Which is that information technology similarly (and seemingly shockingly) didn't produce any net economic gains in the 1970's or 1980's despite all the computerization. It wasn't until the mid-to-late 1990's that information technology finally started to show clear benefit to the economy overall.
The reason is that investing in IT was very expensive, there were lots of wasted efforts, and it took a long time for the benefits to outweigh the costs across the entire economy.
And so we should expect AI to look the same -- it's helping lots of people, but it's also costing an extraordinary amount of money, and the few people it's helping is currently at least outweighed by the people wasting time with it and its expense. But, we should recognize that it's very early days, and that productivity will rise with time, and costs will come down, as we learn to integrate it with best practices.
A Claude subscription is 20 bucks per worker if using personal accounts billed to the company, which is not very far from common office tools like slack. Onboarding a worker to Claude or ChatGPT is ridiculously easy compared to teaching a 1970’s manual office worker to use an early computer.
Larger implementations like automating customer service might be more costly, but I think there are enough short term supposed benefits that something should be showing there.
In the end when jobs are done right they seem to disappear. We notice crappy software or a poorly done HVAC system not clean carpets.
Cattle? You actually think that about other people?
Assistant is dispatching a courier to get medical records. AI auto completes to include the address. Normally they wouldn't put the address, the courier knows who we work with, but AI added it so why not. Except it's the wrong address because it's for a different doctor with the same name. At least they knew to verify it, but still mistakes like this happening at scale is making the other time savings pretty close to a wash.
There are a lot of white-collar tasks that have far lower quality and correctness bars. "Researching" by plugging things into google. Writing reports summarizing how a trend that an exec saw a report on can be applied to the company. Generating new values to share at a company all-hands.
Tons of these that never touch the "real world." Your assistant story is like a coding task - maybe someone ran some tests, maybe they didn't, but it was verifiable. No shortage of "the tests passed, but they weren't the right test, this broke some customers and had to be fixed by hand" coding stories out there like it. There are pages and pages of unverifiable bullshit that people are sleepwalking through, too, though.
Nobody already knows if those things helped or hurt, so nobody will ever even notice a hallucination.
But everyone in all those fields is going to be trying really really hard to enumerate all the reasons it's special and AI won't work well for them. The "management says do more, workers figure out ways to be lazier" see-saw is ancient, but this could skew far towards "management demands more from fewer people" spectrum for a while.
Consider, for example, the following python code:
x = (5)
x = (5,)
Now imagine trying to spot that one missing comma among the 20kloc of code one so proudly claims AI helped them "write", especially if it's in a cold path. You won't see it.
That book was very different than what I expected from all of the internet comment takes about it. The premise was really thin and did't actually support the idea that the jobs don't generate value. It was comparing to a hypothetical world where everything is perfectly organized, everyone is perfectly behaved, everything is perfectly ordered, and therefore we don't have to have certain jobs that only exist to counter other imperfect things in society.
He couldn't even keep that straight, though. There's a part where he argues that open source work is valuable but corporate programmers are doing bullshit work that isn't socially productive because they're connecting disparate things together with glue code? It didn't make sense and you could see that he didn't really understand software, other than how he imagined it fitting into his idealized world where everything anarchist and open source is good and everything corporate and capitalist is bad. Once you see how little he understands about a topic you're familiar with, it's hard to unsee it in his discussions of everything else.
That said, he still wasn't arguing that the work didn't generate economic value. Jobs that don't provide value for a company are cut, eventually. They exist because the company gets more benefit out of the job existing than it costs to employ those people. The "bullshit jobs" idea was more about feelings and notions of societal impact than economic value.
Sure, but there's no such thing as "the company." That's shorthand - a convenient metaphor for a particular bunch of people doing some things. So those jobs can exist if some people - even one person - gets more benefit out of the job existing than it costs that person to employ them. For example, a senior manager padding his department with non-jobs to increase headcount, because it gives him increased prestige and power, and the cost to him of employing that person is zero. Will those jobs get cut "eventually"? Maybe, but I've seen them go on for decades.
