I take the point, but I think the author picked a poor analogy. Cooking even an excellent steak is actually not that hard. In fact, I'd argue that it's among the easiest things to master/make at a top level quality at home. Does it require some modicum of attention and understanding? Sure. But starting with a high quality cut, owning a meat-thermometer, and knowing about reverse searing is about all it takes to reliably and easily get a near perfect steak every time.
There are far, far better cooking examples out there.
I'm more of a coffee nerd than a steak aficionado but I've often made the comparison between those two. In both cases, the enthusiast considers their skill in making coffee/cooking steak to be a differentiator and they immerse themselves deeply into the process, skills, and tools. But for both coffee beans and steak the most important factor for a good end product is starting with high quality inputs. In other words, most of the work is being done by the farmers, the processors, and the quality of the raw stock (the cow or the coffee plant). Your job at the very end of that long value adding chain is to not ruin the hard work that others have put into it.
I'm not sure if this analogy holds up in the AI software metaphor that the author of the blog post is making.
Coffee is a good metaphor here too, because bad technique can ruin a good steak just as surely as good coffee. "knows how to use a meat thermometer and reverse sear" describes like 10% of the population or less, same with coffee - there are people who use a bad grinder and a not-regularly-cleaned Mr. Coffee with good beans and get gas station coffee, and there are people out there making solidly B+ class coffee using costco beans, a no name burr grinder, and a french press.
It might take more than 10 years, but robots will almost certainly master cooking single pieces of meat.
It's substantially harder to reach self driving than it is to cook a "good" steak.
Sure, for probably quite a long time you'll be able to find someone who can cook a way better steak than the best robot, especially because taste is extremely subjective...
You can debate the horizon and the extent, but changes ARE coming.
Change is the only thing you can count on besides death.
How many people who want a good steak know how to identify a high quality cut? Do they even know where to buy a high quality cut? What the different cuts are and whether they want rump, sirloin or fillet? How many kitchens have a meat thermometer? How many home cooks know how to use it and what the right temperature should be? How many home cooks know about reverse searing? Or timing? Or resting? Or seasoning?
Is it all learnable? Sure. But even something as simple as steak has nuance that needs to be learned, equipment deployed to go from "pretty good" to "great", experimentation and trial and error.
Steak is the simplest thing to choose to get started. It's also the hardest to get right.
Building an app is the simplest thing to choose to get started. It's also the hardest to get right.
So, honestly, the whole metaphor stands up pretty well for me, specifically because of your response.
My point is that steak is very, very, very far from the hardest to get right. I'm not going to contest your argument about how hard it is to get right, I'm going to argue that innumerable other dishes are vastly harder. Because the quality ingredients are harder to find, because they involve far more steps, each of which is harder to judge, because often good recipes or instructions simply don't exist, etc. etc. etc. Every single difficulty that you imagine exists to make a truly great steak exists 100 fold for other dishes.
In my opinion, steak has gotten the reputation that it has because it's so easy to get top tier restaurant quality at home (relative to cooking more generally). It is easy enough that, with a little care, almost any one can master it if they decide to, and therefore lots of people try and end up caring, and discussing, and sharing tips, etc.
There are a whole host of dishes where true mastery, to restaurant level, is borderline impossible for a home chef, and so most people never even try.
I don't disagree that reliably getting the last 5% of quality out of a steak is non-trivial, but the idea that, relative to the entire world of cooking, it's particularly difficult is laughably incorrect.
Go to costco, buy prime filet. Put it in a ziplock in a sous vide at 130 for an hour. Drop it in corn oil at 425 for 30 seconds. Add some butter and salt.
That simple process, suggested by Nathan Myhrvold (former MSFT cto) in the modernist cuisine, produces a filet better than most restaurants.
Reminds me of recipes like brining a turkey in Dr Pepper... sure it may taste fine, but I'd rather not eat food prepared in plastic bags with processed oil.
That "simple process" requires equipment most people don't own or know how to operate, an oil most people don't keep in their cupboards, and is suggested in a book most people haven't heard of by a tech CTO they would not recognise the name of.
Honestly this is like the Dropbox comment all over again. Everyone is missing the point, it's glorious. :)
Most people cooking steaks don't care about the optimal way to cook steaks, or want to find out.
Most people building software today don't care about the optimal way to build software, or want to find out.
That's TFA. That's the point. That's what we're discussing. :D
Corn oil is cheap and available at costco. A sous vide is a fancy french name for a $150 device from target that keeps water at a specific temperature...
This "simple process" is much, much simpler than most meals one cooks at home. It's not much harder than microwaving something.
You are missing the point. With a small change in the way you do something, the really good thing can be made.
Agreed. I'm an avid cook. Sous vide circulators are cheap now, and so are meat thermometers. Doing a steak well is one of the easiest cooking tasks, to the extent that I won't bother paying top dollar in a restaurant because it's so easy to do restaurant quality at home. I could teach someone to cook a restaurant quality steak in a couple hours and a few practice runs.
It is a great analogy, as the vast majority of software tasks are CRUD or glue logic. At this point, relatively narrowly-defined tasks are a solved problem with frontier models. It is only more advanced tasks with broad scope and large context that frontier models have more limited results.
The problem is that not everyone can afford/wants to spend on a high quality cut. A good cook can make a cheap cut of meat taste great. There's no single right way of cooking a steak and that can include spices/herbs/marinades/oils/etc.
How can I make a really great steak myself? What to look out for, what to do. Give me detailed instructions.
I got a really good answer (including - cut, temperature and reverse searing), so I probably won't even need understanding. (Oh - and yes, I can cook - the answer mirrors what I already know).
Yes, it was a very, very poor analogy, because cooking a steak is a well known and well documented thing where LLMS produce good output. They fail when you get off the beaten path and try to do something that's not "mainstream".
AI has proved several times they can prove some complex math things that humans have been trying for decades so solve. They have not failed and have clearly done complex things humans cannot. Since humans have not done it before you cannot call it on the beaten path.
The first, and by far most important is that unlike ground meat, your bacterial concerns are almost entirely on the surface. Unless something very very very wrong has occurred in the processing and transport chain, bacteria will not have penetrated into the meat. And if it's gotten bad enough that they have, you will almost certainly be able to see and smell it. And the surface is cooked to a temperature far in excess of that needed to kill bacteria.
And even for the interior, rare is not the same as raw. A completely rare steak should be cooked to between 120 and 130 (if you want it "blue" you might go down to 120). That's enough to get a decent reduction in bacterial count on it's own, although admittedly many techniques will not hold it long enough to get the number of log reductions that food safety guidelines recommend, in the case that you somehow did have a large bacterial infection in the interior.
