7 ms·

I think it all boils down to, which is higher risk, using AI too much, or using AI too little? Right now I see the former as being hugely risky. Hallucinated b

• _se 8mo ago
  Very reasonable take. The fact that this is being downvoted really shows how poor HN's collective critical thinking has become. Silicon Valley is cannibalizing itself and it's pretty funny to watch from the outside with a clear head.
  • daxfohl 8mo ago
    I think it's like the California gold rush. Anybody and their brother can go out and dig, but the real money is in selling the shovels.
    • fao_ 8mo ago
      I don't think this is the case, because the AI companies are all just shuffling around the same 300 million or trillion to each other.
    • koolba 8mo ago
      More like they’re leasing away deeply discounted steam shovels at below market rates and somehow expecting to turn a profit doing so. The real profits are the companies selling them chips, fiber, and power.
      • t0mas88 8mo ago
        But the companies selling them chips are also their shareholders, so those are on the hook as well.
      • rienbdj 8mo ago
        A handful of start ups will find genuine use cases for these models with real business demand. It just won’t be another AI travel agent chat bot.
      • jazz9k 8mo ago
        If it's below market rates, the people using the shovels are the ones making a profit.
        • marcosdumay 8mo ago
          Shovels still have a defined cost, even if there's absolutely no gold there for one to find.
      • sidrag22 8mo ago
        Impossible to say right now... consider just the idea of reactive agentic workflows: test fails, agent is instantly triggered and response is passed off for review, or whatever, something along those lines. Thats staying power, suddenly that lease isnt a lease, its an ongoing cost for as long as that system exists. its gas.
• softwaredoug 8mo ago
  Even within AI coding how people use this varies wildly from one people trying to one shot apps to people being barely above tab completers. When people talk about this stuff they usually mean very different techniques. And last months way of doing it goes away in favor of a new technique. I think the best you can do now is try lots of different new ways of working keep an open mind
  • daxfohl 8mo ago
    Or just wait for things to settle. As fast as the field is moving, staying ahead of the game is probably high investment with little return, as the things you spend a ton of time honing today may be obsolete tomorrow, or simply built into existing products with much lower learning cost. Note, if staying on the bleeding edge is what excites you, by all means do. I'm just saying for people who don't feel that urge, there's probably no harm just waiting for stuff to standardize and slow down. Either approach is fine so long as you're pragmatic about it.
    • p1esk 8mo ago
      Interesting - what makes you think things will slow down?
      • lelanthran 8mo ago
        > Interesting - what makes you think things will slow down? Everything slows down eventually. What makes you think this won't?
      • wiseowise 8mo ago
        What makes you think they won’t? And even if they won’t, not wasting energy going through the churn is a winning strategy if eventually AI reads your mind to know what you want to do.
      • daxfohl 8mo ago
        Settle. Not necessarily slow down. We'll see people gravitate towards a few things, and those will become the standards. It's already started, with claude and codex, compared to the wild west situation a year ago. The closest parallel I can think of is javascript frameworks. The 2010s had a new framework out every week. Lots of people (somewhat including myself) wasted a ton of time trying to keep up with the churn, imagining that constantly being on the bleeding edge was somehow important. The smart ones just picked something reasonably mature and stuck with it. Eventually things coalesced around React. All that time trying to keep up with the churn added essentially no value.
• runarberg 8mo ago
  This is basically Pascal’s wager. However, unlike the original Pascal’s wager, yours actually sounds sound. Another good alike wager I remember is: “What if climate change is a hoax, and we invested in all this clean energy infrastructure for nothing”.
  • daxfohl 8mo ago
    Interesting analogy, but I'd say it's kind of the opposite. In the two you mentioned, the cost of inaction is extremely high, so they reach one conclusion, whereas here the argument is that the cost of inaction is pretty low, and reaches the opposite conclusion.
    • runarberg 8mo ago
      Indeed, another key difference with the climate change wager is that both the action and the consequences are global, whereas the OG wager and the AI wager are both about personal choice.
