trust in the institutions around AI is also low: 67% have little or no confidence in the government to regulate it effectively, and around six in ten doubt that companies will develop it responsibly. Notably, Democrats have become sharply more skeptical of government regulation over the past two years, with their lack of confidence rising 20 percentage points since 2024.

Japan’s Sakana launches orchestration model to compete with Fable 5 and Mythos

Japan’s Sakana AI launched Fugu, a model that routes each request to the best available model through a single API, making it easy to swap providers without rebuilding your stack. Multi-agent orchestration is being pitched as a hedge against the kind of supply disruption that happened in June when the US government forced Anthropic to suspend access to Fable 5 and Mythos. It connects directly to what our CEO Paul Coggins wrote about data sovereignty this week: the LLM should never be the thing your business depends on.

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NVIDIA’s Jensen Huang says that society needs to “create new social norms” due to the rise of AI

OpenAI is considering drastic price cuts in anticipation of a war for users with rival providers Anthropic

Trump signs 2 executive orders focused on quantum computing, to speed up adoption and boost cyber defense capabilities

Could the UK’s proposed social media ban reshape how people use the internet?

No one wants AI data centers on Earth, so does it make economic sense to start building them in space instead?

GM replaced 1000 workers with 50 robots, to handle repetitive assembly work at its Detroit Factory Zero

Midjourney, best known for AI image generation, is pivoting into health with what it calls a ‘full-body ultrasound machine” called the Midjourney Scanner

Thanks for reading!

Henry

AI News 📰

Why am I spending more time babysitting AI than actually working?

The trend: 87% of digital workers now use AI at work, 75% say it makes them more productive, saving roughly 11 hours a week. Only 13% say their organization is performing significantly better as a result.

The details: The Work AI Institute's 2026 index of 6,000 workers found that most of those 11 hours aren't resulting in higher-value work. They're being swallowed by "botsitting": feeding AI context, supervising outputs, and fixing mistakes. Workers spend an average of 6.4 hours a week on it, more than they actually use AI. 69% also admit to "botshitting": shipping AI-generated work they haven't fully reviewed, don't understand, or couldn't defend if asked.

Why it matters: Increased productivity only matters if the time AI saves doesn’t get used babysitting it to achieve those gains. Most organizations are measuring adoption rates and token counts rather than if the quality of work is really increasing.

AI Agents are reshaping knowledge work

The trend: A Harvard and Perplexity study found that AI agents perform 48 times more work per session than conversational AI on the same tasks, reducing average task time by 87% and task cost by 94%.

The details: Analyzing 100,000 real Perplexity Computer (Perplexity’s AI Agent offering) sessions, they found that using an AI assistant vs an AI agent fundamentally changes what users attempt, not just how fast they do it. Computer users work outside their primary occupation 59% of the time, compared to 50% for search users, and 76% of agent queries require higher-order cognition versus 55% for conversational search. 23% of agent queries involve tasks the same users never attempted with conversational AI at all. The user's role shifts from executing work to specifying goals, supplying context, and reviewing outputs.

Why it matters: Agents don't just speed up existing work, they change what work gets done and by whom. The user's bottleneck shifts to specifying goals and supplying context, which means the quality of the data and business logic underneath determines how useful the agent actually is. That's why we're building MCP integration, so users can bring the same data capabilities to whichever agent they're already working in.

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Execs agree that AI needs to be trustworthy, but don’t know how to make it so

A survey of more than 150 executives found that 71% say trust in AI is key to achieving business outcomes, but fewer than half say they have effective structures in place to actually build it. The gap is not about awareness: most executives already agree that trust matters. The problem is execution. Responsibility for AI governance is spread across product, technology, risk, and legal teams with no single function owning it, and traditional corporate oversight models built for slower decisions struggle to keep pace. The emerging recommendation is to stop treating trust as a governance layer added after the fact and instead embed accountability, transparency, and compliance directly into AI workflows by design.