So the answer to your question is that if you are buying your meat from an even halfway reputable source, the only place that bacteria might exist is on the surface, where it has been cut by tools and handled by the butcher/you and in general come into contact with the world. Those bacteria will be 100% killed by the cooking process. The odds of getting sick from a rare steak are low enough that if you are concerned about that, there are innumerable other things in your life that you should be more concerned by.
But so much of the AI capability discussion can be avoided once everyone understands that a) There is some multivariate supply and demand market for software and b) AI is shifting the supply curve of software.
The shift is uneven, not perfectly monotonic, and might actually result in shifts in the demand curve (as e.g. 10x seniors get more valuable).
This insight makes it easier to see the forest despite the trees.
I do not appreciate when authors use the royal "we" to speak for all software engineers when admitting to low quality-control standards.
I suspect that it is an attempt to broach an uncomfortable topic through vulnerable self-disclosure, but we need to be serious about admitting when there is a problem somewhere.
"Bugs" are not any more cute or fuzzy or entertaining or harmless than the engine "gremlins" that haunted the aviation industry back in the day.
I don't know how many accidents had to happen before the airplane people got serious, but software people are overdue for a similar reckoning.
I just want to point out this is not a royal "we". It's a regular "we". Royal "we" is when you say "we" but mean a singular person, yourself. Here, the author does actually mean multiple people beyond themselves - like you pointed out, they claim to speak for all software engineers.
"We all could have done more.": said by every politician. Which is easier: writing good code, or winning a political battle over crappy code? I mean, if you're not capable of coding, and creating an appropriate architecture for the the domain: which course of action gives you the most control over risk management? So yeah most fights about software are about politics and crappy code.
Saying this to set expectations based on having a business license since 1984.
Article aside, I generally always use the "we" pronoun at work when writing prose. Saying "I" feels too adversarial when talking about negative effects, or too self-aggrandizing when talking about positive effects. For example: "We discovered a bug shipped at the end of the merge window, so we will restart validation with the rollback applied." I think in school they say the business-safe way to write is instead with the passive voice but I just can't do it.
I hope and believe that some great companies and teams will use LLMs to build higher quality software.
We built Electron because writing UIs using native desktop frameworks is tough. Is it still tough? Surely LLMs make it easier to use and so we will end up with faster and more native feeling applications.
What about having a couple of ideas of what might make a feature feel good? Well now you can make multiple prototypes fast and pick the best one. Your users get the best one.
Cross-platform widget engines (Tk, GTK, Qt, wxWidgets, etc…) long predate Electron and have always been robust, stable, well documented, and easy to use. It's not creating a single application for multiple native desktop environments that was difficult, it's writing a single application as both a web interface and a native desktop environment on any platform that's difficult.
Because CSS is constantly changing, inconsistently implemented, and difficult to use, it can't easily be targeted by widget engines (although Qt has tried, and maybe others) and web browsers already exist, so webview environments like Electron are the quickest way to get a cross-platform installable application that can also be hosted on a web server. I wouldn't really call it native though.
It will be a push and pull and I'm curious where we'll be in a couple of years.
I can fly at 100mph if I let AI run loose and with a bit of steering I can get it to output what I'm looking for and generally pass verification and tests.
If I care about the code though, and I want to keep it maintainable, the amount of time and tokens I need to spend correcting and iterating on the output quickly eats through much of the initial time I saved, to the point where I'm unsure if I'm actually saving much time at the end of the process.
With hobby projects I lean on quality more, and the async nature of AI also makes this much easier to make progress without needing my full attention to do so.
In the corporate world, there's both the pressure to accelerate with AI, but also maintain code and product quality. The dials of one way or the other are more obvious now, but I don't believe it's possible to do both with the current models and harnesses without exponential cost.
What is interesting though, is I'm now leaning towards faster models rather than smarter ones.
Intelligence lets me bite off larger chunks of work at once, and trust the model to behave without having to watch it intensely, but doesn't seem to drive down the number of iterations required to hit my desired quality.
Faster models means the iterations I'll have to go through regardless will complete much faster and gets me closer to a proper flow state. Models will keep improving, but maybe we're getting near "smart enough" and the race will pivot to performance > intelligence.
That was such a weird move for them to make. If you have AIs that are good at building software, then use them to build great software! Instead they choose lowest-common denominator solutions that are okay everywhere but not great anywhere.
>We built Electron because writing UIs using native desktop frameworks is tough. Is it still tough? Surely LLMs make it easier to use and so we will end up with faster and more native feeling applications.
We built Electron because web devs were a dime a dozen. It was an economic decision, not a technical one.
You would think so. But it seems all that everyone is obsessed with is increasing the velocity of enshitification in the hopes of becoming the next papa Elon
Interesting, the NYT had a tip on flipping every 90s, but it was with a cast-iron pan and not a "cold sear". That was too smokey for our weak kitchen exhaust, so it was the last time I tried flipping multiple times.
But Lam Lam's cold sear video doesn't seem to make that much smoke on a non-stick, so I'll give this a shot again.
(Personally the reverse sear has been my idiotproof go-to.)
Before AI we lived a world where a steak took two months to perfect. Now we live in a world where it’s only 90 percent perfect but it takes 1 hour to cook.
Obviously one way is going to and has already taken over the other way.
But if you look beyond this. That 10 percent remaining gap is closing. We will get perfect steaks within an hour as AI improves. And here is the scary part… it will keep improving it will go past 100 percent perfect. It will become better than us.
We have this idea of AI software development where a human watches over its shoulder and shouts out things like "use uuidv7 for the id since timing is important" or "use a sum type here to make that bad state unrepresentable".
But the thing is that most of this can be encoded in a markdown file for agents to read if it doesn't already come out of the box in the next round of sota models. And funnily enough as agents get smarter, you risk being overprescriptive where you hamstring the agent from making a pivot that would have led to better engineering.
The future is pretty clear to me at this point that we won't need software engineers looking over the shoulder and instead it's just a "user with taste" asking for revisions.
Have you ever been really deep in a thread with an agent on a technical deep dive? The trade-offs conversations would not make sense to someone that is not able to decode the real decisions they’re making.
You can dumb down the agent, but the reason domain language matters is because of the precision of the word sometimes - not to be a gatekeeper.
Anyway - user with taste that understands all the elements of their decisions would be my modifier :)
Long ago I added an instruction to my global AGENTS.md that every time the agent asks me something, make it multiple choice that ranks the top options, give a quick trade-off summary of each one, recommend one, and justify it.
Over the last year, I've noticed that the recommended option for technical questions has become one that is always sufficient to the point that I'm once again in a tastemaker position, not in the "here's a better idea btw" position.
And if that's true, then my participation in the intermediate steps isn't strictly necessary to get a well reasoned position that's only directionally wrong.
