One area of business that I'm struggling in is now boring it is talking to an LLM, I enjoy standing at a whiteboard thinking through ideas, but more and more I see push for "talk to the llm, ask the llm, the llm will know" - The LLM will know, but I'd rather talk to a human about it. Also in pure business, it takes me too long to unlock nuances that an experienced human just knows, I have to do a lot of "yeah but" work, way way more than I would have to do with an experienced humans. I like LLMs and I push for their use, but I'm starting to find something here and I can't put my finger on what it is, I guess they're not wide enough to capture deep nuances? As a result, they seem pretty bad at understanding how a human will react to their ideas in practice.
andy99 · 3h ago
It's not quite the same but since the dawn of smartphones, I've hated it when you ask a question, as a discussion starter or to get people's views, and some jerk reads off the wikipedia answer as if that's some insight I didn't know was available to me, and basically ruins the discussion.
I know talking to an llm is not exactly parallel, but it's a similar idea, it's like talking to the guy with wikipedia instead of batting back and forth ideas and actually thinking about stuff.
seabombs · 44m ago
Some of my colleagues will copy/paste several paragraphs of LLM output into ongoing slack discussions. Totally interrupts the flow of ideas. Shits me to tears.
james_marks · 2h ago
This has peaked in my circles, thankfully. Now it’s considered a bit of a faux pas to look up an answer during a discussion, for exactly this reason.
skybrian · 2m ago
Is it a faux pas to say “I have no idea, ask Google?”
zxexz · 1h ago
In my personal social circle, it's but faux pas to even take out your phone during a discussion, without a statement why. Not explicitly a rule, it just kinda evolved that way. “Ok, I need to know, I’m gonna check wiki” is enough, and honestly makes everything more engaging — “oh! What does it say? How many times DID Cellini escape execution?. Bring up the talk page that should be fun!”
maest · 1h ago
Is it faux pas to google something too?
frakkingcylons · 1h ago
I think the faux pas is regurgitating the first hit you find on StackOverflow or Wikipedia without considering if actually adds something to a discussion.
avoutos · 50m ago
My experience has been similar. I can't escape the feeling that these LLMs are weighted down by their training data. Everything seems to me generically intelligent at best.
sheepscreek · 3h ago
I know what you mean. Also, the more niche your topic the more outright wrong LLMs tend to be. But for white-boarding or brainstorming - they can actually be pretty good. Just make sure you’re talking to a “large” model - avoid the minis and even “Flash” models like the plague. They’ve only ever disappointed me.
Adding another bit - the multi-modality brings them a step closer to us. Go ahead and use the physical whiteboard, then take a picture of it.
Probably just a matter of time before someone hooks up Excalidraw/Miro/Freeform into an LLM (MCPs FTW).
ricw · 3h ago
Just do both? Need an adequate network for that though which new school ai vibe entrepreneurs might lack…
neom · 3h ago
Both indeed. I'm older, I do consulting, often to the new school AI CEOs and they keep thinking I'm nuts for saying we should bring in this person to talk to about this thing...I've tried to explain to a few folks now a human would be much better in this loop, but I have no good way to prove it as it's just experience.
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??
baxtr · 1h ago
Ok wow this is actually frightening.
I talked to a friend recently who is a plastic surgeon. He told me about a young pretty girl came in recently with super clear ideas what she wanted to fixed.
Turns out she uploaded her pictures to an LLM and it gave her recommendations.
My friend told her she didn’t need any treatment at this stage but she kept insisting that the LLM had told her this and that.
I’m worried that these young folks just trust whatever these things tell them to do is right.
wyre · 40m ago
I was talking with Claude last night about making tempura, and it suggested if the oil is too hot I should add ice…
handfuloflight · 3h ago
With the LLM, you're free to ask any question without worrying about what the other party might think of you for asking that question.
andrew_lettuce · 2h ago
Which is the Hallmark of a great teammate, but then we won't need them anymore
handfuloflight · 2h ago
But who has truly stress tested their teammates to see how much level of question bombardment they're willing to take? You really haven't, since they can't just generate tokens at the rate and consistency of an LLM.
PixyMisa · 3h ago
Whereas with humans, you'll get valuable pushback for ideas that have already failed.
handfuloflight · 3h ago
The wisdom to know what to ask humans and what to ask the machine.
WXLCKNO · 7h ago
I'm working on a bunch of different projects trying out new stuff all the time for the past six months.
