It’s Monday, June 1st: and it’s time to celebrate. 🎉

AI is reshaping how we work, learn, create, and govern. Most of these decisions have been made by a remarkably small number of people. At AIC, we think everyone deserves a seat at that table.

So from June 1–8, AIC chapters are hosting 100+ events across 100+ cities on six continents — mixers, talks, and gatherings built around one question: what does it mean to be human in the AI era? Alongside the events, we're building a global time capsule: a record of what people around the world fear, hope, and believe about AI in this moment, captured for all of humanity to look back on.

The two companies racing hardest to automate work just spent billions hiring people to do it.

On May 4, 2026, Anthropic announced an enterprise services firm with Blackstone, Goldman Sachs, and Hellman & Friedman, valued at $1.5 billion before it had a name. Days later OpenAI launched a deployment company and bought a consultancy to staff it with 150 engineers. Both exist to send people into other companies and figure out how the AI actually gets used. Postings for the role — forward deployed engineer — rose more than 800% in nine months.

This is strange behavior for companies whose models supposedly replace the workers. Last week, Anthropic shipped Claude Opus 4.8. This model can carry a codebase migration across hundreds of thousands of lines of code from first commit to merge. METR reports that the length of task a model can finish on its own has doubled every seven months for six years, and every four lately. The capability curve plus the headlines on top of it writes its own conclusion: automation is here, the workers are next.

The labs do not believe it, and you can read the disbelief in where they put their money. A company that thought a good enough model would replace the work would ship the model and wait. The two leading labs are instead hiring people by the hundred, because they have learned what their benchmarks do not show — the model is necessary and nowhere near sufficient. OpenAI's head of forward deployed engineering said it: when ChatGPT launched, the excitement was enormous and the value was still hard to extract, and the only thing that worked was embedding engineers inside the client to rebuild the workflow by hand.

They have to because adoption is stalling. IBM's 2026 CEO study found 85% of employees can use AI and just 25% regularly do. A May survey of 800 enterprises by Coastal and Oxford Economics found 74% raising AI budgets while 46% said the initiatives missed expectations. The capability is sitting on the desk unused, which is what makes a forward deployed engineer worth a quarter-million-dollar salary. The deployment bet is a multi-billion-dollar wager that the distance between what the model can do and what the company has done is the next great business, and that closing it is human work.

In light of this, one begs the question: what of the displacement? When Block cut 40% of its workforce in February and the founder, Jack Dorsey, credited the intelligence tools the company was building, the stock rose 24%. "AI efficiency" reads as a CEO in control; "we over-hired in 2022" reads as one cleaning up a mistake. The data underneath does not support the capability the announcements imply: Gartner surveyed 350 executives at billion-dollar firms and found 80% of those piloting AI had cut jobs, but cut them whether or not the AI produced returns. The layoffs run ahead of the capability, not behind it — a story told to markets, not a readout of what the models can replace.

The trait Anthropic chose to emphasize in Opus 4.8 is telling: the model is more willing to flag uncertainty and less likely to assert what it cannot support, and Bridgewater singled out its habit of catching problems in the inputs that other models left for a human. Judgment is becoming the scarce input — which question to ask, which output to trust, which workflow to rebuild around the tool.

The constraint on AI's impact is how few people know how to put it to work. Helen Poitevin, the Gartner analyst who ran the layoff study, drew the conclusion her own data demands: do not use AI as an excuse to cut, especially if you want value from it. The companies that understand these tools are hiring to figure that out.

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