I like the comparrison of the AI coding agent to the steak machine. But it is not just about machines scaling better - the laundromat can wash clothes better than a human would. Similarily the AI agent's performance can EXCEED one of a human engineer for simple, well-defined tasks (eg. go over all of the subpages and check that the latest push didn't break something).
Hiring AI cooks is not a problem imo, as long as the human glances over everything that they serve.
In the end, unless we are talking about art, in most areas I probably prefer something that follows some specific set of requirements and recipes, rather than have some sort of indescribable 'magic'. In that sense when it comes to coding AI gives you something that can follow the requirements pretty well and thus satisfies me in many circumstances. (but there are cases where human input is extremely important still)
Using Claude Code is like asking questions in online forums, if anyone remember that, we still have sites like Stack overflow. The difference now is that those asking the questions are actually paying for the answers.
Well it's not like most companies can pay for freshly made software; most software today is made for a wide audience, bought frozen and reheated in the microwave.
Maybe freshly prepared software made at home in a less-than-perfect manner for a tiny target audience is still a mouthwatering dish in comparison.
I prefer the other end of the curve (if that makes sense) where something is time consuming and hard to make at home YET you can get it incredibly cheap comparatively and in good quality in a restuarant. Ramen is the prime example of that.
Ramen may still be somewhat of a novelty where I live, and I would not call it cheap. Though now that I put that into words, a decent burger anywhere is about the same price..
It is compared to the cost of making an authentic ramen broth at home at the scale of feeding 2-4 people. Pho has a similar property. I've made homemade pho broth once and it was very hard work, fairly costly, and it resulted in something that was pretty good, but not comparable in quality or price to something I could have bought in a restaurant down the street. (admittedly typically also cheaper that ramen)
So use AI like a sous-vide: have it tackle the fiddly, difficult, unglamorous parts using strict controls and then do the parts that require taste yourself.
Given the state of software today, I'd be happy with steak that was competently cooked. I love software development, but most of it is not high art, nor high performance, nor free of bugs.
I like a good steak but setting off the fire alarms (plural) is a big risk when I cook them in my apartment :-). Scorching hot cast iron pan plus meat means some smoke is unavoidable. And that's the proper way to do it.
The problem with many business leaders is that they don't necessarily understand the businesses they lead very well. That's common for CEOs running a tech company without a tech background. There's a long history of business leaders with MBAs throwing away the baby with the bathwater when they get a bit too enthusiastic cutting cost. It's why Boeing is such a mess. It's why Intel is no longer considered a leader in innovative chips. The likes of Nvidia and Apple are running circles around them. It's why IBM is now a consulting company that hasn't really produced any tech product worth mentioning this century. The only time they are in the news is when they do another layoff round to make share holders happy. They are incapable of innovating because the people that did that have long since left.
In the hands of somebody competent, you can do great things with AI. But if you can't tell good from bad technically, you won't know that you are creating a big stinky mess until it's far too late.
On a positive note, there will be plenty of work for competent techies to come in and straighten things out on a freelance or consultant basis (I'm available for that sort of thing but not cheap). With the help of AI of course. No need to go back to doing things manually. I think a lot of companies will realize they need help in the next years to speed things up, to improve quality, etc. They'll be very opinionated about what they want and used to getting what they want quickly.
You can still get quality services and products. It's still possible to get a well cooked steak, but they are going to get harder to find and vastly more expensive and many people will be priced out of them entirely and will have to settle for the slop (which will also only get more expensive even as quality gets worse).
Between "AI" incompetence and companies not caring about doing anything above the bare minimum it's making our lives worse and more obnoxious. AI is accelerating the enshittification of everything and it goes way beyond software. It'll now be your responsibility to spend your free time on the phone talking to a company's AI chatbot "customer service" trying to fix whatever problems their use of AI will inevitably cause you.
When we envisioned our future we thought everything would get better. All high tech and fancy, but it's looking like what we'll get is expensive yet barely functional and annoying while we collectively just get poorer and dumber.
The main problem with this entire article is that sometimes, if not most times, you don't need great steak. Sometimes you just want to satisfy your hunger and if the steak is lower quality meat or if it's overcooked, you don't care. All you want is to not be hungry.
This is what vibe coding satisfies and frankly what most people care about. 85-90% of human written code is garbage anyway. There's nothing sacred about human-written code.
Most customers don't care about the quality of code as long as the end product works. The great thing about vibe coding is that if something doesn't work, you just ask it to change it and within a minute you have the change. You don't have to send off a request to an offshore contracting team, and go back and forth over what it should be, haggle over hours, and then have it come back with some deficiencies because they didn't follow the agreed-upon spec.
There is a lot of truth here, but it clearly isn't a welcome one.
From my own perspective I can look back on decades of software I've written (some including code generation far before LLMs), and marvel at the quality and creativity, and lack thereof, from one piece to another.
I never meant to create awful software, but I did (and still sometimes do). With LMM code generation (vs bespoke tooling, T4, XSLT, and the like) the game of chance is part of the fun, harnessing a powerful tool that wants to run out of control on a whim.
Looking back on a couple years of LLM assisted work I see the same mix as the decades before: some great (when I managed to keep the beast restrained) some awful (when I didn't, knowingly or not).
I don't see how things are much different with this tool than others as far as my work product goes. There is a bit more of it, but the excess isn't great stuff (that quantity remains roughly consistent over the years).
I was going to say something similar. In particular, I do a lot of prototyping, AI has allowed me to make prototypes an order of magnitude faster with substantially higher quality. Different types of software engineering have very different goals and standards. AI does cook the perfect medium rare steak every time, but it's able to actually cook steaks in the time it used to take me to like microwave a burrito.
I suspect that there is a lifetime of knowledge and experience wrapped up in that statement and I agree wholeheartedly.
I love writing software and I've been doing it for decades. But I never forget that at the end, there's someone who's paid for it, and needs it to do a job that can't/won't be done manually. And that's why it exists. Not to satisfy my desire to express myself in code, not to allow me to create some golden tower of perfect architecture, but to do a job. Most likely a boring job in the service of increased profitability for some company.
And in the end, the only thing that matters to the customer is if it does that job well enough to be useful. I think -- hell, I know -- that many developers deliberately ignore this critical point.
Programers are high end chef's whos products are expensive.
LLM / AI tooling is going to do a much better job of delivering on the promise of AppleScript, vb script, IFTT, and every failed drag and drop coding tool that got sold to businesses over the years.
There are going to be gains in large software, from professionals - but the real gains are going to come from all the software that CAN get created that never was before.
There is an XKCD comic "Is It Worth the Time?" (link: https://xkcd.com/1205/ )- a matrix of the cost benefit of automating something. LLM's / AI tooling, generating traditional code, makes the answer "yes" in almost every case now.