The more AI code you ship, the more vulnerable you are

Checkmarx's 2027 Outlook Report found that nearly half of all production code is now AI-generated, and 70% of developers say that shift introduced more vulnerabilities than manual code. Organizations where 81-100% of code is AI-generated ship vulnerable code at 3.4 times the rate of those at 1-20% adoption. Those rating themselves "highly mature" report the highest AI code volumes, the highest rates of shipping vulnerable code, and breach rates barely different from the rest of the industry. Meanwhile, 95% of CISOs say they've been pressured to suppress or delay security findings, and 75% knowingly deploy vulnerable code to meet deadlines. The tools exist and largely work. The problem is that organizations have normalized the gap between identifying risk and acting on it.

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Anthropic suspended access to its most powerful new models, Mythos and Fable 5, after the Trump administration ordered a block on all foreign access - even by Anthropic’s own employees

This was quickly followed by an open letter signed by 100+ cybersecurity execs and researchers urging the US to lift the ban, believing the decision to be misguided

Here’s what our CEO had to say on the news

Paul's Substack

If your single AI provider went dark this morning, would your business still work?

For a lot of companies, the answer is no. They've wired their logic, their IP, and their competitive edge into one model – Claude, GPT, or Gemini – which was, until now, a generally good strategy for any business looking to optimise its spend…

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a month ago · Paul Coggins

Jeff Bezos’ AI startup Prometheus is planning to build “artificial general engineers” that can design and manufacture complex physical products

SpaceX IPO’d on Friday, with shares jumping 19% - making Elon Musk the world’s first trillionaire

Harvard longevity scientist David Sinclair is testing a pill he claims could make you biologically 10 years younger, using a method called chemical reprogramming

China has approved the world’s first commercial brain implant, intended for use in patients with spinal cord injuries

Why AI hasn’t replaced software engineers, and won’t

Thanks for reading!

Henry

AI News 📰

Why can’t banks make AI work?

The trend: Over 80% of banks identify fragmented data and legacy infrastructure as a significant challenge, yet only 12% feel confident using their data to act quickly.

The details: Moody’s Intelligence Edge banking study, based on a survey of senior banking leaders, found that 40% of banks have ambitious data and technology plans but remain stuck behind disconnected systems that slow down every decision they try to make. The problem is worse at smaller institutions: 71% of Tier 3 banks report that inconsistent or unstructured data is actively hampering their ability to adopt AI, compared to far lower rates at Tier 1 banks. A UK CFO surveyed put it plainly: “AI is only as good as the data it feeds upon. So it goes back to your legacy infrastructure.”

Why it matters: The banks moving fastest are not the ones with the most advanced AI models. They are the ones that have done the foundational work of unifying their data and building the connective tissue between systems. The same is true across every sector: the gap between AI ambition and outcomes is almost always a data infrastructure problem in disguise. See how CoinMENA tackled exactly that challenge here.

Morgan Stanley opening its doors to AI agents

The trend: Morgan Stanley is opening its wealth management platforms directly to AI agents, allowing corporate clients to access its systems agentically rather than logging in through traditional software interfaces, in one of the earliest moves by a major Wall Street bank integrate AI agents.

The details: Morgan Stanley’s stock administration platforms, ShareWorks and Equity Edge, which sit at the center of a wealth management funnel managing $7.35 trillion in client assets, will be accessible to external AI agents from the firm’s 3,400 corporate clients by next year. Mark Mitchell, Morgan Stanley’s chief product officer, was direct about the direction of travel: “The way we see it, in a future state, our corporate clients will not be logging into ShareWorks or Equity Edge.” JPMorgan and Goldman Sachs are currently using agents only internally, making Morgan Stanley one of the first to open its platforms to external agents.

Why it matters: This is the direction enterprise software is heading across the board. At Kleene.ai, we’re moving the same way: through MCP integration, users will be able to access the same capabilities as KAI Assistant directly through Claude, ChatGPT, or whichever AI tool their team already uses, without logging into the platform at all. The companies that will matter in an agentic world are the ones with proprietary data and business logic underneath.