• mgraczyk 8mo ago
  Even if you believe that many are too far on one side now, you have to account for the fact that AI will get better rapidly. If you're not using it now you may end up lacking preparation when it becomes more valuable
  • daxfohl 8mo ago
    But as it gets better, it'll also get easier, be built into existing products you already use, etc. So I wouldn't worry too much about that aspect. If you enjoy tinkering, or really want to dive deep into fundamentals, that's one thing, but I wouldn't worry too much about "learning to use some tool", as fast as things are changing.
    • mgraczyk 8mo ago
      I don't think so. That's a good point but the capability has been outpacing people's ability to use it for a while and that will continue. Put another way, the ability to use AI became an important factor in overall software engineering ability this year, and as the year goes on the gap between the best and worst users or AI will widen faster because the models will outpace the harnesses
      • daxfohl 8mo ago
        I mean, right now "bleeding edge" is an autonomous agents system that spends a million dollars making an unbelievably bad browser prototype in a week. Very high effort and the results are jibberish. By the time these sorts of things are actually reliable, they'll be productized single-click installer apps on your network server, with a simple web interface to manage them. If you just mean, "hey you should learn to use the latest version of Claude Code", sure.
        • mgraczyk 8mo ago
          I mean that you should stay up to date and practiced on how to get the most out of models. Using harnesses like Claude code sure, but also knowing their strengths and weaknesses so you can learn when and how to delegate and take on more scope
• mprast 8mo ago
  It's very interesting to me how many people presume that if you don't learn how to vibecode now you'll never ever be able to catch up. If the models are constantly getting better, won't these tools be easier to use a year from now? Will model improvements not obviate all the byzantine prompting strategies we have to use today?
  • deleted 8mo ago
    [deleted]
  • koolba 8mo ago
    And if you can never catch up, how would someone new to the game ever be a meaningful player?
    • eddythompson80 8mo ago
      If you’ve never driven a model T, how would you ever drive a corolla? If you never did angular 1, how would you ever learn react? If you never used UNIX 4, you’ll be behind in Linux today. /s
  • dns_snek 8mo ago
    I think so, that's why I think that the risk of pretty much ignoring the space is close to zero. If I happen to be catastrophically wrong about everything then any AI skills I would've learned today will be completely useless 5 years from now anyway, just like skills from early days of ChatGPT are completely useless today.
  • wiseowise 8mo ago
    FOMO is hell of a drug.
  • gerdesj 8mo ago
    Wait around five years and then prompt: "Vibe me Windows" and then install your smart new double glazed floor. There is definitely something useful happening in LLM land but it is not and will never be AGI. Oooh, let me dive in with an analogy: Screwdriver. Metal screws needed inventing first - they augment or replace dowels, nails, glue, "joints" (think tenon/dovetail etc), nuts and bolts and many more fixings. Early screws were simply slotted. PH (Philips cross head) and PZ (Pozidrive) came rather later. All of these require quite a lot of wrist effort. If you have ever screwed a few 100 screws in a session then you know it is quite an effort. Drill driver. I'm not talking about one of those electric screw driver thingies but say a De W or Maq or whatever jobbies. They will have a Li-ion battery and have a chuck capable of holding something like a 10mm shank, round or hex. It'll have around 15 torque settings, two or three speed settings, drill and hammer drill settings. Usually you have two - one to drill and one to drive. I have one that will seriously wrench your wrist if you allow it to. You need to know how to use your legs or whatever to block the handle from spinning when the torque gets a bit much. ... You can use a modern drill driver to deploy a small screw (PZ1, 2.5mm) to a PZ3 20+cm effort. It can also drill with a long auger bit or hammer drill up to around 20mm and 400mm deep. All jolly exciting. I still use an "old school" screwdriver or twenty. There are times when you need to feel the screw (without deploying an inadvertent double entendre). I do find the new search engines very useful. I will always put up with some mild hallucinations to avoid social.microsoft and nerd.linux.bollocks and the like.