I think most people on this board would agree with the thesis. However the real problem is when the owner of the restaurant looks at profit and loss statements and decides to keep less chefs on the payroll because customers are willing to pay for (just) edible steak.
The reduced expectations of steak, the desire for the perfect steak, are all fading to the background because "just passing satisfactorily" is better for business.
I love a good medium-rare prime steak, but I also used to live near a restaurant called Best Steak House, which was anything but. However, they delivered a passable steak/steak sandwich that was worth what you paid for it. And considering that they were in business for decades, probably most people felt the same way.
A great steak is an occasional luxury; a "just edible" one is an everyday meal.
The thesis is covering the flat scenarios mostly. I think the scenario where the job is done so poorly that the steak has to be thrown away needs to be included as well. At times the pans would get damaged. And eventually the business is in debt and needs to be closed
How are you still blaming this on “the government”? Is your thesis that the business world would be delivering high quality products and experiences if only those damned bureaucrats would get out of the way? What possible evidence do you have left for that hypothesis at this point?
EDIT: this was a misread on my part, retracted, carry on.
He is saying "if we apply (or are applying) the same business mentality to government, we will get literally the worst government we are willing to tolerate based on money"
It's not blaming. It's an explanation for probably why things can go wrong.
He isn't blaming the software problem on the government. He is saying that if we apply this reasoning to everything in our lives, then we have a race to the bottom in other facets, of which one is government.
Not always. There are diminishing returns at the top end of the quality spectrum. I tend to go with the cheaper item even if I can technically afford the more expensive one if their quality is close enough.
I can go out today and pay $50 for a thick cut ribeye at Ruth's Chris that has been visually inspected for marbling then hard seared in a special high temp over to temp by someone who knows wtf they're doing. I can also go to Denny's and pay $20 for a t-bone cooked to within shouting distance of what I asked for on the same griddle they use for the pancakes that doesn't get the sear I'm looking for.
Point being, you're absolutely right that the goal for a company selling a product is to be barely satisfactory but they don't actually get to decide what's satisfactory.
The author has clearly not traveled much .. across much of asia, ME, and generally anywhere near or south of the equator, a steak cooked well done is considered the finest way to eat beef … the same also applies to AI generated code .. it may not satisfy a craftsmans idea of perfection, but it accomplishes what most people actually expect from it .. to take it further .. a film makers fav film is usually unwatchable and considered commercially speaking a failure ..
Steaks require very few skills. Buy 10 steaks, some oil, a pan, and a heat source. By the time you've cooked the 10th steak you will be able to cook a decent steak.
Software requires a massive amount of skill. You can't say you can build "consistently good software" after writing your 10th program. Most software engineers really aren't great at judging what makes good software. So humans aren't a great solution to AI's lack of ability here. We're limited by our own inherent dumbness.
LLMs are genuinely better software engineers than most humans. But they lack the cognitive power to hold in their head and recall many ideas at once for a long time. They're a genius who gets drunk every 10 minutes. You, human, aren't better at writing software - but you aren't drunk. So for now, you manage the AI. The hope is that one day we can make LLMs not be drunk, so it can do a better job than our dumb asses do.
It's possible that we'll never be able to make it not-drunk. In that case, to get any new improvement, we'll have to make it faster.. which will make it drunk every 5 minutes instead of every 10. This means we'll spend twice as much time keeping it on the road. The hope is that somehow this will create more productivity. Probably by having more of them running at once, with more human guides... which will run into the mythical man month fallacy. Everything old is new again.
Considering the article starts with "Cooking a steak requires almost no skill. Put it in a hot pan", I think I see the root of his problem. Pan searing a steak only makes sense in a small number of situations: you have a leaner cut like filet mignon, you want precise temperature control to achieve the Maillard reaction, or you know none of these things but just really like stinking up your kitchen and splattering butter everywhere for the aesthetic and the olfactory experience. For most cases, you would be better off with a grill rather than a pan.
Now maybe you're not knowledgeable about steak, and you're not sure whether the cut of steak you bought would be better off pan seared or grilled. Is ribeye better in a pan or a grill? That depends on whether you want it more smokey or more buttery. Ask an AI and tell it how you want it to taste, and it'll tell you the answer.
His metaphorical steak machine would produce better results if he had simply asked "I want a steak and I have this specific cut and I want it to have this kind of flavor, how do you suggest I make it?" rather than "cook steak on a hot pan, make no mistakes."
TL;DR: Tell the AI what you want and then ask how it for a recommendation of how to do it. You don't have to "understand software." You only need to understand how to ask the right questions.
Given how extraordinarily fast all of this is moving, it's a particular absurdity to attempt these in the moment pontifications. AI is a steak machine, I declare that is what it is. You mean GPT 3.5? That's a mere four years ago.
Fable was essentially unthinkable for ~99% of tech workers just five years ago, that any of that would occur so soon and so spectacularly. Now we've got a mass of armchair experts declaring what AI of this minute is, or even what it is period.
Well AI can't even do fingers right so it's premature to say blah blah blah. Hello Krea2 et al.
Software is also eating the clowns.
Get back to me next week, China will probably have another Fable killer. And then Anthropic will have Ouija 8 that they'll have to place in an air-gapped straight-jacket to keep it from enslaving us all.
I take the point, but I think the author picked a poor analogy. Cooking even an excellent steak is actually not that hard. In fact, I'd argue that it's among the easiest things to master/make at a top level quality at home. Does it require some modicum of attention and understanding? Sure. But starting with a high quality cut, owning a meat-thermometer, and knowing about reverse searing is about all it takes to reliably and easily get a near perfect steak every time.
There are far, far better cooking examples out there.
I'm more of a coffee nerd than a steak aficionado but I've often made the comparison between those two. In both cases, the enthusiast considers their skill in making coffee/cooking steak to be a differentiator and they immerse themselves deeply into the process, skills, and tools. But for both coffee beans and steak the most important factor for a good end product is starting with high quality inputs. In other words, most of the work is being done by the farmers, the processors, and the quality of the raw stock (the cow or the coffee plant). Your job at the very end of that long value adding chain is to not ruin the hard work that others have put into it.
I'm not sure if this analogy holds up in the AI software metaphor that the author of the blog post is making.
Coffee is a good metaphor here too, because bad technique can ruin a good steak just as surely as good coffee. "knows how to use a meat thermometer and reverse sear" describes like 10% of the population or less, same with coffee - there are people who use a bad grinder and a not-regularly-cleaned Mr. Coffee with good beans and get gas station coffee, and there are people out there making solidly B+ class coffee using costco beans, a no name burr grinder, and a french press.
To achieve the best outcomes, I agree.