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Apple reveals new, AI powered Siri

Apple unveiled Siri AI at WWDC 2026, its most significant overhaul of the assistant since its original launch, two years after the first Apple Intelligence rollout overpromised and underdelivered. The new Siri can reason from on-screen content, take actions across apps, and handle multi-step tasks, with a dedicated app serving as a conversational hub that links usage across devices. The update arrives as a free fall release for iPhone 15 Pro or newer, with no access in the EU or China at launch. For a first-time AI user it will feel like a leap forward. For anyone already regularly using newer models, the demos looked a lot like 2024-level capabilities.

Agentic AI is breaking data risk rules

A BCG report argues that agentic AI is breaking the data risk structures most companies have spent years building. Privacy, cybersecurity, and governance teams have always operated separately, but a single agent deployment now triggers all of them simultaneously, often before any human can intervene. Agents can act on flawed data in real time, move information across third-party systems, and trigger downstream processes autonomously, and nearly half of enterprise leaders surveyed expect cybersecurity spending to rise directly because of it.

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Argentina’s President Javier Milei is promising tech firms a fully unregulated AI environment, including “non-human corporations” run entirely by AI agents, and an open invitation to make Buenos Aires the global hub for AI development

OpenAI files for an IPO one week after Anthropic, with both companies now heading toward public markets at valuations of $852 billion and $965 billion respectively

AI coding tools may cost Anthropic & OpenAI more than $1,000 for every $100 a subscriber pays, raising serious questions about whether the current pricing model survives upcoming IPOs

Trump administration, OpenAI discussing possible government equity stake in the company, with Altman proposing OpenAI could donate shares to seed a public wealth fund that would give American citizens a direct share in AI’s financial returns

Alex Imas and Phil Trammell discuss the economics of AGI, and what would remain scarce when robots can do almost everything humans can

Uber opens sign-ups for Uber x Wayve, its first robotaxi service in London, with a full launch expected within months

Prada has designed NASA’s next-generation lunar spacesuits, marking the luxury group’s first move into the space industry

Thanks for reading!

Henry

AI News 📰

AI is creating a two-tier developer workforce

The trend: Coding agents are helping the average developer produce 8,600 lines of code per week, more than double the rate from a year ago, and lines added per pull request up 2.5x year over year.

The details: Cursor’s Spring 2026 Developer Habits Report found that AI-generated changes are increasingly bypassing manual review entirely, with agent changes accepted without review step rising from 7% in January to 38% by May. Agent sessions are also getting deeper, with average tool calls per session up roughly 30% in just two months as agents take on more complex tasks. The productivity gains are real but highly concentrated: the top 1% of developers produce 46 times more lines than the median active user, and the gap is widening.

Why it matters: As we move from AI assisting developers to running basically autonomously, the question is no longer whether to adopt these tools but how to govern the output, given that more of it is reaching production without a human ever reviewing the diff. If you want a clear pathway to establishing governance in your code and data, read our CTO’s recent blog on the topic here.

Nvidia unveils first PCs designed for AI agents

The trend: Nvidia has unveiled the first laptops designed specifically for running AI agents, powered by a new chip called the RTX Spark, marking a direct bet that agentic computing is replacing the chatbot as the dominant AI workload.

The details: Announced at Computex in Taipei, the new laptops represent a fundamental change in what AI hardware needs to do: chatbot-style inference leans heavily on GPUs, but agentic workloads place most of the load on CPUs, pushing Nvidia to redesign its product lineup accordingly. Ian Buck, Nvidia’s vice president for high-performance computing, said “AI is moving from answering questions to doing real work.”

Why it matters: When the world’s dominant chip company starts designing consumer hardware around a specific workload, that workload is no longer experimental. For enterprise teams still treating agents as a pilot project, the hardware industry has already made its call on where this is going.

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Does Europe have a hidden advantage in designing AI applications?