I don't know if maybe he wasn't explaining it well enough, but that kind of reasoning makes some sense.
A lot of code is written because you want the output from Foo to be the input to Bar and then you need some glue to put them together. This is pretty common when Foo and Bar are made by different people. With open source, someone writes the glue code, publishes it, and then nobody else has to write it because they just use what's published.
In corporate bureaucracies, Company A writes the glue code but then doesn't publish it, so Company B which has the same problem has to write it again, but they don't publish it either. A hundred companies are then doing the work that only really needed to be done once, which makes for 100 times as much work, a 1% efficiency rate and 99 bullshit jobs.
But he states that expressis verbis, so your discovery is not that spectacular.
Although he gives examples of jobs, or some aspects of jobs, that don't help to deliver what specific institutions aim to deliver. Example would be bureaucratization of academia.
Not necessarily, I’ve seen a lot of jobs that were just flying under the radar. Sort of like a cockroach that skitters when light is on but roams freely in the dark.
I thought he made a case for both societal and economic impact.
> Jobs that don't provide value for a company are cut, eventually.
Uhm, seems like Greaber is not the only one drawing conclusions from a hypothetical perfect world
I tried to respond to the specific conversation about Bullshit Jobs above. In my experience, the way this book is brought up so frequently in online conversations is used as a prop for whatever the commenter wants it to mean, not what the book actually says.
I think Graeber did a fantastic job of picking "bullshit jobs" as a topic because it sounds like something that everyone implicitly understands, but how it's used in conversation and how Graeber actually wrote about the topic are basically two different things
Was a LLM used during that optimization? Yes.
Who will correlate the sudden productivity improvement with our optimization of the data flow with the availability of a LLM to do such optimizations fast enough that no project+consultants+management is needed ?
No one.
Just like no one is evaluating the value of a hammer or a ladder when you build a house.
This is where the whole "show me what you built with AI" meme comes from, and currently there's no substitute for SWEs. Maybe next year or next next year, but mostly the usage is generating boring stuff like internal tool frontends, tests, etc. That's not nothing, but because actually writing the code was at best 20% of the time cost anyway, the gains aren't huge, and won't be until AI gets into the other parts of the SDLC (or the SDLC changes).
It’s easy to convince yourself that it is, and anyone can massage some internal metric enough to prove their desired outcome.
Not recognizing the essential role of sales seemed to be a common mistake.
It identified advertising as part of the category that it classed as heavily-bullshit-jobs for reason of being zero-sum—your competitor spends more, so you spent more to avoid falling behind, standard red queen’s race. (Another in this category was the military, which is kinda the classic case of this—see also, the Missile Gap, the dreadnought arms race, et c.) But not sales, IIRC.
It says stuff like why can’t a customer just order from an online form? The employee who helps them doesn’t do anything except make them feel better. Must be a bullshit job. It talks specifically about my employees filling internal roles like this.
> advertising
I understand the arms race argument, but it’s really hard to see what an alternative looks like. People can spend money to make you more aware of something. You can limit some modes, but that kind of just exists.
I don’t see how they aren’t performing an important function.
There's nothing inherent to socialism that would preclude advertising. It's an economic system where the means of production (capital) is owned by the workers or the state. In market socialism you still have worker cooperatives competing on the market.
Ever actually lived in anything approaching one? Yeah, if the stores are empty, it does not make sense to produce ads for stuff that isn't there ...
... but we still had ads on TV, surprisingly, even for stuff that was in shortage (= almost everything). Why? Because the Plan said so, and disrespecting the Plan too openly would stray dangerously close to the crime of sabotage.
You have no idea.
Like when a competing country builds their tenth battleship, so you commission another one to match them. The world would have been better if neither had been build. Money changed hands (one supposes) but the aim of the whole exercise had no effect. It was similar to paying people to dig holes a fill them back in again, to the tune of serious money. This was so utterly stupid and wasteful that there was a whole treaty about it, to try to prevent so many bullshit jobs from being created again.
