I've personally found this is where AI helps the most. I'm often building pretty sophisticated models that also need to scale, and nearly all SO/Google-able resources tend to be stuck at the level of "fit/predict" thinking that so many DS people remain limited to.
Being able to ask questions about non-trivial models as you build them, really diving into the details of exactly how certain performance improvements work and what trade offs there are, and even just getting feed back on your approach is a huge improvement in my ability to really land a solid understanding of the problem and my solution before writing a line of code.
Additionally, it's incredibly easy to make a simple mistake when modeling a complex problem and getting that immediate feedback is a kind of debugging you can otherwise only get on teams with multiple highly-skill people on them (which at a certain level is a luxury reserved only for people working a large companies).
For my kind of work, vibe-coding is laughably awful, primarily because there aren't tons of examples of large ML systems for the relatively unique problem you are often tasked with. But avoiding mistakes in the initial modeling process feels like a super power. On top of that, quickly being able to refactor early prototype code into real pipelines speeds up many of the most tedious parts of the process.
An individual consumer doesn't derive any benefit from companies missing out on automation opportunities.
Would you prefer to buy screws that are individually made on a lathe?
They weren’t cheap soups, but they sure were good.
A high end soup and an affordable soup might be serving two different markets.
I personally think a far more likely scenario is that small businesses of one or a few people become vastly more commonplace. They will be able to do a lot more by themselves, including with less expertise in areas they may not have a lot of knowledge in. I don't think regular employees today should see LLMs as competition, rather they should see it as a tool they can use to level the playing field against current CEOs.
I don't think that was your point but pressed screws got way better properties than cut screws.
There will not be a "quality" dial that you get to tweak to decide on your perfect quality of soup. There will be graduations, and you will be stuck with whatever the store provides. If you want medium quality soup, but the store only carries 3 brands of soup (because unlike in your utopia somebody actually has to maintain an inventory and relationships with their supply chain) and your favourite brand decides to bottom out their quality. It's not "good actually" because of economic whatever. Your soup just sucks now.
Oh but "the market will eventually provide a new brand" is a terrible strategy when they start spicing the soup with lead to give it cinnamon flavor or whatever.
I'm not an ethereal being. I'm a human, I need it to be good now. Not in theory land.
- consolidation, such that there are only a few different choices of soup
- a race to the bottom in quality
- poisoning
These are all possibilities under our current system, and we have mechanisms (laws and market competition) which limit the extent to which they occur.
What is it about extreme automation technology that you think will increase these prevalence of these issues? By what mechanisms will these issues occur more frequently (rather than less frequently), as production technology becomes more capable?
Every time I do something I add another layer of AI automation/enhancement to my personal dev setup with the goal of trying to see how much I can extend my own ability to produce while delivering high quality projects.
I definitely wouldn't say I'm 10x of what I could do before across the board but a solid 2-3x average.
In some respects like testing, it's perhaps 10x because having proper test coverage is essential to being able to let agentic AI run by itself in a git worktree without fearing that it will fuck everything up.
I do dream of a scenario where I could have a company that's equivalent to 100 or 1000 people with just a small team of close friends and trusted coworkers that are all using this kind of tooling.
I think the feeling of small companies is just better and more intimate and suits me more than expanding and growing by hiring.
Can you give some examples? What’s worked well?
The more you can do to tell the AI what you want via a “code-lint-test” loop, the better the results.
So we get code coverage without all the effort, it works well for well defined problems that can be verified with test.
- Using AI code gen to make your own dev tools to automate tasks. Everything from "I need a make target to automate updating my staging and production config files when I make certain types of changes" or "make an ETL to clean up this dirty database" to "make a codegen tool to automatically generate library functions from the types I have defined" and "generate a polished CLI for this API for me"
- Using Tilt (tilt.dev) to automatically rebuild and live-reload software on a running Kubernetes cluster within seconds. Essentially, deploy-on-save.
- Much more expansive and robust integration test suites with output such that an AI agent can automatically run integration tests, read the errors and use them to iterate. And with some guidance it can write more tests based on a small set of examples. It's also been great at adding formatted messages to every test assertion to make failed tests easier to understand
- Using an editor where an AI agent has access to the language server, linter, etc. via diagnostics to automatically understand when it makes severe mistakes and fix them
A lot of this is traditional programming but sped up so that things that took hours a few years ago now take literally minutes.
I worry that messing with the AI is the equivalent of tweaking my colour schemes and choosing new fonts.
- anything with good enough adoption is good enough (unless I'm an SME to judge directly)
- build something with it before considering a switch
- they're similar enough that what I learn in one will transfer to others
- everything sucks compared with 2-3 years from now; switching between "sucks" and "sucks+" will look silly in retrospect
It's a bit simplified and idealized, but is actually fairly spot-on.