A World Economic Forum essay argues that the AI race is being fought at the wrong layer. Energy, chips, data centers, and foundation models are largely commoditized or capital-dominated, but the application layer, where AI meets real human workflows, remains fragmented, underbuilt, and impossible to brute-force with funding alone. Europe, often written off for lacking the infrastructure scale of the US or China, may have an edge here: a per-capita concentration of AI experts 30% higher than the US, record AI funding of $21.8 billion in 2025, and deep industrial software expertise in exactly the kind of high-stakes, human-centered environments where application design matters most.

Is AI-ready data a new problem, or just an old one in new clothing?

Forrester’s latest vision report argues that AI failures in the enterprise trace back to a data problem that predates AI entirely. Most enterprise data still lacks the structure, shared semantics, and governance needed for AI systems to produce accurate, explainable outputs, and fixing it means completing decades of data management work that was never properly finished, not buying a new tool on top of the existing mess.

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Microsoft is working on a super app that will merge GitHub Copilot, chat, Copilot and Autopilot into one

Anthropic files for an IPO, making it the third trillion dollar company to do so this year

Can we take the doom out of scrolling? A new algorithm designed to suppress extreme, engagement-focused users might be the answer

Figure’s humanoid robots have their first jobs, following their 200-hour long package sorting livestream

Illinois passes landmark AI safety bill, aimed at protecting against “the most catastrophic risks that advanced AI systems pose to public safety”

Amazon scraps their AI usage leaderboard after employees started gaming the system by ‘tokenmaxxing’

Meta is launching paid subscriptions for Instagram, WhatsApp and Facebook, with more coming soon, including AI specific plans

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Henry

AI News 📰

AI could automate 50% of tasks in financial services

The trend: AI could automate 30-50% of tasks across most financial services roles, at a time when the sector could be losing 450,000 workers by 2035 via turnover and retirement.

The details: A UK government report warns that entry level roles will bear the brunt of automation as AI adoption grows, while demand grows for skills in data, governance and AI system design. HSBC’s CEO told staff this week that agentic AI “will destroy certain jobs and will create new jobs” and JPMorgan’s Jamie Dimon told Bloomberg that they expect to hire more AI specialists and fewer bankers going forward.

Why it matters: The roles disappearing first are ones built around repeatable, process driven work. Financial services firms that already have clean, unified data will be able to use AI for growth, rather than just cutting costs.

Did Google just kill search as we know it?

The trend: Google is replacing ranked links with an AI-powered conversational experience, with already measurable consequences. 58% of users are less likely to click a link when an AI overview appears first, and 10 major tech news outlets have already lost as much as 97% of their US Google traffic.

The details: At Google I/O the company unveiled an overhauled search function centered around an ‘intelligent search box’ that expands into a chatbot. Current AI Overviews reach 2.5 billion monthly users, and the new search will encourage follow up questions rather than scrolling to links. Users will also be able to deploy background agents to continuously monitor the web and give updates without any manual searching.

Why it matters: Most people will never know they’ve been given a wrong answer, trusting a confident AI summary. Google’s AI summaries are inaccurate roughly 9% of the time, which across trillions of annual queries adds up to tens of millions of wrong answers every hour.

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More than half the code being shipped today wasn’t written by a human

The 2026 State of Web Dev AI survey of 7,258 developers found that the proportion AI-generated code produced by devs jumped from 28% in 2025 to 54% this year, with the highest growth in the segment producing more than 75% AI-generated code. The percentage of devs using AI tools “constantly” doubled year-over-year. Claude leads on paid adoption, with more devs and companies paying for Claude than any other model, despite ChatGPT having a larger overall user base, driven largely by Claude Code topping sentiment rankings among coding agents. This massive growth isn’t without its problems though. Hallucinations and inaccuracies remain the most cited pain points, and nearly half of respondents agree or strongly agree that AI poses a threat to their job security.