Or when Pepsi increases their ad spending in Brazil, so Coca Cola counters, and much of the money ends up accomplishing little except keeping things just how they were. That component or quality of the ad industry, the book claims, is bullshit, on account of not doing any good.
The book treats of several ways in which a job might be bullshit, and just kinda mentions this one as an aside: the zero-sum activity. It mostly covers other sorts, but this is the closest I can recall it coming to declaring sales “bullshit” (the book rarely, bordering on never, paints even most of an entire industry or field as bullshit, and advertising isn’t sales, but it’s as close as it got, as I recall)
This is not true at all. You can find plenty of examples going either way but it’s far from truth from being a universal reality
Companies are obviously not hesitant to lay off anyone, especially for cost saving. It is interesting how you think that people are laid off because they’re unproductive.
Maybe you're lucky enough to be doing cutting edge research or do something that really seriously impacts human beings, but I've done plenty of "mission critical right fucking now" work that a week from now (or even hours from now, when I worked for a content marketing business) is beyond irrelevant. It's an amazing thing watching marketing types set money on fire burning super expensive developer time (but salaried, so they discount the cost to zero) just to make their campaigns like 2-3% more efficient.
I've intentionally sat on plenty of projects that somebody was pushing really hard for because they thought it was the absolute right necessary thing at the time and the stakeholder realized was pointless/worthless after a good long shit and shower. This one move has saved literally man years of work to be done and IMO is the #1 most important skill people need to learn ("when to just do nothing").
Most "Bullshit Jobs" can already be automated, but can isnt always should or will. Graeber is a capex thinker in an opex world.
> There’s not much of value to obtain from the book.
Anthropological insight has much more value than anything economists may produce on economy.
Claude Code has rate limits for a reason: I expect they are carefully designed to ensure that the average user doesn't end up losing Anthropic money, and that even extreme heavy users don't cause big enough losses for it to be a problem.
Everything I've heard makes me believe the margins on inference are quite high. The AI labs lose money because of the R&D and training costs, not because they're giving electricity and server operational costs away for free.
I'll be convinced they're actually making money when they stop asking for $30 billion funding rounds. None of that money is free! Whoever is giving them that money wants a return on their investment, somehow.
Once that happens, whomever is left standing can dial back the training investment to whatever their share of inference can bear.
Or, if there's two people left standing, they may compete with each other on price rather than performance and each end up with cloud compute's margins.
Training costs are fixed at whatever billions of dollars per year.
If inference is profitable they might conceivably make a profit if they can build a model that's good enough to sign up vast numbers of paying customers.
If they lose even more money on each new customer they don't have any path to profitability at all.
Which means that training needs to be ongoing. So the revenue covers the inference? So what? All that means is that it doesn't cover your costs and you're operating at a loss. Because it doesn't cover the training that you can't stop doing either.
Seems like a pretty dumb take. It’s like saying it only takes $X in electricity and raw materials to produce a widget that I sell for $Y. Since $Y is bigger than $X, I’m making money! Just ignore that I have to pay people to work the lines. Ignore that I had to pay huge amounts to build the factory. Ignore every other cost.
They can’t just fire everyone and stop training new models.
Gross profit = revenues - cost of goods sold
Operating profit = Gross profit - operating expenses including depreciation & amortisation
Net profit = Operating profit - net interest expense - taxes
If I am on a roll, I will flip on Extra Usage. I prototyped a fully functional and useful niche app in ~6 total hours and $20 of extra usage, and it's solid enough and proved enough value to continue investing in and eventually ship to the App store.
Without Claude I likely wouldn't have gotten to the finished prototype version to use in the real world.
For Indy dev, I think LLMs are a new source of solutions. This app is too niche to justify building and marketing without LLM assistance. It likely won't earn more than $25k/year but good enough!
For people doing work with LLMs as an assistant for codebase searching, reviews, double checks, and things like that the $20/month plan is more than fine. The closer you get to vibecoding and trying to get the LLM to do all the work, the more you need the $100 and $200 plans.