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.
charliebwrites · 7h ago
> 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
Can you give some examples? What’s worked well?
haiku2077 · 5h ago
- Extremely strict linting and formatting rules for every language you use in a project. Including JSON, YAML, SQL.
- 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.
RaftPeople · 2h ago
> make an ETL to clean up this dirty database
Can you provide concrete details?
When I do projects in this realm, it requires significant discussion with the business to understand how reality is modeled in the database and data, and that info is required before any notion of "clean up" can be defined.
haiku2077 · 1h ago
Yeah, you still do all of that domain research and requirements gathering and system design as your meatbag job. But now instead of writing the ETL code yourself by hand you can get 80-90% of the way there in a minute or two with AI assistance.
jprokay13 · 49m ago
Claude recommended I use Tilt for setting up a new project at work.
I wasn’t sure if it was worth it…is it pretty easy to set up a debugger? Not only do I have to adopt it, but I have to get a small team to be OK with it.
Our target deploy environment is K8S if that makes a difference. Right now I’m using mise tasks to run everything
Even things that took days or weeks are being done in minutes now. And a few hours on top to ensure correctness.
jay_kyburz · 5h ago
I worry that once I've done all that I won't have time for my actual work. I also have to investigate all these new AI editors, and sign up for the API's and work out which is best, then I have to learn how to prompt properly.
I worry that messing with the AI is the equivalent of tweaking my colour schemes and choosing new fonts.
haiku2077 · 1h ago
> I also have to investigate all these new AI editors, and sign up for the API's and work out which is best, then I have to learn how to prompt properly.
I found this didn't take me very long. Try things in order of how popular they seem and keep notes on what you do and don't like.
I personally settled on Zed (because I genuinely like the editor even with the AI bits turned off), Copilot (because Microsoft gave me a free subscription as an active OSS dev) and Claude Sonnet (seems to be a good balance). Other people I work with like Claude Code.
cartoonfoxes · 3h ago
Some of what I learned from a decade of keeping up with the perfusion of JS libraries and frameworks seems relevant to AI:
- 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
jprokay13 · 6h ago
If you haven’t, adding in strict(er) linting rules is an easy win.
Enforcing documentation for public methods is a great one imo.
The more you can do to tell the AI what you want via a “code-lint-test” loop, the better the results.
crgwbr · 4h ago
Honestly the same is true for human devs. As frustrating as strict linting can be for newer devs, it’s way less frustrating than having all the same issues pointed out in code review. That’s interesting because I’ve been finding that all sorts of stuff that’s good for AI is actually good for humans too, linting, fast easy to run tests, standardized code layouts, etc. Humans just have more ability to adapt to oddities at the moment, which leads to slack.
jprokay13 · 53m ago
Yeah I find it pretty funny that so much of us (myself included) threw out strict documentation practices because “the code should be self documenting!”
Now I want as much of it as I can get.
malux85 · 5h ago
For us it’s been auto-generating tests - we focus efforts on having the LLM write 1 test, manually verifying it. Then use this as context and tell the llm to extend to all space groups and crystal systems.
So we get code coverage without all the effort, it works well for well defined problems that can be verified with test.
ChrisMarshallNY · 4h ago
A while back, someone here linked to this story[0].
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.
Weblate has been doing that for any number of languages (up to 200 or however many it supports) for many years, using many different sources, including public translation memory reviewed by humans.
It can be plugged into your code forge and fully automated — you push the raw strings and get a PR with every new/modified string translated into every other language supported by your application.
I use its auto-translation feature to prepare quick and dirty translations into five languages, which lets you test right away and saves time for professional translators later — as they have told me.
If anyone is reading this, save yourself the time on AI bullshit and use Weblate — it's a FOSS project.
stephen_g · 4h ago
Hopefully it’s better for individual strings, but I’ve heard a few native speakers of other languages (who also can speak English) complaining about websites now serving up AI-translated versions of articles by default. They are better than Google Translate of old, but apparently still bad enough that they’d much rather just be served the English original…
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.
eCa · 3h ago
> they’d much rather just be served the English original
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.
ChrisMarshallNY · 4h ago
Exactly. I wouldn't use it for bulk translations. This was literally, 4 words.
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.
