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Hey, I'm Reza Markovadi.
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Welcome back to QuantoBits.
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We watch Minions for the first time for Family Movie Night.
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If you haven't seen it, it's hundreds of little yellow helpers, all super eager, all building things at once.
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Mostly chaos with the odd thing that actually works.
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And it looked a lot like AI experimentation in some companies right now.
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I'm quite AI pilled.
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I've been pushing companies to experiment with AI as early as possible, and I still am, but I'm also starting to see the practical limits of how far that should go.
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So this week is about how to keep experimenting and make sure the experiments leave something behind.
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Here's what happens when experimentation becomes the strategy.
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People build a lot of things that demo well dashboards, command centers, tools that pull data together before a customer meeting.
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They look great, but you get only a few real automations, the kind that actually force a process to change and make a real dent in productivity.
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And very few of all these projects change and number the CFO tracks.
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And it's not just smaller companies.
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Accenture, the big consulting firm, surveyed 300 technology leaders at companies with more than a billion dollars in revenue and published the results on November 3rd.
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70% said they use AI mainly to make individual people more productive.
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Only 37% use AI agents, meaning AI that takes actions on its own, to run a piece of work from start to finish.
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Accenture's own summary was pretty blunt.
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Enterprise AI deployment is broad, value conversion is not.
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In other words, a lot of companies are using AI, and far fewer are turning it into business results.
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A mid-market company can't afford to spread AI that thin without knowing which bets pay off.
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There's the kind of beauty contest going on.
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The projects that win at PLOS are the ones that demo well and are easy to count.
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A new dashboard, hours saved, how many people use the tool this month?
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None of those is a result yet.
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Hours saved is a starting point.
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The result is what changes in the business because of those hours.
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Now, most of these experiments will never turn into lasting workflows.
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That's fine as long as each one stays cheap and contained.
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So here's a test I will use instead of counting experiments.
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Is your tenth experiment cheaper and safer to run than your first?
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If every team starts from scratch on its own, the answer is usually no.
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Here's why.
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Every disconnected experiment builds its own plumbing, its own way to signing, its own copy of the data, its own connection to the CRM.
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And some of that plumbing leaks.
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I spoke with someone at a software company recently, and they had found that about a tenth of their AI build was based.
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Some of it was forgotten API keys.
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Those are the credentials software uses to call an AI service.
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And these were still running up chargers for work nobody needed.
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Some was automated jobs nobody had switched off.
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Same company, different problem.
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Their AI search tool was answering revenue questions with numbers it found in team chat messages.
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So now every revenue requestion goes to one verified source and nothing else.
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Goldman Sachs Investment Bank went through its own version of reining in a free-for-all.
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For months, it let traders and bankers use AI coding tools, La Cloud Code, a coding assistant from the AI company Anthropic.
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Then in October, it updated its internal guidance to limit those tools mostly to its roughly 12,000 engineers, Bloomberg reported.
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A spokesperson said the change followed a period of experimentation that weighed productivity and risk management.
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So what should experiments leave behind?
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At its simplest, four things.
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First, who and what can act.
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That's sign in and permissions for people and for their AI agents, plus which AI models are approved for which tasks.
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So routine work doesn't default to the most expensive model and sensitive work doesn't go to one nobody's checked.
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Second, what the AI can trust.
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One approved source for each kind of company data.
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Third, what it can touch.
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Approved connections into company systems with limits on what each one can change.
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And fourth, what it did and what it cost.
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A record of every action checks on the quality of what it produced and cost tracking.
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Load full transcript
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A failed experiment can still earn back part of its cost if the next team gets to use what it left behind.
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And that fourth piece, the record of actions and cost, is how you finally answer the CFO's question.
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What are we actually getting back in revenue or productivity revolut?
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The British fintech shows what this looks like at a bigger scale.
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Its head of operations and infrastructure, David Abbitball, told a conference in New York in October that engineers had been spending$1,000 to$10,000 a month each on AI coding tools, with little to show for it.
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So Revolute put strict budget limits in place.
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Then it moved to an open weight model, meaning one anyone can download and run.
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On computers it runs itself.
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By its own figures, that costs about a tenth as much per use as the commercial coding tools.
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Once AI was that cheap, engineers stopped rationing it.
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He says it improved coding time by 60%.
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That's Revolute's own number, and the register, the tech site that reported his talk, doesn't say how it was measured.
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Most mid-market companies won't run their own models, but the lesson travels.
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Once the infrastructure changed the economics, they could open up news.
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You don't have to build all four at once.
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Start with the one your last AI problem came from.
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Check what your sign-in provider and your existing tools already do before you build anything new.
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For trusted data, it can be as simple as writing down which source counts for which number.
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Now there's a fair objection to all this.
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Building infrastructure first can become its own beauty contest.
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A platform team builds shared pieces nobody uses, or the reviews get so heavy that nobody starts.
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And moving fast and breaking things has real value.
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It's often how you find the old hard-end processes that slow AI projects down.
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So don't build the four pieces up front.
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Build them as your experiments show you need them.
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When several teams hit the same wall, build that piece once for everybody.
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So where do I land?
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Keep experimenting.
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But be more intentional about which experiments you back because they take time and money, and you want people rowing in roughly the same direction.
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Back the ones that change a KPI you care about or that make the next experiment cheaper or safer by adding one of those four pieces.
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The rest only if you can afford them.
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People get excited about playing with a new technology, and that excitement fades.
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By the time it fades, you want AI already running, work that matters to the business.
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One quick hit, AI use is turning into a ration budget line.
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Even at Apple, Bloomberg reports that in October, Apple cut some of its own engineers' daily AI spending caps from$300 to$200.
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Staff outside engineering could get a cap as low as$25 a day.
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Apple didn't comment, if an engineer used that$200 cap in full, that's about$50,000 a year at 250 working days.
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So expect your AI bill to need the same management as your cloud bill.
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For after hours might pick from outside work.
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A book, or really an audiobook.
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Dark Matter by Blake Crouch, a science thriller from 2016.
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Jason Dessen is a Chicago physics professor who gave up research career to raise a family.
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He's abducted on the walk home and wakes up in a lab in a world where he never married his wife or had his son.
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Instead, that version of him built the box that opens into other versions of reality, like books about time travel and the many words idea.
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That every choice splits off a new reality.
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The execution was weaker than the idea though, the writing is average, the romance was too much for a sci-fi book, and the showdown at the end was overdone.
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But the takeaway landed with me.
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The grass always looks greener in other versions of ourselves, and the other sign may hold something you like less.
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Four stars.
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That's it for this week.
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The full issue with all the links and sources is in your inbox or at quanta bits newsletter.behive.com.
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I'm Reza Maracabadi, thanks for listening.
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See you next week.