mgdev
The ZIRP era was better for us than we deserved, which has made the recent “creative destruction” caused by AI feel so much worse.
The problem is most people don't know what makes them successful from a people perspective, except in the coarsest terms.
90% of the business outcomes are delivered by 10% of the people. The problem is they don't know which people. Therefore they also don't know which processes, if automated or augmented with AI, will achieve the target outcomes.
So instead they’re shotgunning. The problem is that the existing people-based system has built-in stabilizing mechanisms to prevent misalignment from taking the entire operation off course. AI stresses and often breaks those.
The result? Higher costs and chaos.
This needs to be treated like an org transformation. Traditionally that's most successfully done by starting a new small team that builds ground-up and eats the old team.
I'm also not saying Claude can't have their own terms.
But when government and private enterprise meet, we have problems.
The government—supposedly acting as and agent of the people but largely acting in the name of a more nebulous "national defense"—has the power to kingmake private companies that can act outside the normal checks and balances of those people, and in a way that distorts the normal market dynamics that would otherwise give people the power to vote with their dollar.
In other words, we lose the vote on both sides.
> Free markets trend torwards monopolies
Where did you pick this up? In a perfect market you'd see fewer monopolies, not more. Monopolies occur when there is some other force undermining meaningful competition.
In some rare cases, that can be e.g. unique talent or culture, which prevents other talent from cohering well enough to compete. (E.g. pacifists make poor arms dealers.)
But usually it's a) resources (e.g. geography for some scarce material) or—far more likely—it's b) distortion due to government intervention, or worse, c) corrupt government intervention.
I'm also not saying Claude can't have their own terms.
But when government and private enterprise meet, we have problems.
The government—supposedly acting as and agent of the people but largely acting in the name of a more nebulous "national defense"—has the power to kingmake private companies that can act outside the normal checks and balances of those people, and in a way that distorts the normal market dynamics that would otherwise give people the power to vote with their dollar.
In other words, we lose the vote on both sides.
They need to decide of AI is a technology or an ideology. We cannot afford to blend the two, or we're going to end up as some kind of perverted global theocracy.
1) humans (AI assisted or not) answering with real-money stakes, and
2) people betting on which humans are correct (borrow page from prediction markets)
This does four things simultaneously:
1) It introduces consequences for being wrong, which in turn introduces real asymmetric incentives to be contrarian-but-correct - which are high-value answers you don't get out of a stock model
2) It creates real-world incentives for human guided votes to answers (because, again, there's a real-world stake)
3) Together, this gets humans back on the platform answering questions, which creates meaningful exhaust data that can be sold back to the AI companies.
4) It creates incentives to game the system, which creates yet-more exhaust data for training anti-fraud AI systems
Full circle for Lisp as an AI language.
But models are climbing their way up the percentile charts.
90% of the software development in the work, by volume, can be done by people who are in the 50th percentile of skill.
So these model advances do meaningfully expand the amount of "useful" work that can be done without people. And simultaneously not be "good enough" for the specific kind of work you find yourself doing day to day.
Both can be true.
Agree! To me it comes down to intention and attention.
Depending on my *intention*, I should devote more or less of my *attention* to one thing vs another.
Another way to think about it is essential vs incidental attention, which I borrow from essential vs incidental complexity [0].
Many things require attention, or it doesn't perform correctly. Essential attention. Many, many more things don't require my full attention, and they'll still perform adequately. They satisfice.
Farm out the tasks that require only incidental attention.
[0]:https://github.com/papers-we-love/papers-we-love/blob/main/d...
AI helps you build more software in the same way that a C compiler helps you build more assembler.
I spend far more tokens on specification and verification than I do on actual feature-code generation.
And with it a small team of 3 developed something in just a few weeks that would have normally taken us 12 months. And we are selling it for multiple-seven figures to customers who (by all rights) should have been able to token-gen their way to the same outcome, but haven't.
So there is clearly some value in the people, and clearly some value in the AI. But I think people are misplacing/misattributing which goes where, in some cases.
I've rarely met a "craft" person that authors TLA+ models to find correctness issues in their systems. And yet as an outcome person I frequently ask my AI minions to do this and similar exercises.
What I think you're actually describing is competency using AI as a tool to drive effective and correct outcomes. That takes practice and systems - same as it ever was.
If you buy in, I think you'll find you can create great, correct systems... only more than you could ever do alone. Without N*N comms overhead. Without having to convince your louder, less competent teammate to actually finish their work.
Do craft people like outcomes? Sure. Can outcome people appreciate craft? Sure. But fundamentally, you typically fall more in one camp than the other.
And this assertion comes from the outcome people. Is it insane? No. To assert it's insane is insane, because it shows a fundamental disconnect from the economics of software development.
Which is about right, because the craft people don't do something primarily for the economics.
Problem is while we have a visceral feel for what the "value" of GitHub is to human users for human usage, I'm not sure we have a feel for what the value of the agentic equivalent is.
IMO it's probably very high on paper if you think through what kind of work is unlocked at agent speeds, provided you have a very capable work-execution fabric.
But since we're anchored to human pricing, whatever they propose will feel expensive.
Have you considered making a cloudy, managed version?
I hope they prove me wrong so I can buy Minimal Phone 3!
Not-JIRA + dark mode + usable APIs will take you far.
This is a 'tipping point' situation. Exodus will be a little at a time, then all at once.
Nothing precludes you from doing that with AI-gen code vs human-gen code. What you just described is downstream.
If you have a human authoring code, you re-roll every time they release a new version. AI just releases versions faster, and in response to different, faster-moving inputs.
Project model capabilities out a few years. Even if you only assume linear improvement at some point your risk-adjusted outcome lines cross each other and this becomes the preferred way of authoring code - code nobody but you ever sees.
Most enterprises already HATE adopting open source. They only do it because the economic benefit of free reuse has traditionally outweighed the risks.
If you need a parallel: we already do this today for JIT compilers. Everything is just getting pushed down a layer.
It also means that you need to extract enough value to cover the cost of said tokens, or reduce the economic benefit of finding exploits.
Reducing economic benefit largely comes down to reducing distribution (breadth) and reducing system privilege (depth).
One way to reduce distribution is to, raise the price.
Another is to make a worse product.
Naturally, less valuable software is not a desirable outcome. So either you reduce the cost of keeping open (by making closed), or increase the price to cover the cost of keeping open (which, again, also decreases distribution).
The economics of software are going to massively reconfigure in the coming years, open source most of all.
I suspect we'll see more 'open spec' software, with actual source generated on-demand (or near to it) by models. Then all the security and governance will happen at the model layer.