UPDATE: I went and verified it. The translation was absolutely perfect. Not sure what this means for translation services, but it certainly saved me several hundred dollars, and several days, just to add one label prompt to a free app.
ujkhsjkdhf234 · 5h ago
Instagram was 13 employees before they were purchased by Facebook. The secret is most employees in a 1000 person company don't need to be there or cover very niche cases that your company likely wouldn't have.
WJW · 3h ago
Don't fall for the lottery winner bias. Some companies just strike it rich, often for reasons entirely outside their control. That doesn't mean that copying their methods will lead to the same results.
ghaff · 1h ago
And for enterprise sales, you need a salesforce and many multi-billion companies have an enterprise salesforce. And documentation writers, and support staff, in multiple geographies, and events/marketing teams to support customers, etc.
teaearlgraycold · 6h ago
Definitely agree small teams are the way to go. The bigger the company the more cognitive dissonance is imposed on the employees. I need to work where everyone is forced to engage with reality and those that don’t are fired.
nemothekid · 7h ago
I'm not entirely convinced this trend is because AI is letting people "manage fleets of agents".
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.
simonw · 6h ago
I think the productivity improvement you can get just from having a decent LLM available to answer technical questions is significant enough already even without the whole Agent-based tool-in-a-loop thing.
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.
I'm not denying LLMs are useful. I believe the trend was going to happen whether regardless of how useful LLMs are.
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.
homebrewer · 2h ago
Their boilerplate works out of the box, you don't need to change anything. I recently packaged, signed, and published an OCI container into ghcr for the first time, it took about 5 to 10 minutes without touching any LLMs thanks to the quality of their documentation.
data-ottawa · 1h ago
Conceptually I find LLMs/AI broaden my skillset but slow down any processes that are deep in specific knowledge and context.
It is really nice to have that, it raises the floor on the skills I'm not good at.
jordanb · 6h ago
Eh I felt that way about the internet in 2010s. Seemed like virtually any question could be answered by a google query. People were making jokes that a programmer's job mostly consisted of looking things up on stack overflow. But then google started sucking and SO turned into another expertsexchange (which was itself good in the 2000s).
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.
tasty_freeze · 4h ago
> expertsexchange
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.
gedy · 2h ago
> Valuing a manager on their number of reports is now out of fashion
I highly doubt human nature has changed enough to say that. It's just a down market.
heraldgeezer · 3h ago
So I can't hide in the masses watching Netflix anymore?
TZubiri · 4h ago
"and technically the site has survived."
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.
Those are almost all management decisions, I expected a lot more crashes and security issues and a general inability to ship without taking things down.
spacemadness · 4h ago
I think we’re going to have to deal with the stories of shareholders wetting themselves over more layoffs more than we’re going to see higher quality software produced. Everyone is claiming huge productivity gains but generally software quality and new products being created seem at best unchanged. Where is all this new amazing software? It’s time to stop all the talk and show something. I don’t care that your SQL query was handled for you, thats not the bigger picture, that’s just talk.
delusional · 4h ago
This has been an industry wide problem at silicon valley for years now. For all their talks of changing the world, what we've gotten the last decade has been taxi and hotel apps. Nothing truly revolutionizing.
john2x · 3h ago
The revolution is happening at the top.
WJW · 3h ago
Top of what? It seems to be the same few people at the top for decades now.
zabzonk · 3h ago
And they are mostly sociopaths.
exclipy · 2h ago
I do see the AI agent companies shipping like crazy. Cursor, Windsurf, Claude Code... they are adding features as if they have some magical workforce of tireless AI minions building them. Maybe they do!
jmward01 · 3h ago
This may date me, but it feels like 1999 again where a small startup can disrupt an industry. Not just because of what LLMs can do in terms of delivered product, but because a small team can often turn on a problem so much faster than a big one can. I really hope that there are hundreds, if not thousands, of three to five person companies forming in basements right now ready to challenge the big players again.
geremiiah · 7h ago
AI helps you cook code faster, but you still need to have a good understanding of the code. Just because the writing part is done quicker doesn't mean a developer can now shoulder more responsibility. This will only lead to burn out, because the human mind can only handle so much responsibility.
crystal_revenge · 5h ago
> but you still need to have a good understanding of the code
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.
hnthrow90348765 · 5h ago
They often combine front end and back end roles (and sometimes sysadmin/devops/infrastructure) into one developer, so now I imagine they'll use AI to try and get even more. Burnout be damned, just going by their history.
satvikpendem · 6h ago
I read a few books the other day, The Million-dollar, One-person Business and Company of One. They both discuss how with the advances of code (to build a product with), the infrastructure to host them (with AWS so that you don't need to build data centers), and the network of people to sell to (the Internet in general, and more specifically social media, both organic and ads-based), the likelihood of running a large multi-million-dollar company all by yourself greatly increases in a way it has never done in the history of humanity before.
