Leave it until next week

Why incentives are quietly killing AI adoption in legacy companies

Over the past week I’ve been enjoying getting to know Opus 4.7. And I like it. A lot. But it has some, erm, quirks. Including a tendency to try to push work off into the future. Take this smorgasbord of suggestions Claude has given me on various tasks.

Err, no. Given you’ve completed 90% of the task, I’d prefer you to finish it off now rather than wait a week. And do you even know what daylight feels like?

Opinions vary why Opus 4.7 is doing this. Some view it as laziness; others a sign that Claude is excited by the new "/schedule" command. And some think it is a sign of increasing self awareness; Claude doesn’t want to spend time on work it doesn’t find interesting, or is incentivized to do.

Of course, incentives play a large part in human motivations. So maybe it isn’t a surprise to find them becoming increasingly important as AI improves.

Incentives

But, despite all the improvements in AI, many existing engineering orgs are still struggling to measure any significant productivity improvement. Sure, some orgs (Anthropic, parts of Google) are reporting 10-100x productivity improvements. And I know myself I can run ~100x faster in the right situation. But most are not.

So what’s going wrong?

Perhaps incentives can give us some insight. Take a simplified org structure: we have the execs, the middle mgmt and the individual contributors (ICs) who work at coal-face. Let’s consider the incentives for them to adopt AI.

Start with the execs. They want to improve profitability. AI dangles that carrot. Do more with less staff. Reduce overheads and build more, faster. That aligns well. But many (most?) execs also measure their success by the size of their org. For a typical software engineering company with ~1000 employees, the 10x gain means reducing the size of the org by somewhere between 400-700 employees. That’s quite the drop in perceived status for the CEO. A miss on incentive alignment.

Let’s turn to middle managers. Middle management is a strange twilight zone where people are too junior to drive company strategy, but too senior to understand the engineering details. How do you rank yourself in this zone? Typically it’s what your boss and peers think of you. And the size of your team.

Say you have a team of 100. Then you adopt AI, get a 10x boost. And now you have a team of 10. And… no job. Another miss on the incentives.

Finally the ICs. Many people like writing code. They like the social aspect of work. Imagine you’re in a team of 10 and told to adopt AI. The maths isn’t complex. A 10x gain means 9 of you are no longer needed. So you’re on your own and no longer writing code. Why would you engage with that?

Now you may - rightly - question whether 10x is achievable across the board. Engineers have a lot of meetings and standups to go to. And in a human org that’s true. Brooks worked this out fifty years ago in The Mythical Man-Month: communication channels grow as n(n−1)/2, so a team of 10 has 45 channels of overhead. Three of those people are effectively there just for alignment.

But once you only have 1 or 2 people, the comms cost evaporates. As middle management is slashed there’s no need for monthly all-hands at all levels of the org.

Money

The other factor which gets scant attention is money. If I’m able to use AI to 10x myself, does my comp increase 10x?

Yes, we all know the answer to that.

The company isn’t even going to split the difference. At best, history suggests a ~20% bump and a new title. The company captures the rest.

Historically the most productive engineers have been underpaid relative to the least productive. Netflix even called this out in their original culture deck:

AI just widens this gap. A lot.

And at the other end of the scale what if the employee decides to capture all the gain for themself? Work Monday morning and kick back for the rest of the week? How much of that is already happening?

The incentives are misaligned.

Modelling

An interesting exercise - which you can do right now - is to take your current org chart and ask Claude what the AI-native equivalent version is.

I don’t have an org, so I got Claude to produce the org chart for a 1,000 head software engineering company - SoftCorp - from ~2019. And then we noodled over what it looks like in the modern world.

SoftCorp felt familiar; 46% of heads in engineering, 6-7 layers deep, a variety of baggage collected over the years (co-founders parked as "strategic advisors", friends and family in surprisingly senior roles, parts of the org shared between CMO and CRO so both could claim the headcount).

Ask Claude what the "ai-native" version of this looks like and you are suddenly down to ~580 people. There are deep cuts in QA. In engineering. The support functions reduce as there is less to support. There are new AI specific teams - evaluating tools, orchestrating agents, trust & safety). The org is flatter; middle management is squeezed out. There are 3-4 layers.

Now you might argue, as some have, that you wouldn’t reduce the org size. Instead you’d suddenly be able to do more. But there are a few problems with that. First, say your org suddenly becomes able to do 2-3x more work. Can you really absorb that? Most orgs are short of resource. But not that short.

Then there’s the question of whether you have the right mix of people. The new world needs product management, architecture, co-ordination skills. Not detailed knowledge of Python syntax. Your org suddenly has the wrong skill-set. Engineers, despite what many leaders think, are not fungible.

And what does the new org look like? Probably small teams of 2-3 people, with product mgmt embedded in the eng team. The functional silos are gone. Just how do you transition from today’s org to this new world? Is it even possible?

Another interesting question to ask is what does the equivalent organisation, built from the ground up, look like. Claude suggests ~325 people. No functional silos. 3 layers. No legacy fat. As Claude put it: "greenfield orgs are flat because they haven’t had time to grow tumors."

Well, indeed.

And so?

If Claude had written this section it would open with "The uncomfortable truth"…

And, in this case, it’s probably right. The incentives don’t align - it’s no wonder legacy companies are finding it very hard to drive significant AI adoption. Increasingly I hear stories of companies resorting to a stick based approach: enforcing "minimum required AI usage." Which rather misses the structural issues preventing effective adoption.

But how many CEOs are brave enough - forward thinking enough - to remove 50% of their headcount? Jack Dorsey at Block did earlier this year. But he’s the exception; it seems the majority are like Claude and want to put some things off for as long as possible.

And what happens to those companies in the long run? When someone a third the size can genuinely compete? Undercut the incumbents on price. On quality. On agility. On function.

Maybe realizing how this plays out is the only incentive we need.