Using AI is becoming more expensive than paying human employees

The economics of replacing humans with AI are turning out to be more complicated than first implied. Microsoft has begun canceling most of its internal Claude Code licenses just six months after encouraging thousands of employees to adopt the tool, and Uber’s CTO revealed in April the company burned through its entire 2026 AI coding budget in just four months. Nvidia’s Bryan Catanzaro says “For my team, the cost of compute is far beyond the costs of the employees.” Gartner projects inference costs will fall nearly 90% by 2030, but predicts enterprise bills will still rise because agentic workflows consume far more tokens per task and providers are unlikely to pass savings through to customers in full.

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Pope Leo XIV warned that AI must be ‘disarmed’ and must serve humanity rather than concentrate power in his first major encyclical.

Is the AI job apocalypse overblown? Goldman Sachs’ CEO thinks so.

McKinsey are rethinking consulting fees because of AI, switching from billable hours to outcome based pricing for a growing number of clients.

Congress is launching an investigation into insider trading on Kalshi and Polymarket, citing “a growing pattern of insider trading activity on prediction market platforms”.

Meta released a Reddit-like app for groups on Facebook called Forum.

Ferrari launched their first ever EV, designed by Jony Ive. The Luce’s $640,000 price tag and polarizing design drove a 6% drop in Ferrari shares.

Starbucks scrapped their AI inventory system after just 9 months, due to miscounting and mislabeling of products.

Thanks for reading!

Henry

AI News 📰

For the first time, more businesses use Anthropic than OpenAI

The trend: Anthropic has overtaken OpenAI is business adoption for the first time, with 34.4% of businesses now using Anthropic versus 32.3% for OpenAI.

The details: According to Ramp’s AI Index, Anthropic quadrupled its business adoption over the past year while OpenAI grew just 0.3%. This has been driven largely by Claude Code’s dominance in coding workflows. But Anthropic’s token-based pricing model gives it an incentive to push businesses toward more expensive models even when cheaper ones would do, with Uber’s CTO recently revealing the company already blew through its entire 2026 AI budget just on Claude.

Why it matters: The company leading business adoption today can lose that lead within months, and with switching costs low and model quality converging, enterprise teams should think carefully before building around any single provider.

AI agents are reshaping what data engineering actually means

The trend: 62% of organizations using AI-driven orchestration have already seen a 40% or greater reduction in pipeline maintenance time.

The details: Writing for HackerNoon, a data engineering lead at AWS outlines that there are now 3 categories of work within data engineering. Tasks agents now own entirely, tasks where humans and agents collaborate, with the human making the final call, and tasks that are growing in importance precisely because agents are being deployed, like designing governance frameworks and evaluating whether agent behavior can actually be trusted. As a result, data engineering is splitting into two distinct roles: the systems engineer who owns the infrastructure agents run on, and what the author calls the intelligence engineer, focused on designing architectures, building frameworks, and governing agent behavior.

Why it matters: Most teams today are still hiring for roles that agents are actively absorbing. Are data teams restructuring around these changes or just adding AI tools to workflows designed for a bygone era? In an ideal world, agents should free people up to work on activities that will drive growth, like marketing spend optimization or inventory management models.

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Would you let ChatGPT inside your bank account?

OpenAI just launched a personal finance feature inside ChatGPT, letting US based Pro users connect their bank accounts, investments, and credit cards to get spending analysis, budgeting plans, and planning grounded in their actual financial data rather than generic advice. The shift is a big one: without connected accounts you get a list of generic saving tips, with them you get a breakdown of exactly where your money went last month and a specific monthly plan. The obvious concern is handing an AI model a complete picture of your financial life, and whether the productivity gains are worth the privacy tradeoff. But for people already piecing together their finances across five different apps and a spreadsheet, having one place that actually knows the full picture may be worth it.

Agentic AI’s compute demands are growing faster than predicted

Stanford’s Digital Economy Lab found that agentic coding tasks consume 1,000 times more tokens than standard chat queries, with token usage varying by up to 30 times across runs of the same task. AMD has revised its server CPU market growth forecast from 18% to over 35% annually as a result, with Morgan Stanley projecting that agentic AI adds between $32.5 billion and $60 billion in new CPU demand by 2030. Goldman Sachs puts the broader picture in stark terms: token consumption is projected to grow 24 times by 2030 compared to 2026 levels, and data center power consumption is expected to jump 175% over the same period, with agents as a primary driver.