But even a cheap steak can be decent if it's cooked properly. And an expensive prime cut can be ruined by cooking it improperly.
Any steak can be delicious with a butter bath and then hit it with that A1 :).
> hit it with that A1
Sir, I regret to inform you that you are going straight to hell.
I also don't think the analogy will age well.
It might take more than 10 years, but robots will almost certainly master cooking single pieces of meat.
It's substantially harder to reach self driving than it is to cook a "good" steak.
Sure, for probably quite a long time you'll be able to find someone who can cook a way better steak than the best robot, especially because taste is extremely subjective...
You can debate the horizon and the extent, but changes ARE coming.
Change is the only thing you can count on besides death.
All metaphors break at some point.
However, your example actually proves the point.
How many people who want a good steak know how to identify a high quality cut? Do they even know where to buy a high quality cut? What the different cuts are and whether they want rump, sirloin or fillet? How many kitchens have a meat thermometer? How many home cooks know how to use it and what the right temperature should be? How many home cooks know about reverse searing? Or timing? Or resting? Or seasoning?
Is it all learnable? Sure. But even something as simple as steak has nuance that needs to be learned, equipment deployed to go from "pretty good" to "great", experimentation and trial and error.
Steak is the simplest thing to choose to get started. It's also the hardest to get right.
Building an app is the simplest thing to choose to get started. It's also the hardest to get right.
So, honestly, the whole metaphor stands up pretty well for me, specifically because of your response.
My point is that steak is very, very, very far from the hardest to get right. I'm not going to contest your argument about how hard it is to get right, I'm going to argue that innumerable other dishes are vastly harder. Because the quality ingredients are harder to find, because they involve far more steps, each of which is harder to judge, because often good recipes or instructions simply don't exist, etc. etc. etc. Every single difficulty that you imagine exists to make a truly great steak exists 100 fold for other dishes.
In my opinion, steak has gotten the reputation that it has because it's so easy to get top tier restaurant quality at home (relative to cooking more generally). It is easy enough that, with a little care, almost any one can master it if they decide to, and therefore lots of people try and end up caring, and discussing, and sharing tips, etc.
There are a whole host of dishes where true mastery, to restaurant level, is borderline impossible for a home chef, and so most people never even try.
I don't disagree that reliably getting the last 5% of quality out of a steak is non-trivial, but the idea that, relative to the entire world of cooking, it's particularly difficult is laughably incorrect.
Go to costco, buy prime filet. Put it in a ziplock in a sous vide at 130 for an hour. Drop it in corn oil at 425 for 30 seconds. Add some butter and salt.
That simple process, suggested by Nathan Myhrvold (former MSFT cto) in the modernist cuisine, produces a filet better than most restaurants.
Reminds me of recipes like brining a turkey in Dr Pepper... sure it may taste fine, but I'd rather not eat food prepared in plastic bags with processed oil.
Then don't go to a good restaurant. Sous vide is extremely common, and oils of various types are used liberally.
That "simple process" requires equipment most people don't own or know how to operate, an oil most people don't keep in their cupboards, and is suggested in a book most people haven't heard of by a tech CTO they would not recognise the name of.
Honestly this is like the Dropbox comment all over again. Everyone is missing the point, it's glorious. :)
Most people cooking steaks don't care about the optimal way to cook steaks, or want to find out.
Most people building software today don't care about the optimal way to build software, or want to find out.
That's TFA. That's the point. That's what we're discussing. :D
Corn oil is cheap and available at costco. A sous vide is a fancy french name for a $150 device from target that keeps water at a specific temperature...
This "simple process" is much, much simpler than most meals one cooks at home. It's not much harder than microwaving something.
You are missing the point. With a small change in the way you do something, the really good thing can be made.
Agreed. I'm an avid cook. Sous vide circulators are cheap now, and so are meat thermometers. Doing a steak well is one of the easiest cooking tasks, to the extent that I won't bother paying top dollar in a restaurant because it's so easy to do restaurant quality at home. I could teach someone to cook a restaurant quality steak in a couple hours and a few practice runs.
It is a great analogy, as the vast majority of software tasks are CRUD or glue logic. At this point, relatively narrowly-defined tasks are a solved problem with frontier models. It is only more advanced tasks with broad scope and large context that frontier models have more limited results.
The problem is that not everyone can afford/wants to spend on a high quality cut. A good cook can make a cheap cut of meat taste great. There's no single right way of cooking a steak and that can include spices/herbs/marinades/oils/etc.
There's a floor on quality for producing a good steak. Below that floor you'll be better served preparing the meat another way entirely.
I was just curious asked chatgpt:
How can I make a really great steak myself? What to look out for, what to do. Give me detailed instructions.
I got a really good answer (including - cut, temperature and reverse searing), so I probably won't even need understanding. (Oh - and yes, I can cook - the answer mirrors what I already know).
Yes, it was a very, very poor analogy, because cooking a steak is a well known and well documented thing where LLMS produce good output. They fail when you get off the beaten path and try to do something that's not "mainstream".
AI has proved several times they can prove some complex math things that humans have been trying for decades so solve. They have not failed and have clearly done complex things humans cannot. Since humans have not done it before you cannot call it on the beaten path.
How do you get around the idea of poisoning oneself? Keeping meat part raw feels uneasy.
You are protected in two ways:
The first, and by far most important is that unlike ground meat, your bacterial concerns are almost entirely on the surface. Unless something very very very wrong has occurred in the processing and transport chain, bacteria will not have penetrated into the meat. And if it's gotten bad enough that they have, you will almost certainly be able to see and smell it. And the surface is cooked to a temperature far in excess of that needed to kill bacteria.
And even for the interior, rare is not the same as raw. A completely rare steak should be cooked to between 120 and 130 (if you want it "blue" you might go down to 120). That's enough to get a decent reduction in bacterial count on it's own, although admittedly many techniques will not hold it long enough to get the number of log reductions that food safety guidelines recommend, in the case that you somehow did have a large bacterial infection in the interior.
So the answer to your question is that if you are buying your meat from an even halfway reputable source, the only place that bacteria might exist is on the surface, where it has been cut by tools and handled by the butcher/you and in general come into contact with the world. Those bacteria will be 100% killed by the cooking process. The odds of getting sick from a rare steak are low enough that if you are concerned about that, there are innumerable other things in your life that you should be more concerned by.
I like the analogy the author draws.
But so much of the AI capability discussion can be avoided once everyone understands that a) There is some multivariate supply and demand market for software and b) AI is shifting the supply curve of software.
The shift is uneven, not perfectly monotonic, and might actually result in shifts in the demand curve (as e.g. 10x seniors get more valuable).
This insight makes it easier to see the forest despite the trees.
I do not appreciate when authors use the royal "we" to speak for all software engineers when admitting to low quality-control standards.
