> But you can't talk to them about the flow of the code. You can't ask them for their thinking as to why certain things are.

You can absolutely do this. It's even right most of the time.

Literally in the middle of ripping apart a vibe coded mess at work to figure out what's even worth keeping. Not fun :(

It's so fucking bad. I'm watching a team try to maintain a huge dashboard/control application that interfaces with a large amount of hardware using solely AI workflows.

Literally nothing works, all the timers/time counters are different across the pages, constantly commands hardware to do stupid shit, breaks during critical moments/in front of clients.

Eventually mgmt had to institute change freezes for high profile events because the team was breaking too much shit all the time.

The average C suite dipshit doesn't realize that the performance drops off a cliff once your project is more than some fraction of the context window so they will make pretty dashboards all day long but once you need to cover all the edge cases of a real system it all explodes.

AI isn't trained on the type of software style we'll need to create systems using AI, it's trained on how we used to write software. It doesn't reuse code or elegantly structure annoying, it just adds more code until the thing builds and passes some fake tests, even if half of it is functionally dead/unused.

Can you name a service that charged companies thousands/seat/month that turned out to be almost or completely useless? There's lots of random services sold to corporates that are not very useful (all the random benefits besides health care, life insurance, and other big-ticket items), but the per-seat charge of those is much smaller.

It's just silly to claim it has zero correlation.

The massive PRs is something that probably has to end. You can ai generate smaller changes in reviewable PR sizes. It probably even helps the AI code review tools to break the work in to smaller logical chunks too.

Oh, it won't get any better. LLMs already trained on every bit of code ever published, they won't get any more material.

> It might keep at that level, increase even more, or drop down

Bold prediction. :)

I think anyone predicting a drop or near-term flattening is not thinking beyond the online bubbles where these tools are discussed. In a local tech meetup a lot of the normal companies are barely coming online with AI tools at their company, and even then with very low limits.

I can see a corporate future where tokens are haggled over in department budgets just like any other line item. Some projects will get more of them, other projects will get less of them. "Use AI for everything" will become "use AI economically and build things that outlast our budget for it."

So it might either go up, stay the same, or go down? :)

"as most arguments don't apply to today's world" makes me want to roll my eyes so hard at you. The vast majority of problems we had with building complicated systems are all still just sitting there. People are speedrunning relearning things we've known about software engineering for decades.

The more things change, the more they stay the same.

What's an int vs a float vs a boolean? What's a function? What's a class? What's a variable? You don't actually need to know the answer to those questions in order to vibe code. That's a lot of priors to update!

> "unlimited tokens for every employees we don't even care if it actually ends up being a net positive financially"

That was clearly a short-term trend that would obviously get fixed. Doesn't say much about AI coding as a business model.

No disagreement on computing 2.0, but companies spending 3-5k per employee for hardware isn't generally a monthly cost. It's a at the time of hire, and then once every 3 to 5 years after that, for a monthly amortized cost of about $50/employee.

I have my concerns with current inference pricing in that there's a non-zero possibility for a rug pull in the future for the subscription plans for organizations and individuals that can still use them. For now, its only companies larger than ~150 users that need to pay per token, but what if that wasn't the case? Not every company can afford over $1k/month/employee to give them access to AI tooling, further making it harder to compete against the behemoths. If we get to a point where an individual can no longer pay $100/month for nearly unlimited usage and instead must pay per token, that's going to be a problem.

Personal computing eventually became an equalizer (until we started centralizing on mainframes again, aka the cloud) because it got cheap. My hope is that inference also gets just as, if not cheaper.

I have high hopes for local AI and open weight models and we will continue the ethos of local, personal computing and not needing to offload everything to OpenAI/Anthropic/Google, etc. to get work done once the hardware and hardware availability catch up.

The Dotcom bubble is an interesting comparison.

The general thrust that everything would be online was correct, it was just that the market mistimed and misallocated of capital by a decade or more. There was massive spending on infrastructure capacity that we wouldn't end up needing until the 2010s. There were hype driven valuations completely disconnected from business fundamentals just because a company was an 'internet' company. Things were going from cutting edge to obsolete in less than a year. There were breathless promises that this was business 2.0! Of course, none of that sounds remotely like what is going on today...

I'm optimistic about AI, but I also don't think that it is going to change everything as fast as promised.

Two things can be true at the same time. It can be true that this is here to stay. It can also be true that companies are grossly overvalued right now and that the market is irrationally exuberant. This would mean we could both have a crash and also see AI coding be the new future.

I think the right comparison is the invention of the microprocessor. At that time people were grappling with a lot of the same things we are today - would it automate jobs away, would it transform education and the work place, etc.

Hardware's not generally a subscription, monthly cost though.

You update it for them every 3/4 years (if they're lucky).

It probably makes a bit more sense to compare it to existing software subscriptions like Office, or the old-school 'per-seat' licenses per user for software.

I don't think it is unreasonable to say both will happen, is it?

In the long term, tokens will fall in price. Obviously. (If "tokens" continues to be the unit)

In the short to medium term, for the IPOs to succeed, people have to start actually paying for what they are using, so the price will go up, and is going up, quite a lot. Once their value is set they will slowly fall from that point (or some point maybe halfway, depending on how much the market is willing to continue to subsidise).

I am an AI cynic, but I am now an informed cynic; I am learning agentic tools so I know where they are useful and I know my enemy.

I think the "fad" here is cloud-based, metered AI being a dominant work mode.

Nothing, so far, has suggested to me that any other outcome is likely than edge- to local-scale, on-device, on-laptop, on-prem models getting good enough to the point where people use them by default and use the cloud models only when they need the extra oomph.

I cannot believe that there is anything other than an enormous incentive for companies like Uber to find local, small model and on-premises solutions to their problems, not least while pricing is so changeable and people are getting nasty surprises.

Betting on OpenAI and Anthropic being around over the long term in the form that they are now, that feels like valley hopium. Utility monopolies essentially always derive from physical/geograpical limitations, don't they?