On the ChatGPT side, the $20/month subscription plan for GPT Codex feels extremely generous right now. I tried getting to the end of my window usage limit one day and could not.
> so the "just another $20 SaaS" argument doesn't sound too good
Having seen several company's SaaS bills, even $100/month or $200/month for developers would barely change anything.
but at that point you could go for a bugger one and split amongst headcount
Real life example: A client came to me asking how to compare orders against order confirmation from the vendor. They come as PDF files. Which made me wonder: Wait, you don't have any kind of API or at least structured data that the vendor gives you?
Nope.
And here you are. I am not talking about a niche business. I assume that's a broader problem. Tech can probably automate everything and this since 30 years. Still business lack of "proper" IT processes, because at the end every company is unique and requires particular measures to be "fully" onboarded to IT based improvements like that.
As measured by whom? The same managers who demanded we all return to the office 5 days a week because the only way they can measure productivity is butts in seats?
We do have a way to see the financial impact - just add up Anthropic and oAI's reported revenues -> something like $30b in annual run rate. Given growth rates, (stratospheric), it seems reasonable to conclude informed buyers see economic and/or strategic benefit in excess of their spend. I certainly do!
That puts the benefits to the economy at just around where Mastercard's benefits are, on a dollar basis. But with a lot more growth. Add something in there for MS and GOOG, and we're probably at least another $5b up. There are only like 30 US companies with > $100bn in revenues; at current growth rates, we'll see combined revenues in this range in a year.
All this is sort of peanuts though against 29 trillion GDP, 0.3%. Well not peanuts, it's boosting the US GDP by 10% of its historical growth rate, but the bull case from singularity folks is like 10%+ GDP growth; if we start seeing that, we'll know it.
All that said, there is real value being added to the economy today by these companies. And no doubt a lot of time and effort spent figuring out what the hell to do with it as well.
Does profitable always equal useful? Might other cultures justifiably think differently, like the Amish?
I also didn’t talk profitable. Upshot, though, I don’t think it’s just a US thing to say that when money exchanges hands, generally both parties feel they are better off, and therefore there is value implied in a transaction.
As to what it will be used for: yes.
And that's the point here: value is handicapped by the web interface, and we are stuck there for the foreseeable future until the tech teams get their priorities straight and build decent data integration layers, and workflow management platforms.
[0]: https://www.almendron.com/tribuna/wp-content/uploads/2018/03...
https://www.nber.org/system/files/working_papers/w25148/w251...
I don't agree that real GDP measures what he thinks it measures, but he opines
>Data released this week offers a striking corrective to the narrative that AI has yet to have an impact on the US economy as a whole. While initial reports suggested a year of steady labour expansion in the US, the new figures reveal that total payroll growth was revised downward by approximately 403,000 jobs. Crucially, this downward revision occurred while real GDP remained robust, including a 3.7 per cent growth rate in the fourth quarter. This decoupling — maintaining high output with significantly lower labour input — is the hallmark of productivity growth.
https://www.ft.com/content/4b51d0b4-bbfe-4f05-b50a-1d485d419...
[0] on the basis that IT and AI are not general technologies in the mold of the dynamo, keyword "intangibles", see section 4 p21, A method to measure intangibles
On hacker news, a very tech literate place, I see people thinking modern AI models can’t generate working code.
The other day in real life I was talking to a friend of mine about ChatGPT. They didn’t know you needed to turn on “thinking” to get higher quality results. This is a technical person who has worked at Amazon.
You can’t expect revolutionary impact while people are still learning how to even use the thing. We’re so early.
It takes time to get an intuition for the kinds of problems they've seen in pre-training, what environments it faced in RL, and what kind of bizarre biases and blindspots it has. Learning to google was hard, learning to use other peoples libraries was hard, and its on par with those skills at least.
If there is a well known design pattern you know, thats a great thing to shout out. Knowing what to add to the context takes time and taste. If you are asking for pieces so large that you can't trust them, ask for smaller pieces and their composition. Its a force multiplier, and your taste for abstractions as a programmer is one of the factors.