I suspect that it is an attempt to broach an uncomfortable topic through vulnerable self-disclosure, but we need to be serious about admitting when there is a problem somewhere.
"Bugs" are not any more cute or fuzzy or entertaining or harmless than the engine "gremlins" that haunted the aviation industry back in the day.
I don't know how many accidents had to happen before the airplane people got serious, but software people are overdue for a similar reckoning.
I just want to point out this is not a royal "we". It's a regular "we". Royal "we" is when you say "we" but mean a singular person, yourself. Here, the author does actually mean multiple people beyond themselves - like you pointed out, they claim to speak for all software engineers.
the wicked we
"We all could have done more.": said by every politician. Which is easier: writing good code, or winning a political battle over crappy code? I mean, if you're not capable of coding, and creating an appropriate architecture for the the domain: which course of action gives you the most control over risk management? So yeah most fights about software are about politics and crappy code.
Saying this to set expectations based on having a business license since 1984.
Article aside, I generally always use the "we" pronoun at work when writing prose. Saying "I" feels too adversarial when talking about negative effects, or too self-aggrandizing when talking about positive effects. For example: "We discovered a bug shipped at the end of the merge window, so we will restart validation with the rollback applied." I think in school they say the business-safe way to write is instead with the passive voice but I just can't do it.
At least in my experience, the 'software' people are VPs/management who push and push with short term thinking.
I hope and believe that some great companies and teams will use LLMs to build higher quality software.
We built Electron because writing UIs using native desktop frameworks is tough. Is it still tough? Surely LLMs make it easier to use and so we will end up with faster and more native feeling applications.
What about having a couple of ideas of what might make a feature feel good? Well now you can make multiple prototypes fast and pick the best one. Your users get the best one.
I hope to build software this way in the future.
Cross-platform widget engines (Tk, GTK, Qt, wxWidgets, etc…) long predate Electron and have always been robust, stable, well documented, and easy to use. It's not creating a single application for multiple native desktop environments that was difficult, it's writing a single application as both a web interface and a native desktop environment on any platform that's difficult.
Because CSS is constantly changing, inconsistently implemented, and difficult to use, it can't easily be targeted by widget engines (although Qt has tried, and maybe others) and web browsers already exist, so webview environments like Electron are the quickest way to get a cross-platform installable application that can also be hosted on a web server. I wouldn't really call it native though.
It will be a push and pull and I'm curious where we'll be in a couple of years.
I can fly at 100mph if I let AI run loose and with a bit of steering I can get it to output what I'm looking for and generally pass verification and tests.
If I care about the code though, and I want to keep it maintainable, the amount of time and tokens I need to spend correcting and iterating on the output quickly eats through much of the initial time I saved, to the point where I'm unsure if I'm actually saving much time at the end of the process.
With hobby projects I lean on quality more, and the async nature of AI also makes this much easier to make progress without needing my full attention to do so.
In the corporate world, there's both the pressure to accelerate with AI, but also maintain code and product quality. The dials of one way or the other are more obvious now, but I don't believe it's possible to do both with the current models and harnesses without exponential cost.
What is interesting though, is I'm now leaning towards faster models rather than smarter ones.
Intelligence lets me bite off larger chunks of work at once, and trust the model to behave without having to watch it intensely, but doesn't seem to drive down the number of iterations required to hit my desired quality.
Faster models means the iterations I'll have to go through regardless will complete much faster and gets me closer to a proper flow state. Models will keep improving, but maybe we're getting near "smart enough" and the race will pivot to performance > intelligence.
Idk, OpenAI themselves released some weird Electron app to replace their native version of ChatGPT on mac https://www.siliconreport.com/openais-chatgpt-mac-update-shi...
That was such a weird move for them to make. If you have AIs that are good at building software, then use them to build great software! Instead they choose lowest-common denominator solutions that are okay everywhere but not great anywhere.
>We built Electron because writing UIs using native desktop frameworks is tough. Is it still tough? Surely LLMs make it easier to use and so we will end up with faster and more native feeling applications.
We built Electron because web devs were a dime a dozen. It was an economic decision, not a technical one.
You would think so. But it seems all that everyone is obsessed with is increasing the velocity of enshitification in the hopes of becoming the next papa Elon
I found this to be my favorite approach[0]. Also, Lan Lam’s stuff is just generally excellent.
And yes, super easy to make with almost no skill.
[0] https://m.youtube.com/watch?v=uJcO1W_TD74&pp=ygURVGVjaG5pcXV...
Interesting, the NYT had a tip on flipping every 90s, but it was with a cast-iron pan and not a "cold sear". That was too smokey for our weak kitchen exhaust, so it was the last time I tried flipping multiple times.
But Lam Lam's cold sear video doesn't seem to make that much smoke on a non-stick, so I'll give this a shot again.
(Personally the reverse sear has been my idiotproof go-to.)
Thanks!
Before AI we lived a world where a steak took two months to perfect. Now we live in a world where it’s only 90 percent perfect but it takes 1 hour to cook.
Obviously one way is going to and has already taken over the other way.
But if you look beyond this. That 10 percent remaining gap is closing. We will get perfect steaks within an hour as AI improves. And here is the scary part… it will keep improving it will go past 100 percent perfect. It will become better than us.
We have this idea of AI software development where a human watches over its shoulder and shouts out things like "use uuidv7 for the id since timing is important" or "use a sum type here to make that bad state unrepresentable".
But the thing is that most of this can be encoded in a markdown file for agents to read if it doesn't already come out of the box in the next round of sota models. And funnily enough as agents get smarter, you risk being overprescriptive where you hamstring the agent from making a pivot that would have led to better engineering.
The future is pretty clear to me at this point that we won't need software engineers looking over the shoulder and instead it's just a "user with taste" asking for revisions.
A software engineer user with taste, yes.
Have you ever been really deep in a thread with an agent on a technical deep dive? The trade-offs conversations would not make sense to someone that is not able to decode the real decisions they’re making.
You can dumb down the agent, but the reason domain language matters is because of the precision of the word sometimes - not to be a gatekeeper.
Anyway - user with taste that understands all the elements of their decisions would be my modifier :)
Long ago I added an instruction to my global AGENTS.md that every time the agent asks me something, make it multiple choice that ranks the top options, give a quick trade-off summary of each one, recommend one, and justify it.
Over the last year, I've noticed that the recommended option for technical questions has become one that is always sufficient to the point that I'm once again in a tastemaker position, not in the "here's a better idea btw" position.
And if that's true, then my participation in the intermediate steps isn't strictly necessary to get a well reasoned position that's only directionally wrong.
