Because very few knows how to use AI. I teach AI courses on the side. I've done auditing supervised fine tuning and RLHF projects for a major provider. From seeing real prompts, many specifically from people who work with agents every day, people do not yet have the faintest clue how to productively prompt AI. A lot of people prompt them in ways that are barely coherent.
Even if models stopped improving today, it'd take years before we see the full effects of people slowly gaining the skills needed to leverage them.
You'd be surprised how low the bar is. What I'm seeing is down to the level of people not writing complete sentences.
There doesn't need to be any "magic" there. Just clearly state your requirements. And start by asking the model to plan out the changes and write a markdown file with a plan first (I prefer this over e.g. Claude Code's plan mode, because I like to keep that artefact), including planning out tests.
If a colleague of yours not intimately familiar with the project could get the plan without needing to ask followup questions (but able to spend time digging through the code), you've done pretty well.
You can go over-board with agents to assist in reviewing the code, running tests etc. as well, but that's the second 90%. The first 90% is just to write a coherent request for a plan, read the plan, ask for revisions until it makes sense, and tell it to implement it.
Not surprising. Many folks struggle with writing (hence why ChatGPT is so popular for writing stuff), so people struggling to coherently express what they want and how makes sense.
But the big models have come a long way in this regard. Claude + Opus especially. You can build something with a super small prompt and keep hammering it with fix prompts until you get what you want. It's not efficient, but it's doable, and it's much better than having to write a full spec not half a year ago.
This is exactly it. A lot of people use it that way. And it's still a vast improvement, but they could also generally do a lot better with some training. I think this is one of the areas where you'll unfortunately see a big gap developing between developers who do this well, and have the models work undisturbed for longer and longer while doing other stuff, and those who ends up needing a lot more rework than necessary.
> Claude + Opus especially. You can build something with a super small prompt and keep hammering it with fix prompts until you get what you want.
LOL: especially with Claude this was only in 1 out of 10 cases?
Claude output is usually (near) production ready on the first prompt if you precisely describe where you are, what you want and how you get it and what the result should be.
You're right, they should know better, but I think a lot of them have gotten away with it because most of them are not expected to produce written material setting out missing assumptions etc. and breaking down the task into more detail before proceeding to work, so a lot have never gotten the practice.
Once people have had the experience of being a lead and having to pass tasks to other developers a few times, most seem to develop this skill at least to a basic level, but even then it's often informal and they don't get enough practice documenting the details in one go, say by improving a ticket.
Because ai doesnt work like this “make me money” or “make stardew valley in space”. The hard part is the painful exploration and necessary taste to produce something useful. The number of these kind of people did not increase with ai.
Eg, ai is a big multiplier but that doesnt mean it will translate to “more” in the way people think.
It doesn’t need to be useful or a good game to launch on steam. Surely if it was a “big multiplier” 5-10x, it would be noticeably impacting steam launches.
Now if it’s something closer to 20%, we’re seeing exactly what you’d expect.
It comes down back to that whole discussion around intelligence becoming cheaper and more accessible but motivation and agency remaining stable.
I’ve worked with a few folks who have been given AI tools (like a designer who never coded in his life, a or video/content creator) who have absolutely taken off with creating web apps and various little tools and process improvements for themselves thanks by just vibecoding what they wanted. The key with both these individuals is high agency, curiosity, and motivation. That was innate, the AI tooling just gave them the external means to realise what they wanted to do with more ease.
These kinds of folks are not the majority, and we’re still early into this technological revolution imo (models are improving on a regular basis).
In summary, we’ve given the masses to “intelligence” but creativity and motivation stay the same.
Yeah but if it’s 100x easier to do something, it takes much less motivation to push through and finish it.
If you look at every game dev forum in existence, or you’ve ever talked to people about why they got into CS there are probably 1000x more people who want to publish a game than have done it.
If there was a a tool that provided a 10x-100x speed boost it would push enough of those people over the edge and make a significant impact on number of games released.
That’s to say nothing of boosting existing game devs.
My guess is that the true impact of this will be difficult to measure for a while. Most "single-person start-ups" will probably not be high-visibility VC-backed, YC affairs, and rather solopreneurs with a handful of niche moonlighted apps each making 3-4 digit monthly revenue.
95% of all new startups have the word AI in the description, so of course there are lots of new API wrappers and people trying to build off of existing models.
There aren’t noticeably more total startups or projects though.
Sorry, I swapped the numbers. It's actually 1447 this year vs 1413 last year so 34 more games this year. So essentially now growth. Despite there being a clearly accelerating growth trend since 2018.
Even if models stopped improving today, it'd take years before we see the full effects of people slowly gaining the skills needed to leverage them.