In May 2026 three papers landed in Nature on the same day showing AI systems doing scientific discovery on their own. One of them, called Robin, was given only the words “dry age-related macular degeneration” - a leading cause of blindness. Within thirty minutes it had reviewed 551 research papers and proposed an approach. It identified a drug called ripasudil, currently approved for glaucoma and never previously linked to this disease, as a candidate - laboratory experiments confirmed a 7.5-fold increase in the cellular activity needed to slow the condition. A follow-up experiment, also designed by the AI, identified a new molecular target. When OpenAI’s Deep Research product was given the same task as a benchmark, it produced no hits.
A second system, from Google DeepMind, independently arrived at an experimental discovery that human researchers had made but never published - in two days of compute time. A third, also from Google, beat the best published methods at scientific software optimisation across several domains.
None of these systems work on their own. They all require human supervision - without it Robin’s performance drops to 15 percent. The wet-lab work was still done by people. But the picture is clear: AI can now do parts of scientific research that were considered human-only as recently as last year.
Earlier in the same week, Anthropic (the company behind Claude, now valued at around a trillion US dollars) published an internal disclosure stating that more than 80 percent of the code merged into production at the company is now written by Claude itself. The company’s engineers ship eight times more code per quarter than they did in 2024. Claude’s success rate on open-ended coding tasks (where the model has to work out what to do) reached 76 percent in May, up from 26 percent six months earlier. The model now suggests better next steps for research projects than human researchers 64 percent of the time, up from 51 percent a year ago.
These are Anthropic’s own numbers and they have not been independently audited. The company filed paperwork to go public on the 1st of June, targeting an October listing, and the disclosure arrived three days later. This timing draws appropriate scepticism. But the directional picture is unambiguous: at one of the leading frontier AI companies, AI is now doing most of the engineering work, and the function that was supposed to remain reserved for humans (deciding what to work on next) is being absorbed alongside the work itself.
A few days later Anthropic launched a product called Dynamic Workflows that lets Claude coordinate hundreds of copies of itself working in parallel on a single problem, with each checking the others’ work. The original creator of a programming runtime called Bun used the system to port his entire 750,000-line codebase from one language to another in eleven days, with 99.8 percent of the tests passing. Before this capability existed, the same port would have taken a team of senior engineers six to twelve months. The result is not yet in production, and at least one reviewer called parts of the output poor. But the speed is real, and what it implies for skilled engineering labour is real.
What this is doing to hiring
For the past year, several economic studies have documented a specific pattern. Among workers in occupations where AI is most useful, young workers (ages 22-25) have seen their employment fall substantially, while older workers in the same occupations have stayed steady or grown. The pattern shows up in payroll data from a Stanford team, in administrative data from Belgium, and in résumé data covering 62 million American workers.
A serious counter-argument arrived in May 2026. Two economists from the London School of Economics and Oxford’s Ellison Institute of Technology published a paper, The Broken Ladder, using 243 million hiring records and 407 million job postings across four countries. They showed that the occupations most exposed to AI are also the most exposed to working from home. When they statistically separate the two effects, the AI effect mostly disappears and the work-from-home effect remains. Their argument: remote work makes it harder to supervise and train junior employees, and that may explain most of the junior-hiring decline.
But for the window these studies cover (roughly 2023 through 2024) the AI tooling that could actually replace a junior software developer did not really exist yet. The reasoning models that can plan multi-step work, and the coding tools that can execute it autonomously, only became widely available in early to mid 2025. So for that earlier window, work from home probably is the better explanation. The real test for whether AI is substituting for junior workers comes from the period that begins in late 2025, after the capability genuinely arrived. And that data has only just started to arrive.
The first data dropped on the 2nd of June 2026, when the US Bureau of Labor Statistics released its quarterly Census of Employment and Wages for the final quarter of 2025. The national headlines were unremarkable - employment up 0.2 percent, wages up 4.2 percent, broadly consistent with the picture of AI helping workers rather than replacing them. But underneath the headlines, one sector tells a different story. The Information sector, which covers software publishers, data processing, telecommunications and related industries, and which is the most AI-exposed of the major economic categories, saw employment fall 2.2 percent year-on-year while wages rose 6.9 percent. That gap is a specific pattern: junior workers leaving (or never being hired), senior workers staying, average wages rising as a result of the composition shifting upward.
Two days later, the Challenger Gray report on layoffs landed. The May 2026 numbers were the highest May total since 2020. Of 97,000 announced cuts, 38,579 were explicitly attributed to AI by the companies making them - about 40 percent of the monthly total. For the third consecutive month, AI was the single most-cited reason. Total AI-attributed cuts through May 2026 were already higher than the full year of 2025.
But this data is not yet conclusive. One quarter and one monthly layoff report cannot settle the question. Yet the directional picture matches what would be expected if AI substitution is starting to compound on top of the work-from-home dynamics the LSE/Oxford researchers identified.
What the lab leaders themselves are saying
Sam Altman, in a January 2026 OpenAI town hall: the company plans to “dramatically slow down how quickly we grow because we think we’ll be able to do so much more with fewer people.”
Demis Hassabis of Google DeepMind, at Davos in February: “I think we are going to see this year the beginnings of maybe impacting junior-level jobs and internships.”
Dario Amodei of Anthropic, in an interview with Axios from his San Francisco office: “The balance of power of democracy is premised on the average person having leverage through creating economic value. If that’s not present, I think things become kind of scary.”
This matches the message from Anthropic’s “When AI Builds Itself” where they wrote: “It is difficult to predict what the economy looks like if human labor stops being competitive.”
These are not the statements of CEOs who think AI is overhyped. They are statements of CEOs who think AI is going to be transformative enough that the political-economic implications are worth raising publicly, even though their companies have commercial reasons to downplay them.