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.
lpa22 · 4h ago
AWS, GCP and other cloud providers play just as large of a role in allowing for tiny teams. Used to need an ops team of 10+ people to do all the stuff on premise that AWS can do
JaggerFoo · 6h ago
Some excellent ideas presented in the article. It doesn't matter if they all pan out, just that they expand our thinking into the realm of AI and its role in the future of business startups and operations.
Revenue per employee, to me, is an aside that distracts from the ideas presented.
apical_dendrite · 8h ago
When I worked at a startup that tried to maximize revenue per employee, it was an absolute disaster for the customer. There was zero investment in quality - no dedicated QA and everyone was way too busy to worry about quality until something became a crisis. Code reviews were actively discouraged because it took people off of their assigned work to review other people's work. Automated testing and tooling were minimal. If you go to the company's subreddit, you'll see daily posts of major problems and people threatening class-action lawsuits. There were major privacy and security issues that were just ignored.
raincole · 3h ago
So did revenue per employee increase?
golergka · 7h ago
Really depends on the type of business you're in. In the startup I work in, I worked almost entirely on quality of service for the last year, rarely ever on the new features — because users want to pay for reliability. If there's no investment in quality, then either the business is making a stupid decision and will pay for it, or users don't really care about it as much as you think.
ldjkfkdsjnv · 7h ago
Theres two types of software, the ones no one uses, and the ones people complain about
hackable_sand · 6h ago
Everyone should just write their own software then.
apical_dendrite · 6h ago
I've worked at a number of companies - the frequency and seriousness of customer issues was way beyond anything I've experienced anywhere else.
aussieguy1234 · 3h ago
" Do what you do best, and let AI do the rest".
Exactly the approach I'm taking with Tunnelmole, which as of right now is still a one person company with no investors.
I focused on coding, which I'm good at. I'm also reasonably good at content writing, I have some articles on Hackernoon before the age of AI.
So far AI has helped with
- Marketing ideas and strategies
- General advice on setting up a company
- Tax stuff, i.e what are my options for paying myself
- The logo. I used stable diffusion and an anime art model from CivitAI, had multiple candidates made, chose one, then did some minor touch ups in Gimp
I'm increasingly using it for more and more coding tasks as it gets better. I'll generally use it for anything repeatable and big refactors.
One of the biggest things coding wise working alone is Code Review. I don't have human colleagues at Tunnelmole who can review code for me. So I've gotten into the routine of having AI review all my changes. More than once, bugs have been prevented from being deployed to prod using this method.
ryandrake · 7h ago
It seems like a more and more recurring shareholder wet dream that companies could one day just be AI employees for digital things + robotic employees for physical things + maybe a human CEO "orchestrating" everything. No more icky employees siphoning off what should rightfully be profit for the owners. It's like this is some kind of moral imperative that business is always kind of low-key working towards. Are you rich and want to own something like a soup company? Just lease a fully-automated factory and a bunch of AI workers, and you're instantly shipping and making money! Is this capitalism's final end state?
taberiand · 19m ago
Final end state where it eats itself? Who buys all that soup when the workers have no jobs and no money? How much soup can one human CEO drink? (And why isn't the CEO also replaced by the AI?)
rahimnathwani · 7h ago
If I'm buying soup, I'd prefer the manufacturer, the retailer, and any other part of the supply chain to be as efficient as possible, so they can compete in the market to offer me soup of a given quality at the lowest possible cost.
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?
mikeocool · 7h ago
Personally, the best soups I’ve ever had were not made in kitchens that were optimized for efficiency or automation, they were optimized for quality.
They weren’t cheap soups, but they sure were good.
awb · 5h ago
Luxury goods and staple goods have distinct optimizations, both viable for generating profits and economic utility.
A high end soup and an affordable soup might be serving two different markets.
dyauspitr · 5h ago
Quality is a function of the ingredients used and the correct preparation. Neither of these things are something machines can’t do.
rightbyte · 4h ago
> Would you prefer to buy screws that are individually made on a lathe?
