WINDOW INTO THE FUTURE OF WORK

Everyone is using AI. Sort of.

Everyone got the memo. Use AI more. Experiment. Find use cases. Become “AI native.” Whatever you do, don’t be the person who gets left behind. And apparently people are feeling that pressure.

Microsoft asked 20,000 people who already use AI at work about this earlier this year. 65% said they’re worried about falling behind if they don’t adapt quickly. Except 45% also said it feels safer to keep hitting their existing goals than to redesign how they work with AI. And only 13% said they’re rewarded for trying to reinvent the work when the experiment doesn’t immediately pay off.

This is a pretty cursed setup lol. You’re being told the way you work needs to change, while the safest way to succeed is to keep delivering exactly what you already deliver.

So of course people start optimising for the easiest thing they can prove. Prompts sent. Tokens burned. Copilot opened. Training completed. Agent deployed.

All very easy to count. Whether the work actually got better is where it gets annoying. So what does progress look like? 👇

When AI actually changes the work

Buffer basically stopped normal work for four days and told the whole company to build something useful with AI. By the end, 71 people had shipped 56 new internal tools.

And Hailley Griffis, their Head of Communications & Content, went into the week describing herself as non-technical and came out having shipped six internal tools and making small website fixes herself instead of immediately pinging an engineer.

This is obviously still a very AI-forward company. But Buffer isn’t the only team landing here. At LangChain, an AI drafts sales outreach, but it lands in the rep’s Slack for them to send, edit or cancel. Every edit teaches the system what to change next time. Mintlify literally has someone responsible for keeping the company knowledge their AI works from useful.

That is much closer to the AI future of work I’m seeing emerge. Specific bits of work now work differently and somebody is responsible for making sure it keeps working.

WHAT WE’RE TRYING TO MEASURE INSTEAD

I said on LinkedIn this week that I’m going to start sharing more of what we’re seeing inside companies trying to change how people work with AI. So here’s one that feels very relevant today.

At 100 School, AIQ is one of the ways we look at whether people are getting better at using AI in their work. It boils down to three pretty basic things:

The full thing gets more specific depending on the team. But those three questions are a lot closer to what we care about than how many times somebody opened ChatGPT this week.

HOW TO AI

The model war is back again.

Claude Fable 5.1 launched Tuesday. GPT-6 Astra followed Thursday. Try them but you probably don’t need to relearn how to use AI this week.

These are 4 guides that still matter whichever model you open:

• How to fix a bad AI answer.

• How to find a task worth using AI for.

• How to turn one good AI result into something repeatable.

• How to make AI check its own work against your quality bar.

BEFORE YOU GO

See you next Sunday! ✌️