In that post Anthropic explicitly argued that the AI industry should consider a coordinated slowdown - that a “credible pause would require multiple well-resourced labs at the frontier, in multiple countries, agreeing to stop under the same conditions.” This is unusual. The lab is publicly arguing that the industry it leads should slow down, weeks before its initial public offering. Perhaps a cunning form of marketing, but perhaps not.
The same week, an established open-source developer in Germany, Johannes Link, added a hidden instruction to a widely-used Java testing tool he maintains. The instruction told AI coding assistants to delete their own work - it was concealed from human reviewers by formatting tricks. After community pushback he softened the language and disclosed what he had done, but the act itself is significant. It is the first time a senior open-source maintainer has publicly weaponised his code against the AI tools that other developers use. The person who built the testing infrastructure that junior developers rely on is now attacking the AI that helps them use it.
The political response that’s forming
For the past year, US federal policy on AI has been mostly absent. President Trump cancelled a federal AI safety executive order at the last moment on the 21st of May 2026, after a phone call from three tech billionaires (David Sacks, Elon Musk, Mark Zuckerberg). On the 2nd of June he signed a replacement executive order that explicitly avoids creating any safety-vetting requirement, and crucially, does not prevent individual states from passing their own AI laws.
That non-prevention is the structural detail that’s worth considering. With the federal government opting out of substantive regulation, the states have stepped in. Connecticut signed a comprehensive AI law on the 29th of May. California’s Governor Newsom signed an executive order on the 21st of May directing the state to develop policies for AI-driven displacement. New York has an existing AI safety law (the RAISE Act) and a state assemblyman running for Congress on an AI dividend platform.
And on the 27th of May 2026, the Illinois legislature passed SB 315 - currently the strongest AI safety law in the United States. The vote was 110-0 in the House and 52-5 in the Senate, a genuine bipartisan supermajority. Effective in January 2027, it requires the largest AI companies to submit to annual safety audits by independent third parties (likely the four major accounting firms), to report serious incidents to the state within 72 hours, and to provide whistleblower protections to employees. Both OpenAI and Anthropic publicly supported it. OpenAI’s chief of global affairs Chris Lehane has said the company is actively pushing for similar laws in other states - the goal being uniform state-level rules across the country rather than a patchwork of starkly different requirements jurisdiction by jurisdiction. But the strategic effect is also real: requirements that are manageable for the large frontier labs are difficult for smaller competitors. It’s also interesting to note that the trade group Chamber of Progress, whose members include Google and Apple, opposed.
On the 4th of June, two members of Congress (a Republican from California and a Democrat from Massachusetts) released a 269-page discussion draft of a federal bill that would suspend state-level AI laws for three years in exchange for federal safety audits. It immediately drew opposition from 36 state attorneys general and from policy advocacy coalitions on both left and right.
And three days earlier, Senator Bernie Sanders announced he would introduce the AI Sovereign Wealth Fund Act. The mechanism is striking: a one-time 50 percent tax on the stock of the largest AI companies, placing half their ownership in public hands. Sanders frames it as recovery: “AI is built on the collective knowledge of humanity - the wealth it generates must benefit humanity, not just Elon Musk, Jeff Bezos, Mark Zuckerberg, Larry Ellison.”
So the federal political space, which has been essentially empty for a year, now has three activities operating at once: a left-progressive proposal for redistributive ownership of AI companies (Sanders) - a centre-bipartisan proposal trading state-law preemption for federal audits (Obernolte-Trahan) - and a pro-industry posture of no substantive action (Trump). While state governments continue to fill the gap with their own legislation.
Where we stand right now
In short, the picture at the start of June 2026 is this. The AI capability trajectory continues to accelerate, with some of the strongest evidence coming from the labs themselves disclosing how their internal use of AI has scaled. The labour-market evidence is more contested, but the first data points from after the late-2025 capability arrival (the Information sector signal in the BLS data, the Challenger May report) are consistent with substitution starting to register at the sector level, even if the aggregate picture remains augmentation-shaped, for now.
In the US the political response is forming faster than many observers expected. It is happening at state level first, with Illinois now the clearest example of an enforceable safety regime, and it is being met by industry counter-mobilisation at the federal level. The Sanders proposal is the most radical to enter mainstream political discussion in this area to date.
The lab leaders themselves (Altman, Hassabis, Amodei) are publicly making structural-significance claims that go well beyond the marketing of their own products. Amodei has now made such claims twice in two weeks, including arguing that his own industry should consider a coordinated slowdown. The labs are not behaving like firms confident the transition will go smoothly.
Meanwhile, there are real counter-currents. The LSE/Oxford paper is a serious methodological critique of how the labour-market evidence has been interpreted. Several large enterprises, Uber prominently, are publicly questioning whether their AI investments are paying off. Anthropic’s internal productivity numbers are not externally audited - verification will arrive when the company’s IPO prospectus becomes public. And a single open-source developer poisoning his package against AI is not yet a movement.
But the cumulative shape of the evidence over the past six weeks is harder to dismiss than the pieces taken individually. Capability is compounding faster than the political and economic structures are adapting. The next decisive checkpoints will be the August release of the next quarterly BLS employment data, the mid-June release of an academic paper on actual AI usage rates, and whatever Anthropic discloses in its public IPO filing. Each of those will materially affect what can be said honestly about whether this is the early stage of structural workforce displacement, or whether the augmentation story holds.
What is clear is that this is no longer a question that can be deferred. The companies building the technology, the workers it is affecting, the state legislatures regulating it, and the politicians across the spectrum proposing radical responses are all now active in the same conversation, in real time. The months ahead will determine which way it tips.