I don't think that was your point but pressed screws got way better properties than cut screws.
delusional · 4h ago
You're picturing a utopia at the limit of some idealized world. Try and take a second to return to planet earth.
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.
__MatrixMan__ · 1h ago
The wild thing is that they'll probably find entirely novel versions of this:
> they start spicing the soup with lead to give it cinnamon flavor or whatever
Like, we all know lead is bad, and we all know that humans are unscrupulous, but at least the human putting lead in the soup knows they're being unscrupulous at that time (probably). For an AI it would just be an entirely favorable optimization.
We're going to find out how far we can trust them, and the limits of that trust will determine where the people need to be.
rahimnathwani · 3h ago
You describe three potential undesirable outcomes:
- 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?
goatlover · 4h ago
I'd prefer not to live in a fully automated society where shareholders and CEOs reap all the profits while the rest of us scrape by on just enough UBI to prevent a revolution.
jack_h · 4h ago
I don't understand this scenario. If everyone is on UBI then most people are essentially near poverty. Where are these CEOs deriving all of their profit from?
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.
Hammershaft · 4h ago
Sounds unironically great if we could do it, the productivity improvements would allow dramatic improvements in living standards with moderate redistribution. I don't think this is where these llms are getting us.
mechagodzilla · 7h ago
If anyone can pay-as-you-go use a fully automated factory, and the factories are interchangeable, it seems like the value of capital is nearly zero in your envisioned future. Anyone with an idea for soup can start producing it with world class efficiency, prices for consumers should be low and variety should be sky-high.
No comments yet
timewizard · 2h ago
> "Ushering in a new era."
It's ushering in a new era of valley bullshit. If only journalists tried to falsify their premise before blindly publishing it.
> Jack Clark whether AI’s coding ability meant “the age of the nerds” was over.
When was the "age of the nerds" exactly? What does that even mean? My interpretation is that it means "is the age of having to pay skilled programmers for quality work over?" Which explains Bloomberg's interest.
> “I think it’s actually going to be the era of the manager nerds now,” Clark replied. “I think being able to manage fleets of AI agents and orchestrate them is going to make people incredibly powerful.”
And they're all going to be people on a subscription model and locked into one particular LLM. It's not going to make anyone powerful other than the owner class. This is the worst type of lie. They don't believe any of this. They just really really hate having to pay your salary increases every year.
> AI is sometimes described as providing the capability of “infinite interns.”
More like infinite autistic toddlers. Sure. It can somehow play a perfect copy of Chopin after hearing it once. Is that really where business value comes from? Quickly ripping other people off so you can profit first?
The Bloomberg class I'm sure is so thrilled they don't even have the sense to question any of this self serving propaganda.
ldjkfkdsjnv · 7h ago
I think this is the beginning of the end of early stage venture capital in b2b saas. Growth capital will still be there, but increasingly there will be no reason to raise. It will empower individuals with actual skill sets, rather than those with fancy schools on their resume
lmeyerov · 6h ago
I think bar for b2c, prosumer, SMB, yes.. folks want to see fast revenue growth (vs eyeballs & ideas)...
but enterprise... not as much, and that's half the b2b market
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.
dboreham · 7h ago
Like Johnny Depp in that movie..
noman-land · 4h ago
How come you put two dots instead of one or three?
khazhoux · 5h ago
You’re gonna have to be more specific
altairprime · 4h ago
The subhead makes a specific misstatement:
> 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.)
runako · 5h ago
AI gets top billing, but the assault via tax code on engineering employment is likely a bigger factor.
It’s true, especially with the “vibe” movement happening in real-time on X… “you can just do things” — I am building ai app layers b2c/b2b and while I do have an ml technical co-founder, I am largely scaling this with AI from strategy, visuals to coding. For example, with Claude created a framework for my company to scale, then built an AI powered dashboard in cursor around it as the command center. At scale we don’t need a team of more than ~5 to reach 7 fig MRR.
I know talking to an llm is not exactly parallel, but it's a similar idea, it's like talking to the guy with wikipedia instead of batting back and forth ideas and actually thinking about stuff.
Adding another bit - the multi-modality brings them a step closer to us. Go ahead and use the physical whiteboard, then take a picture of it.
Probably just a matter of time before someone hooks up Excalidraw/Miro/Freeform into an LLM (MCPs FTW).