• wavemode 8mo ago
  > I think it all boils down to, which is higher risk, using AI too much, or using AI too little? This framing is exactly how lots of people in the industry are thinking about AI right now, but I think it's wrong. The way to adopt new science, new technology, new anything really, has always been that you validate it for small use cases, then expand usage from there. Test on mice, test in clinical trials, then go to market. There's no need to speculate about "too much" or "too little" usage. The right amount of usage is knowable - it's the amount which you've validated will actually work for your use case, in your industry, for your product and business. The fact that AI discourse has devolved into a Pascal's Wager is saddening to see. And when people frame it this way in earnest, 100% of the time they're trying to sell me something.
  • paulryanrogers 8mo ago
    Those of us working from the bottom, looking up, do tend to take the clinical progressive approach. Our focus is on the next ticket. My theory is that executives must be so focused on the future that they develop a (hopefully) rational FOMO. After all, missing some industry shaking phenomenon could mean death. If that FOMO is justified then they've saved the company. If it's not, then maybe the budget suffers but the company survives. Unless of course they bet too hard on a fad, and the company may go down in flames or be eclipsed by competitors. Ideally there is a healthy tension between future looking bets and on-the-ground performance of new tools, techniques, etc.
    • krackers 8mo ago
      >must be so focused on the future They're focused no the short-term future, not the long-term future. So if everyone else adopts AI but you don't and the stock price suffers because of that (merely because of the "perception" that your company has fallen behind affecting market value), then that is an issue. There's no true long-term planning at play, otherwise you wouldn't have obvious copypcat behavior amongst CEOs such as pandemic overhiring.
      • charcircuit 8mo ago
• zozbot234 8mo ago
  > I think it all boils down to, which is higher risk, using AI too much, or using AI too little? It's both. It's using the AI too much to code, and too little to write detailed plans of what you're going to code. The planning stage is by far the easiest to fix if the AI goes off track (it's just writing some notes in plain English) so there is a slot-machine-like intermittent reinforcement to it ("will it get everything right with one shot?") but it's quite benign by comparison with trying to audit and fix slop code.
• rainmaking 8mo ago
  > my best ideas often happen when knee deep in some codebase I notice that I get into this automatically during AI-assisted coding sessions if I don't lower my standards for the code. Eventually, I need to interact very closely with both the AI and the code, which feels similar to what you describe when coding manually. I also notice I'm fresher because I'm not using many brainscycles to do legwork- so maybe I'm actually getting into more situations where I'm getting good ideas because I'm tackling hard problems. So maybe the key to using AI and staying sharp is to refuse to sacrifice your good taste.
  • daxfohl 8mo ago
    Yeah, I get this too. Still, I think sometimes being forced to grind on something will spur the "oh wait" moment that leads to new ways of thinking about things. Whereas when the LLM is doing the grinding, you don't see it. You just get a final PR with only the answer to the problem at hand, and you miss the bigger opportunity. That said, maybe it's not a big deal. Kind of like way back when I moved from C++ to GC code, I remember I missed memory leaks, because having it all automatically taken care of for free felt like giving up control and encouraging of lazy practices and loose ends. Turns out it wasn't really a big deal at all.
• otabdeveloper4 8mo ago
  > you may be less productive than optimal There is zero evidence that LLM's improve software developer productivity. Any data-driven attempts to measure this give ambivalent results at best.
• JoshuaDavid 8mo ago
  It definitely comes up if you're just reviewing an already-"completed" PR. Even if you're not going to ship AI-generated code to prod (and I think that's a reasonable choice), it's often informative to give a high-level description of what you want to accomplish to a coding agent and see what it does in your codebase. You might find that the AI covered a particular edge case that you would have missed. You might find that even if the PR as a whole is slop.
• deleted 8mo ago
  [deleted]
• kody 8mo ago
  Coaxed into dead-end architecture is the exact issue I have had when trying agentic coding. I find that I have the greatest success when I plan everything out and document the implementation plan as precisely as possible before handing it off to the agent. At which point, the hard part is already done. Generating the code was not really the bottleneck. Using LLMs to generate documentation for the code that I write, explaining data sheets to me, and writing boilerplate code does save me a lot of time, though.
• firemelt 8mo ago
  using ai too little is never wrong I think