Prove It or Lose It: The New AI Access Rule

A London CEO makes staff earn their AI. A World Bank trial in Peru found AI didn’t make teachers faster, it made them sharper judges. The tool is easy. The judgment is the job.

Oct 21, 2026

Directing Intelligence Digest | October 21, 2026

Two stories in today’s video, one from work and one from learning. In the first, a payments company hands out AI only to people who can show what it adds. In the second, a randomized trial with 1,269 teachers finds the real payoff of AI training wasn’t time. It was judgment.

The short version

Paysend’s CEO gives AI to top performers who prove what it adds, and estimates 25% more engineering output from the same team.
In UK government research, 77% of AI-using businesses saw no change in revenue.
A Texas restaurant replaced its walked-out server with an AI host. The tips now go to the kitchen.
Half of all business AI spending comes from 2% of firms, and executives report a 0.2% yearly productivity bump so far.
In Peru, AI training saved teachers zero time but made them better at catching bad AI output, especially teachers over 54.
Teachers in Greece let harsh grades stand when told an AI gave them.

WORK

Paysend makes you earn your AI

At London payments company Paysend, AI access is earned. CEO Ben Chisell, a self-described “very cynical man” about the hype, gives general-purpose AI tools to his highest performers, often only after they show what the tools add. New hires get a month to experiment, then explain what they achieved. One new SEO lead asked for Claude, then gave it back a month later: it only wrote his briefs faster. Now he’s building a single content-brief agent instead. Chisell estimates 25% more engineering output with the same-sized team (his estimate, not an audit). The contrast, per CNBC: Lloyds Banking Group rolled out 40,000 Copilot licenses, and UK government research found 77% of AI-using firms saw no revenue change.

What to watch: “Human in the loop shouldn’t mean human CC’d,” Chisell says. “It should mean human ownership.”

Read it: CNBC: Some companies give AI to everyone. This CEO is doing the opposite
On X: Shakthi’s post

Who’s Directing? Handing everyone a license is easy. It’s also how you scale bad judgment at the speed of software. Before you buy another seat, ask who on your team can show you what the last one produced.

The server walked out. The AI host stayed.

Two months ago, the only server at Wok & Karahi, a family-run Chinese, Indian and Pakistani restaurant in Spring, Texas, walked off the job. Owner Abbas Zoeb, a former Google software engineer, replaced the role within 24 hours: a virtual host named Haniya that speaks seven languages and answers the phone after closing, plus QR codes and pre-pay ordering. Zoeb told FOX 26 Houston he’s saving or winning back $9,650 a month, about $3,500 of it the server’s salary, for roughly $300 a month in software. Kitchen staff and the chef now get 100% of tips. The numbers are the owner’s. A University of Houston-Downtown professor notes it fits quick lunch spots better than fine dining.

What to watch: whether diners trade hospitality for speed, and whether this becomes the default answer to a no-show.

Read it: FOX 26 Houston: Spring restaurant turns to AI host to stay afloat after server walks out

Who’s Directing? A job didn’t vanish here. It was split. The machine took the order pad and the kitchen got the tips. Owners, that’s the real decision: when AI saves you a salary, who gets the difference?

Half of all AI spending comes from 2% of firms

Nick Bloom and colleagues surveyed nearly 9,000 CFOs, CEOs and senior executives across seven countries this summer for NBER. 78% of firms use AI. US firms expect to spend about $2,000 per employee this year, around $300 billion in total, but just 2% of firms account for over half of it. Executives use AI 2.1 hours a week, up from 1.5 in the winter. They report essentially no net effect on jobs, yet more than 40% of firms expect gains or losses that only cancel out in aggregate. The productivity bump so far: 0.2% a year, with 0.6% expected over the next three years.

What to watch: that churn under the flat line. “No net effect” can still mean your job.

Read it: NBER Working Paper 34836: Firm Data on AI
On X: Azeem Azhar’s thread

Who’s Directing? “No net job effect” is an average, and nobody lives in an average. Leaders, if 40% of firms expect people moving in and out, tell your team which side of that ledger they’re on before the survey does.