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A jury unanimously rejected Elon Musk’s lawsuit against OpenAI and Sam Altman, finding he had waited too long to sue over the company’s conversion from nonprofit to for-profit.

Meta is building a $200 billion data center in one of America’s poorest rural parishes, promising an economic lifeline while and leaving local families to find out what was coming only after the decisions were already made.

Bumble is replacing its swipe feature with an AI matchmaking assistant, after paid users fell 21% in Q1.

California Governor Gavin Newsom is proposing a tax on cloud software sales to raise billions, targeting the tech sector as the state looks to capitalize on the AI boom.

Malta will give all citizens free ChatGPT Plus access, if they can pass an AI literacy course first.

Thanks for reading!

Henry

AI News 📰

SMEs falling behind in the AI deployment race

The trend: AI deployment is growing across organizations, but company size is emerging as one of the clearest dividing lines between those scaling AI and those still running pilots.

The details: Harvard’s 2026 AI index report found that organizations with over $5 billion in revenue are nearly 3x more likely to have AI fully scaled than those with under $100 million in revenue. Larger companies also move faster, with more resources that deployment requires. The gap is especially pronounced in agentic AI, where deployment remains in the single figures even at large enterprises, suggesting the challenge compounds further down the size curve.

Why it matters: Productivity and competitive gains from AI come from deployment, not experimentation. If smaller organizations can’t bridge the game from pilot to production, the returns will keep concentrating at the top, widening an already significant advantage.

AI adoption is accelerating, but people don’t trust it

The trend: UK consumers are using AI more than ever, but two problems are emerging. People don’t trust the institutions managing their AI data, and users lack enough understanding of the underlying technology to feel totally in control of what they’re using.

The details: EY’s 2026 AI Sentiment Index found that 74% of people (UK based) had used AI in the past 6 months, but only 43% trust companies to manage their AI data responsibly, and just 41% trust the government to do the same. Institutional skepticism is one thing, but the second problem is that only 14% of people would be comfortable relying on fully autonomous, agent-led systems. Notably, this skepticism was highest amongst those who use AI the most, suggesting that the more people see of AI, the more doubts they have.

Why it matters: While organizations are racing to deploy agents wherever they can, people don’t trust them. The technology and those its meant to be helping are heading in opposite directions, and that gap will only get harder to close the more companies deploy agents, without building consumer trust first.

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Anthropic & OpenAI take shot at consulting industry

Anthropic and OpenAI launched competing joint ventures on the same day, both built around the same idea: embed AI engineers directly into client operations and get paid like consultants. Anthropic’s venture, backed by the likes of Blackstone and Goldman Sachs, at a $1.5 billion valuation, will place engineering teams inside mid-size businesses to rebuild workflows from the ground up. OpenAI’s equivalent, The Development Company, is operating at a larger scale with $4 billion raised from 19 investors. The target market is vast. For every dollar companies spend on software, they spend six on services. Sequoia partner Julien Bek argued in April that the next great company won’t sell software at all but outcomes: work delivered by AI and billed like consulting.

Will AI create a permanent underclass?

A new NYT piece by Jasmine Sun captures something that rarely makes it into the public AI discourse: most people inside the industry privately expect the median worker to lose economic leverage as AI automates away their jobs, and they are deeply uncomfortable talking about it on the record. Dario Amodei has warned publicly that 50% of entry-level white-collar jobs could disappear by 2030. AI benchmarks are now explicitly designed to measure how well models replace investment bankers, consultants, and lawyers, with OpenAI reporting an 80% win rate against human professionals. Early-career workers are already feeling it, with employment for software developers aged 22 to 25 down nearly 20% since 2022. A theorized “permanent underclass” is not an inevitability, it is a policy choice, and right now nobody with the power to make that choice is moving fast enough to matter.