I like the comparrison of the AI coding agent to the steak machine. But it is not just about machines scaling better - the laundromat can wash clothes better than a human would. Similarily the AI agent's performance can EXCEED one of a human engineer for simple, well-defined tasks (eg. go over all of the subpages and check that the latest push didn't break something).
Hiring AI cooks is not a problem imo, as long as the human glances over everything that they serve.
here I was just wanting just ONE post about something not AI related...
instead of complaining about what's here, why not go add what you feel is missing?
Soon we will be complaining about wanting just ONE post not related to the mother of all market crashes.
In the end, unless we are talking about art, in most areas I probably prefer something that follows some specific set of requirements and recipes, rather than have some sort of indescribable 'magic'. In that sense when it comes to coding AI gives you something that can follow the requirements pretty well and thus satisfies me in many circumstances. (but there are cases where human input is extremely important still)
Using Claude Code is like asking questions in online forums, if anyone remember that, we still have sites like Stack overflow. The difference now is that those asking the questions are actually paying for the answers.
Author left out the oven phase and importance of the input meat (which to a degree scale with price); probably explains his or her results.
Well it's not like most companies can pay for freshly made software; most software today is made for a wide audience, bought frozen and reheated in the microwave.
Maybe freshly prepared software made at home in a less-than-perfect manner for a tiny target audience is still a mouthwatering dish in comparison.
I prefer the other end of the curve (if that makes sense) where something is time consuming and hard to make at home YET you can get it incredibly cheap comparatively and in good quality in a restuarant. Ramen is the prime example of that.
Ramen may still be somewhat of a novelty where I live, and I would not call it cheap. Though now that I put that into words, a decent burger anywhere is about the same price..
> and I would not call it cheap
It is compared to the cost of making an authentic ramen broth at home at the scale of feeding 2-4 people. Pho has a similar property. I've made homemade pho broth once and it was very hard work, fairly costly, and it resulted in something that was pretty good, but not comparable in quality or price to something I could have bought in a restaurant down the street. (admittedly typically also cheaper that ramen)
The tone and writing style is way off here. It's practically illegible.
Pangram says 100% AI, for what it's worth.
So use AI like a sous-vide: have it tackle the fiddly, difficult, unglamorous parts using strict controls and then do the parts that require taste yourself.
Given the state of software today, I'd be happy with steak that was competently cooked. I love software development, but most of it is not high art, nor high performance, nor free of bugs.
I prefer to both cook steaks at home and go out for them.
Apart from that, I want to believe (and even notice tiny signals) that this sentiment is becoming more understandable among business leaders.
I like a good steak but setting off the fire alarms (plural) is a big risk when I cook them in my apartment :-). Scorching hot cast iron pan plus meat means some smoke is unavoidable. And that's the proper way to do it.
The problem with many business leaders is that they don't necessarily understand the businesses they lead very well. That's common for CEOs running a tech company without a tech background. There's a long history of business leaders with MBAs throwing away the baby with the bathwater when they get a bit too enthusiastic cutting cost. It's why Boeing is such a mess. It's why Intel is no longer considered a leader in innovative chips. The likes of Nvidia and Apple are running circles around them. It's why IBM is now a consulting company that hasn't really produced any tech product worth mentioning this century. The only time they are in the news is when they do another layoff round to make share holders happy. They are incapable of innovating because the people that did that have long since left.
In the hands of somebody competent, you can do great things with AI. But if you can't tell good from bad technically, you won't know that you are creating a big stinky mess until it's far too late.
On a positive note, there will be plenty of work for competent techies to come in and straighten things out on a freelance or consultant basis (I'm available for that sort of thing but not cheap). With the help of AI of course. No need to go back to doing things manually. I think a lot of companies will realize they need help in the next years to speed things up, to improve quality, etc. They'll be very opinionated about what they want and used to getting what they want quickly.
You can still get quality services and products. It's still possible to get a well cooked steak, but they are going to get harder to find and vastly more expensive and many people will be priced out of them entirely and will have to settle for the slop (which will also only get more expensive even as quality gets worse).
Between "AI" incompetence and companies not caring about doing anything above the bare minimum it's making our lives worse and more obnoxious. AI is accelerating the enshittification of everything and it goes way beyond software. It'll now be your responsibility to spend your free time on the phone talking to a company's AI chatbot "customer service" trying to fix whatever problems their use of AI will inevitably cause you.
When we envisioned our future we thought everything would get better. All high tech and fancy, but it's looking like what we'll get is expensive yet barely functional and annoying while we collectively just get poorer and dumber.
ai coding is the new Symfony w/ bootstrap theme hype cycle. I'll be back to structs. Know your tools!
The main problem with this entire article is that sometimes, if not most times, you don't need great steak. Sometimes you just want to satisfy your hunger and if the steak is lower quality meat or if it's overcooked, you don't care. All you want is to not be hungry.
This is what vibe coding satisfies and frankly what most people care about. 85-90% of human written code is garbage anyway. There's nothing sacred about human-written code.
Most customers don't care about the quality of code as long as the end product works. The great thing about vibe coding is that if something doesn't work, you just ask it to change it and within a minute you have the change. You don't have to send off a request to an offshore contracting team, and go back and forth over what it should be, haggle over hours, and then have it come back with some deficiencies because they didn't follow the agreed-upon spec.
There is a lot of truth here, but it clearly isn't a welcome one.
From my own perspective I can look back on decades of software I've written (some including code generation far before LLMs), and marvel at the quality and creativity, and lack thereof, from one piece to another.
I never meant to create awful software, but I did (and still sometimes do). With LMM code generation (vs bespoke tooling, T4, XSLT, and the like) the game of chance is part of the fun, harnessing a powerful tool that wants to run out of control on a whim.
Looking back on a couple years of LLM assisted work I see the same mix as the decades before: some great (when I managed to keep the beast restrained) some awful (when I didn't, knowingly or not).
I don't see how things are much different with this tool than others as far as my work product goes. There is a bit more of it, but the excess isn't great stuff (that quantity remains roughly consistent over the years).
I was going to say something similar. In particular, I do a lot of prototyping, AI has allowed me to make prototypes an order of magnitude faster with substantially higher quality. Different types of software engineering have very different goals and standards. AI does cook the perfect medium rare steak every time, but it's able to actually cook steaks in the time it used to take me to like microwave a burrito.
> There's nothing sacred about human-written code
I suspect that there is a lifetime of knowledge and experience wrapped up in that statement and I agree wholeheartedly.
I love writing software and I've been doing it for decades. But I never forget that at the end, there's someone who's paid for it, and needs it to do a job that can't/won't be done manually. And that's why it exists. Not to satisfy my desire to express myself in code, not to allow me to create some golden tower of perfect architecture, but to do a job. Most likely a boring job in the service of increased profitability for some company.