LEARNING

Not faster, but better: Peru’s teachers became sharper judges

The World Bank randomized 390 public schools in Lima, Peru, covering 1,269 sixth-grade teachers. Treated teachers got a one-day training, a Microsoft Copilot license and eight months of weekly WhatsApp tips. Copilot activation rose 20.4 points over a 2.8% control mean. Time saved on seven core tasks, from lesson planning to grading to parent messages? Precisely zero. The gains were in skill: better prompts, even on a rubric the trainers never saw, and 6.7 points less likely to accept a weak AI answer as good. The biggest gains went to teachers over the median age of 54, who started furthest behind, so the gap narrowed. Note: Microsoft’s AI Economy Institute co-funded the study.

What to watch: whether school systems sell AI training on better judgment instead of hours saved.

Read it: World Bank Policy Research Working Paper 11476: Not Faster, but Better (PDF)
On X: Michael’s roundup post

Who’s Directing? We keep selling AI to teachers as time back. The evidence says it’s judgment, and judgment is the better deal. If your PD promises a shorter Sunday night, you’re promising the wrong thing.

Teachers let harsh AI grades stand

In a preregistered experiment, 1,339 teachers in Greece reviewed identical student work with a deliberately wrong score, labeled as coming from either a colleague or an AI. When the score was too harsh, the gap between the final grade and a fair one was 22% larger under the AI label. Lenient errors got corrected either way. Teachers rated AI as less fair and less accountable, then deferred to it anyway, and the effect was strongest among younger, tech-confident teachers. Meanwhile, Korea is scaling AI essay scoring: Seoul’s Chaeum AI grew from 66 pilot schools to a planned 135, and Incheon just trained about 180 teachers to grade alongside its system, with a December comparison of teacher and AI scores.

Read it: PNAS Nexus: Why do experts miss AI’s errors? · Aju Press: AI grading expands to more Korean classrooms · Weekly Hankook: Incheon compares teacher and AI scoring (Korean)
On X: Classignal’s thread

Who’s Directing? “A human will check it” is a promise, not a safeguard. The teachers who trusted AI least still let it fail the kid. If you’re putting AI in the grading loop, train the doubt before you ship the tool.

Springfield teachers push to pause classroom AI

In Springfield, Massachusetts, the teachers’ union organized after learning in late August that Alpha School planned an AI pilot at Kiley Prep Middle School. Alpha says two hours a day of AI tutoring covers academics, with classroom “guides” rather than teachers. The Springfield Empowerment Zone postponed the pilot on September 21. Union president Riley Hernandez called it “an experiment on Springfield children,” and the union held a community forum on October 14. State union president Matt Bach wants a one-year moratorium on district contracts with AI and ed tech companies.

Read it: Massachusetts Teachers Association: Educators, Parents Oppose an AI Intrusion into Public Schools · Hoodline: Springfield teachers demand AI moratorium

Who’s Directing? The fight in Springfield isn’t really about AI, but who gets asked to be involved. Any pilot that skips the teachers and the parents has already failed its most important test.

Quick hits from X

Sayash Kapoor says the newest AI agents improved at experimental design on open-ended research, but the original authors still rejected their papers on judgment and calibration. His post
Alex Imas says DeepMind’s AI-in-science work shows huge uplift in hypothesis generation, with the bottleneck moving downstream to verification and testing. His post

Who’s Directing? When ideas get cheap, checking gets expensive. The people who can tell a real result from a pretty one just became your scarcest asset. Pay them like it.

Worth a listen

Plain English with Derek Thompson, “You’re Not AGI-Pilled Enough,” with Ethan Mollick: on why capability grows fast while organizations change slowly, and the question of what happens when “humans, once the prompter, become the prompted.” Watch the episode

Today’s stories all land on one skill. Paysend wants proof, Peru’s teachers got sharper and Greece’s teachers didn’t look twice. Using AI takes a day of training. Knowing when it’s wrong is the work, and it stays ours.

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