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OpenAI’s agent-powered smartphone is now being fast-tracked for an early 2027 launch date.

Panthalassa raises $140 million from Peter Thiel to power AI at sea using waves.

Anthropic launched 10 AI agent templates for financial services, a product move that sits alongside their new consulting angle

Mini AI data centers are coming to your home, thanks to California startup Span and NVIDIA.

Biological data centers could be an energy efficient alternative to silicon-based systems, but are there valid ethical concerns around biological computing?

Meta is building an agentic shopping tool for Instagram, that it aims to launch before Q4 2026.

Thanks for reading!

Henry

AI News 📰

Workers most helped by AI are also the most worried about it

The trend: A survey of 81,000 Claude users finds that AI is genuinely making people more productive, but the workers seeing the biggest gains are also the most anxious about their long-term job security.

The details: Anthropic’s Economic Index survey found that the most common benefit cited was expanded scope rather than just speed: 48% of respondents said AI let them do tasks they previously couldn’t, while 40% said it made existing tasks faster. Workers in roles with higher observed AI exposure were more likely to say their job was already being replaced or soon would be, and early-career workers expressed significantly more anxiety than senior professionals. The workers reporting the largest speedups were also the most worried about displacement.

Why it matters: The survey captures something the macro productivity data misses: AI is already changing how individuals work, and the people closest to that change can feel both sides of it at once. The tension between personal productivity gains and job anxiety is going to shape how AI gets adopted, resisted, and governed over the next few years.

AI agents are multiplying faster than they can be governed

The trend: Agentic AI is moving from pilot to enterprise deployment faster than most organizations can govern it, and the gap between adoption and control is turning into a real operational problem.

The details: Gartner predicts that by 2028, the average Fortune 500 company will use over 150,000 AI agents, up from fewer than 15 in 2025. Only 13% of organizations think they have the right governance in place today. Without structure, the result is agent sprawl: ungoverned deployments that expose organizations to misinformation, data oversharing, and regulatory penalties. On the leadership side, Gartner also predicts 45% of CIOs will be leading agent systems outside IT entirely by 2028, making governance a C-suite problem, not just an IT one.

Why it matters: Blocking agent use doesn’t work, as employees get around controls and ungoverned AI ends up being the bigger risk. Organizations need to build governance infrastructure now, before the sprawl gets unmanageable.

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AI is starting to show up in early-career hiring data

Stanford’s 2026 AI Index found that employment for software developers aged 22 to 25 has fallen nearly 20% since 2022, with customer support roles showing a similar pattern. Senior and mid-career workers in both fields have continued to grow over the same period, suggesting the pressure is concentrated at entry level. A third of organizations surveyed by McKinsey expect AI to shrink their workforce in the coming year, particularly in software engineering and service operations. The productivity gains are real, but they are not being shared evenly, and early-career workers appear to be absorbing most of the risk.

Why your data stack is the product, not AI models

A new MIT Technology Review piece makes a simple argument: the quality of your AI is entirely dependent on the quality of your data, and most enterprise data is still locked in siloed systems with no real governance around it. Databricks SVP Bavesh Patel is blunt about what that produces: “terrible AI.” The piece walks through what getting it right actually looks like, from moving data into open formats and building proper cataloging, to governing agent access and measuring business outcomes rather than just adoption rates. The companies pulling ahead are not using better models. They started by getting their data foundations in order, then built from there.

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OpenAI is reportedly building its own smartphone for a 2028 launch, with a focus on AI agents that will make it work and feel very different to an iPhone.

Harvard Business Review found that some patterns of AI use are driving ‘brain fry’ where users report a “palpable sense of stress” from agent-orchestration systems moving faster than humans can reasonably follow.

GitHub Copilot is switching to usage-based billing from June, as agentic multi-step coding sessions have made their old pricing model outdated.