In early usenet/forum days, the XY problem described users asking for implementation details of their X solution to Y problem, rather than asking how to solve Y. In llm prompting, people fall into the opposite. They have an X implementation they want to see, and rather than ask for it, they describe the Y problem and expect the LLM to arrive at the same X solution. Just ask for the implementation you want.
Asking bots to ask bots seems to be another skill as well.
If not, you don't know how to use it efficiently.
A large part of using AI efficiently is to significantly lower that review burden by having it do far more of the verification and cleanup itself before you even look at it.
As for the last example, for all the money being spent on this area, if someone is expected to perform a workflow based on the kind of question they're supposed to ask, that's a failure in the packaging and discoverability aspect of the product, the leaky abstraction only helps some of us who know why it's there.
1. People who only think of using AI in very specific scenarios. They don’t know when you use it outside of the obvious “to write code” situations and they don’t really use AI effectively and get deflated when AI outputs the occasional garbage. They think “isn’t AI supposed to be good at writing code?”
2. People who let AI do all the thinking. Sometimes they’ll use AI to do everything and you have to tell them to throw it all away because it makes no sense. These people also tend to dump analyses straight from AI into Slack because they lack the tools to verify if a given analysis is correct.
To be honest, I help them by teaching them fairly rigid workflows like “you can use AI if you are in this specific situation.” I think most people will only pick up tools effectively if there is a clear template. It’s basically on-the-job training.
But from my social feed the impression was that it is taking over the world:)
I asked it because I am building something similar since some tome and I thought its over they were faster than me but as it appears there’s no real adoption yet. Maybe there will be some once they release it as part of ChatGPT but even then it looks like too early as actually few people are using the more advanced tools.
It’s definitely in very early stage. It appears that so far the mainstream success in AI is limited to slop generation and even that is actually small number of people generating huge amounts of slop.
Shocking results, I say!
Most people are just "not that deep in there" as most people on HN.
If you have been working on a usecase similar to OpenClaw for sometime now I'd actually say you are in a great position to start raising now.
Being first to market is not a significant moat in most cases. Few people want to invest in the first company in a category - it's too risky. If there are a couple of other early players then the risk profile has been reduced.
That said, you NEED to concentrate on GTM - technology is commodified, distribution is not.
> It appears that so far the mainstream success in AI is limited to slop generation and even that is actually small number of people generating huge amounts of slop
The growth of AI slop has been exponential, but the application of agents for domain specific usecases has been decently successful.
The biggest reason you don't hear about it on HN is because domain-specific applications are not well known on HN, and most enterprises are not publicizing the fact that they are using these tools internally.
Furthermore, almost anyone who is shipping something with actual enterprise usage is under fairly onerous NDAs right now and every company has someone monitoring HN like a hawk.
On my app the tech is based on running agent generated code on JavaScriptCore to do things like OpenClaw, I’m wrapping the JS engine with the missing functionality like networking, file access and database access so I believe I will not have a problem with releasing it on Apple AppStore as I use their native stack. Then since this stack is also OS, I’m making a version that will run on Linux, the idea being users develops their solution on their device(iOS&Mac currently) see it working and and then deploys on a server with a tap of a button, so it keeps running.
You need to answer these questions in order to decide whether a Show HN makes sense versus a much more targeted launch.
If you do not know how to answer these questions you need to find a cofounder asap. Technology is commodified. GTM, sales, and packaging is what turns technology into products. Building and selling and fundraising as 1 person is a one-way ticket to burnout, which only makes you and your product less attractive.
I also highly recommend chatting with your network to understand common types of problems. Once you've identified a couple classes of problems and personas for whom your story resonates, then you can decide what approach to take.
Best of luck!
Thanks for the advice! I’m at a stage where I want to have such tool and see who else wants it. Not sure yet about it’s viability as a business and what is the exact market. Maybe I will find out by putting it into the wild and that’s why I consider to release it as a mobile app first.