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??
I talked to a friend recently who is a plastic surgeon. He told me about a young pretty girl came in recently with super clear ideas what she wanted to fixed.
Turns out she uploaded her pictures to an LLM and it gave her recommendations.
My friend told her she didn’t need any treatment at this stage but she kept insisting that the LLM had told her this and that.
I’m worried that these young folks just trust whatever these things tell them to do is right.
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?
- 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.
Can you provide concrete details?
When I do projects in this realm, it requires significant discussion with the business to understand how reality is modeled in the database and data, and that info is required before any notion of "clean up" can be defined.
Our target deploy environment is K8S if that makes a difference. Right now I’m using mise tasks to run everything
I worry that messing with the AI is the equivalent of tweaking my colour schemes and choosing new fonts.
I found this didn't take me very long. Try things in order of how popular they seem and keep notes on what you do and don't like.
I personally settled on Zed (because I genuinely like the editor even with the AI bits turned off), Copilot (because Microsoft gave me a free subscription as an active OSS dev) and Claude Sonnet (seems to be a good balance). Other people I work with like Claude Code.
- 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
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.
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...
It can be plugged into your code forge and fully automated — you push the raw strings and get a PR with every new/modified string translated into every other language supported by your application.
I use its auto-translation feature to prepare quick and dirty translations into five languages, which lets you test right away and saves time for professional translators later — as they have told me.
If anyone is reading this, save yourself the time on AI bullshit and use Weblate — it's a FOSS project.
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.
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.
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.
UPDATE: I went and verified it. The translation was absolutely perfect. Not sure what this means for translation services, but it certainly saved me several hundred dollars, and several days, just to add one label prompt to a free app.
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
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.
It is really nice to have that, it raises the floor on the skills I'm not good at.
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.
I highly doubt human nature has changed enough to say that. It's just a down market.
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.
https://www.twz.com/news-features/u-s-has-attacked-irans-nuc...
and see for yourself if Twitter is dead.
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.
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.
Exactly the approach I'm taking with Tunnelmole, which as of right now is still a one person company with no investors.
I focused on coding, which I'm good at. I'm also reasonably good at content writing, I have some articles on Hackernoon before the age of AI.
So far AI has helped with
- Marketing ideas and strategies
- General advice on setting up a company
- Tax stuff, i.e what are my options for paying myself
- The logo. I used stable diffusion and an anime art model from CivitAI, had multiple candidates made, chose one, then did some minor touch ups in Gimp
I'm increasingly using it for more and more coding tasks as it gets better. I'll generally use it for anything repeatable and big refactors.
One of the biggest things coding wise working alone is Code Review. I don't have human colleagues at Tunnelmole who can review code for me. So I've gotten into the routine of having AI review all my changes. More than once, bugs have been prevented from being deployed to prod using this method.
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 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.
> they start spicing the soup with lead to give it cinnamon flavor or whatever
Like, we all know lead is bad, and we all know that humans are unscrupulous, but at least the human putting lead in the soup knows they're being unscrupulous at that time (probably). For an AI it would just be an entirely favorable optimization.
We're going to find out how far we can trust them, and the limits of that trust will determine where the people need to be.
- 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?
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.
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It's ushering in a new era of valley bullshit. If only journalists tried to falsify their premise before blindly publishing it.
> Jack Clark whether AI’s coding ability meant “the age of the nerds” was over.
When was the "age of the nerds" exactly? What does that even mean? My interpretation is that it means "is the age of having to pay skilled programmers for quality work over?" Which explains Bloomberg's interest.
> “I think it’s actually going to be the era of the manager nerds now,” Clark replied. “I think being able to manage fleets of AI agents and orchestrate them is going to make people incredibly powerful.”
And they're all going to be people on a subscription model and locked into one particular LLM. It's not going to make anyone powerful other than the owner class. This is the worst type of lie. They don't believe any of this. They just really really hate having to pay your salary increases every year.
> AI is sometimes described as providing the capability of “infinite interns.”
More like infinite autistic toddlers. Sure. It can somehow play a perfect copy of Chopin after hearing it once. Is that really where business value comes from? Quickly ripping other people off so you can profit first?
The Bloomberg class I'm sure is so thrilled they don't even have the sense to question any of this self serving propaganda.
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.
> 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.)
https://news.ycombinator.com/item?id=44226145
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.