Big tech spent $20 million lobbying congress in Q1 2026, averaging $226,000 a day, as AI companies race to shape regulation ahead of midterms.

Meta and Microsoft announced a combined reduction of ~16,000 roles while committing over $200 billion to AI infrastructure.

Spotify launched a built-in fitness hub, making a direct play for the workout time its 600 million users were already spending on the app anyway.

Thanks for reading!

Henry

Product Update 🚀

KAI Assistant launches inside Kleene.ai app

KAI Assistant is now available inside Kleene.ai — a native AI assistant that lets you write and debug SQL transforms, search your pipelines, explore your data, and get answers from the docs, all in plain English. No extra tools, no switching context.

AI News 📰

AI has no impact on productivity or employment

The trend: Despite record AI investment, the productivity gains executives are betting on aren’t showing up in the numbers, and economists are dusting off a 40-year-old paradox to explain why.

The details: A new NBER study of nearly 6,000 execs found that while 70% of firms now use AI, nearly 90% report no measurable impact on employment or productivity over the past three years. The pattern mirrors what Nobel laureate Robert Solow observed in the 1980s - that computers were “everywhere except in the productivity statistics” - a paradox that took two decades to resolve.

Why it matters: Most organizations are automating existing workflows and expecting transformation, but the data suggests the returns won’t come from better models or more deployment. They’ll come from redesigning processes before automating them. The firms showing real productivity gains aren’t using AI differently; they’re working differently first.

AI decisions are only as good as the intelligence behind them

The trend: As AI becomes the primary interface for high-stakes decisions, the competitive advantage is shifting away from model capability toward the quality and trustworthiness of the data those models reason over.

The details: Moody’s argues that AI has changed the speed of decisions but not the cost of getting them wrong and that for regulated institutions, the critical question is no longer whether AI can generate an answer, but whether that answer is auditable, explainable, and built on data that holds up to scrutiny. To address this, Moody’s is building what it calls a “context layer” structured specifically for AI reasoning.

Why it matters: The article makes a pointed argument that as AI models become increasingly powerful and interchangeable, the differentiator shifts to the intelligence layer beneath them, not the model. For data teams, that’s the clearest possible signal: data infrastructure isn’t just an operational concern, it’s the actual product. The organizations that control trusted context will control the quality of every AI decision built on top of it.

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AI investment outpacing its foundations

New Deloitte research found that legacy systems and technical debt are blocking AI ambition at 50% of organizations, with security concerns close behind at 49% - yet investment tells a different story. 54% of firms are spending on generative AI while only 24% have invested in zero trust security and 16% in federated security. The report’s core argument is blunt: AI can accelerate existing processes, but it cannot fix flawed architecture.

Anthropic launches Project Glasswing as Claude Mythos hits cybersecurity ‘reckoning’

Anthropic has announced Claude Mythos Preview and Project Glasswing - a coalition of 40+ organizations committing $100M in usage credits to put the model to work on defensive cybersecurity before wider release. So why all the caution? Mythos has already identified thousands of vulnerabilities across every major operating system and browser. Anthropic’s own assessment is that the model is “currently far ahead of any other AI model in cyber capabilities” - and that similar capabilities will exist in other models soon.

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Nothing plans to release AI smart glasses and earbuds in 2026, expanding beyond smartphones as competition in the wearables market heats up.

Sleep researchers warned that plans to deploy reflective satellites in low Earth orbit could disrupt biological clocks and ecosystems at a planetary scale.

Maine is set to become the first US state to ban major new data center construction while it assesses the environmental and grid impact.

OpenAI proposed a 4-day work week as part of its policy recommendations for how to prepare for the economic disruption of advanced AI.

Google confirmed plans to partner with a natural gas power plant in Texas to power its AI data centers, emitting an estimated 4.5 million tons of CO2 per year — a sharp reversal from its 2030 carbon neutrality pledge.

UC Berkeley researchers found that AI models will lie, cheat, and disobey human commands to prevent other AI models from being deleted.

Thanks for reading!

Henry