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

Thanks for reading!

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