I have been using AI every day. Just today, I used ChatGPT to translate an app string into 5 languages.
[0] https://www.oneusefulthing.org/p/superhuman-what-can-ai-do-i...
I guess similar to my experience with the AI voice translation YouTube has, I’ve felt similar - I’d rather listen to the original voice but with translated subtitles than a fake voice.
What was useful, was that I could explain exactly what the context was, in both a technical and usability context, and it understood it enough to provide appropriate translations.
Yes. And the sites that gives me a poorly translated text (which may or may not be translated by ai) with no means to switch to English is an immediate back-button.
Usually, and especially technical articles, poor/unreadable translations are identifiable within a few words. If the text seems like it could be interesting, I spend more time searching for the in-english button then I spent reading the text.
Reverse there afaict, enterprise + defense tech are booming. AI means get to do a redo + extension of the code automation era. It's fairly obvious to buyers + investors this time around so don't even need to educate. Likewise, in gov/defense tech, palantir broke the dam, and most of our users there have an instinctive allergic reaction to palantir+xai, so pretty friendly.
I do think the trend of the tiny team is growing though and I think the real driver were the laysoffs and downsizings of 2023. People were skeptical if Twitter would survive Elon's massive staff cuts and technically the site has survived.
I think the era of the 2016-2020 empire building is coming to an end. Valuing a manager on their number of reports is now out of fashion and theres now no longer any reason to inflate team sizes.
This morning I used Claude 4 Sonnet to figure out how to build, package and ship a Docker container to GitHub Container Registry in 25 minutes start to finish. Without Claude's help I would expect that to take me a couple of hours at least... and there's a decent chance I would have got stuck on some minor point and given up in frustration.
Transcript: https://claude.ai/share/5f0e6547-a3e9-4252-98d0-56f3141c3694 - write-up: https://til.simonwillison.net/github/container-registry
So far from what I've experienced AI coding agents automate away the looking things up on SO part (mostly by violating OSS licenses on Github). But that part is only bad because the existing tools for doing that were intentionally enshitified.
My vote for the unintentionally funniest company name. I wonder if they were aware when the landed on it, or if they were so deep in the process that it was too late to change course when they realized what they had done.
AI ended up being a convenient excuse for big tech to justify their layoffs, but Twitter already painted a story about how bloated some organizations were. Now that there is no longer any status in having 9,001 reports the pendulum has swing the other way - it's now sexy to brag about how little people you employ.
Only if you squint. If you look at the quality of the site, it has suffered tremendously.
The biggest "fuck you" are phishers buying blue checkmarks and putting the face of the CEO and owner to shill scams. But you also have just extremely trash content and clickbaits consistently getting (probably botted) likes and appearing in the top of feeds. You open a political thread and somehow there's a reply of a bear driving a bicycle as the top response.
Twitter is dead, just waiting for someone to call it.
They were written before the advent of ChatGPT and LLMs in general, especially coding related ones, so the ceiling must be even greater now, and this is doubly true for technical founders, for LLMs aren't perfect and if your vibed code eventually breaks, you'll need to know how to fix it. But yes, in the future with agents doing work on your behalf, maybe your own work becomes less and less too.
Revenue per employee, to me, is an aside that distracts from the ideas presented.
Greg Isenberg has some of the best takes on this on X. He articulates the paradigm shift extremely well.. @gregisenberg — one example: https://x.com/gregisenberg/status/1936083456611561932?s=46)
Ahh yes, fantastic insights.
> Startups used to brag about valuations and venture capital. Now AI is making revenue per employee the new holy grail.
The corrected form is:
> Startups used to brag about valuations and venture capital. Now AI is making rate of revenue growth per employee the new holy grail.
Specifically, as with all growth capitalism, it is long-term irrelevant how much revenue each employee generates. The factor that is being measured is how much each employee increases the rate of growth of revenue. If a business is growing revenue at +5% YoY, then a worker that can increase that rate by 20% (to +6% YoY) is worth keeping; a worker that can only increase revenue by 5% contributed +0% YoY after the initial boost and will be replaced by automation, AI, etc. (This is also why tech won’t invest in technical debt: it may lower expenses, but those one-time efficiencies are typically irrelevant when increasing the rate of growth of income results in far more income than the costs of the debt.)
I've noticed across the board, they also spend A LOT of time getting all the data into LLMs so they can talk to them instead of just reading reports, like bro, you don't understand churn fundamentally, why are you looking at these numbers??
kjhughes•4h ago