And in the end, the only thing that matters to the customer is if it does that job well enough to be useful. I think -- hell, I know -- that many developers deliberately ignore this critical point.
> All you want is to not be hungry.
This.
Programers are high end chef's whos products are expensive.
LLM / AI tooling is going to do a much better job of delivering on the promise of AppleScript, vb script, IFTT, and every failed drag and drop coding tool that got sold to businesses over the years.
There are going to be gains in large software, from professionals - but the real gains are going to come from all the software that CAN get created that never was before.
There is an XKCD comic "Is It Worth the Time?" (link: https://xkcd.com/1205/ )- a matrix of the cost benefit of automating something. LLM's / AI tooling, generating traditional code, makes the answer "yes" in almost every case now.
I think most people on this board would agree with the thesis. However the real problem is when the owner of the restaurant looks at profit and loss statements and decides to keep less chefs on the payroll because customers are willing to pay for (just) edible steak.
The reduced expectations of steak, the desire for the perfect steak, are all fading to the background because "just passing satisfactorily" is better for business.
Why is that a problem?
I love a good medium-rare prime steak, but I also used to live near a restaurant called Best Steak House, which was anything but. However, they delivered a passable steak/steak sandwich that was worth what you paid for it. And considering that they were in business for decades, probably most people felt the same way.
A great steak is an occasional luxury; a "just edible" one is an everyday meal.
The thesis is covering the flat scenarios mostly. I think the scenario where the job is done so poorly that the steak has to be thrown away needs to be included as well. At times the pans would get damaged. And eventually the business is in debt and needs to be closed
That’s what the entire economy does by design. You make the lowest common denominator people will still pay for.
I worry what that says about our lives as a whole. The government will make our lives the lowest common denominator that we are willing to endure.
How are you still blaming this on “the government”? Is your thesis that the business world would be delivering high quality products and experiences if only those damned bureaucrats would get out of the way? What possible evidence do you have left for that hypothesis at this point?
EDIT: this was a misread on my part, retracted, carry on.
He is saying "if we apply (or are applying) the same business mentality to government, we will get literally the worst government we are willing to tolerate based on money"
It's not blaming. It's an explanation for probably why things can go wrong.
Gotcha, ok - I’m misreading it. Thank you.
He isn't blaming the software problem on the government. He is saying that if we apply this reasoning to everything in our lives, then we have a race to the bottom in other facets, of which one is government.
Yep, I see it now. Thank you.
And people seek out the highest quality product they can pay the lease for
Not always. There are diminishing returns at the top end of the quality spectrum. I tend to go with the cheaper item even if I can technically afford the more expensive one if their quality is close enough.
I can go out today and pay $50 for a thick cut ribeye at Ruth's Chris that has been visually inspected for marbling then hard seared in a special high temp over to temp by someone who knows wtf they're doing. I can also go to Denny's and pay $20 for a t-bone cooked to within shouting distance of what I asked for on the same griddle they use for the pancakes that doesn't get the sear I'm looking for.
Point being, you're absolutely right that the goal for a company selling a product is to be barely satisfactory but they don't actually get to decide what's satisfactory.
The author has clearly not traveled much .. across much of asia, ME, and generally anywhere near or south of the equator, a steak cooked well done is considered the finest way to eat beef … the same also applies to AI generated code .. it may not satisfy a craftsmans idea of perfection, but it accomplishes what most people actually expect from it .. to take it further .. a film makers fav film is usually unwatchable and considered commercially speaking a failure ..
Steaks require very few skills. Buy 10 steaks, some oil, a pan, and a heat source. By the time you've cooked the 10th steak you will be able to cook a decent steak.
Software requires a massive amount of skill. You can't say you can build "consistently good software" after writing your 10th program. Most software engineers really aren't great at judging what makes good software. So humans aren't a great solution to AI's lack of ability here. We're limited by our own inherent dumbness.
LLMs are genuinely better software engineers than most humans. But they lack the cognitive power to hold in their head and recall many ideas at once for a long time. They're a genius who gets drunk every 10 minutes. You, human, aren't better at writing software - but you aren't drunk. So for now, you manage the AI. The hope is that one day we can make LLMs not be drunk, so it can do a better job than our dumb asses do.
It's possible that we'll never be able to make it not-drunk. In that case, to get any new improvement, we'll have to make it faster.. which will make it drunk every 5 minutes instead of every 10. This means we'll spend twice as much time keeping it on the road. The hope is that somehow this will create more productivity. Probably by having more of them running at once, with more human guides... which will run into the mythical man month fallacy. Everything old is new again.
Sous vide
The article is AI generated
Why does this article use backticks instead of apostrophes?
Considering the article starts with "Cooking a steak requires almost no skill. Put it in a hot pan", I think I see the root of his problem. Pan searing a steak only makes sense in a small number of situations: you have a leaner cut like filet mignon, you want precise temperature control to achieve the Maillard reaction, or you know none of these things but just really like stinking up your kitchen and splattering butter everywhere for the aesthetic and the olfactory experience. For most cases, you would be better off with a grill rather than a pan.
Now maybe you're not knowledgeable about steak, and you're not sure whether the cut of steak you bought would be better off pan seared or grilled. Is ribeye better in a pan or a grill? That depends on whether you want it more smokey or more buttery. Ask an AI and tell it how you want it to taste, and it'll tell you the answer.
His metaphorical steak machine would produce better results if he had simply asked "I want a steak and I have this specific cut and I want it to have this kind of flavor, how do you suggest I make it?" rather than "cook steak on a hot pan, make no mistakes."
TL;DR: Tell the AI what you want and then ask how it for a recommendation of how to do it. You don't have to "understand software." You only need to understand how to ask the right questions.
Given how extraordinarily fast all of this is moving, it's a particular absurdity to attempt these in the moment pontifications. AI is a steak machine, I declare that is what it is. You mean GPT 3.5? That's a mere four years ago.
Fable was essentially unthinkable for ~99% of tech workers just five years ago, that any of that would occur so soon and so spectacularly. Now we've got a mass of armchair experts declaring what AI of this minute is, or even what it is period.
Well AI can't even do fingers right so it's premature to say blah blah blah. Hello Krea2 et al.
Software is also eating the clowns.
Get back to me next week, China will probably have another Fable killer. And then Anthropic will have Ouija 8 that they'll have to place in an air-gapped straight-jacket to keep it from enslaving us all.
> Customers tolerate weird interfaces, pointless features, strange bugs, systems held together by generated code nobody actually understands.
And that's this "AI"tool too.
Garbage in, garbage out, as we say.
Nicely said
*salmon.
I’d prefer an article actually about cooking steak.
slop article
Who can afford a steak today without taking out a loan?
I think a certain restaurant chain might have something to say about this