Monitoring specific user accounts or keywords? Is this typically done by a social media reputation management service?
Last time he said: "yes yes I know about ChatGPT, but I do not use it at work or home."
Therefore, most people wont even know about Gemini, Grok or even Claude.
I think this is the prior you should investigate. That may be what HN used to be. But it's been a long time since it has been an active reality. You can still see actual expert opinions on HN, but they are the minority more and more.
From personal experience, I've also noticed that some of the most toxic discourse and responses I've received on this platform are overwhelmingly from post-2022 users.
Really? Can you show any examples of someone claiming AI models cannot generate working code? I haven't seen anyone make that claim in years, even from the most skeptical critics.
I started working today on a project I hadn’t touched in a while but I now needed to as it was involved in an incident where I needed to address some shortcomings. I knew the fix I needed to do but I went about my usual AI assisted workflow because of course I’m lazy the last thing I want to do is interrupt my normal work to fix this stupid problem.
The AI doesn’t know anything about the full scope of all the things in my head about my company’s environment and the information I need to convey to it. I can give it a lot of instructions but it’s impossible to write out everything in my head across multiple systems.
The AI did write working code, but despite writing the code way faster than me, it made small but critical mistakes that I wouldn’t have made on my first draft.
For example, it just added in a command flag that I knew that it didn’t need, and it actually probably should have known it, too. Basically it changed a line of code that it didn’t need to touch.
It also didn’t realize that the curled URL was going to redirect so we needed an -L flag. Maybe it should have but my brain knew it already.
It also misinterpreted some changes in direction that a human never would have. It confused my local repository for the remote one because I originally thought I was going to set a mirror, but I changed plans and used a manual package upload to curl from. So it out the remote URL in some places where the local one should have been.
Finally, it seems to have just created some strange text gore while editing the readme where it deleted existing content for seemingly no reason other than some kind of readline snafu.
So yes it produced very fast great code that would have taken me way longer to do, but I had to go back and consume a very similar amount of time to fix so many things that I might as well have just done it manually.
But hey I’m glad my company is paying $XX/month for my lazy workday machine.
This is your problem: How should it know if you do not provide it?
Use Claude - in the pro version you can submit files for each project which are setting the context: This can be files, source code, SQL scripts, screenshots whatever - then the output will be based on your context given by providing these files.
https://fortune.com/2026/02/15/ai-productivity-liftoff-doubl...
Source of the Stanford-approved opinion: https://www.ft.com/content/4b51d0b4-bbfe-4f05-b50a-1d485d419...
The 1990s boom was in large part due to connectivity -- millions[1] of computers joined the Internet.
[1] _ In the 1990s. Today, there are billions of devices connected, most of them Android devices.
I think it's more likely that people "feel" more productive, and/or we're measuring bad things (lines of code is an awful way to measure productivity -- especially considering that these agents duplicate code all the time so bloat is a given unless you actively work to recombine things and create new abstractions)
(Think about this old black/white or green mainframe screens - horrible looking but it gets their job done)
The thing to note is, verifying if something got done is harder and takes time in the same ballpark as doing the work.
If people are serious about AI productivity, lets start by addressing how we can verify program correctness quickly. Everything else is just a Ferrari between two traffic red lights.
Is that somewhat substantiated assumption? I recall learning on University in 2001 the history of AI and that initial frameworks were written in 70's and that prediction was we will reach human-like intelligence by 2000. Just because Sama came up with this somewhat breakthrough of an AI, it doesn't mean that equal improvement leaps will be done on a monthly/annual basis going forward. We may as well not make another huge leaps or reach what some say human intelligence level in 10 years or so.
Is it fair to say that wall street is betting America's collective pensions on AI...
I am glad to see articles like this that evaluate impact, but I wish the following would get more public interest:
With LLMs we are chasing sort-of linear growth in capability at exponential cost increases for power and compute.
Were you mad when the government bailed out mis-managed banks? The mother of all government bailouts might be using the US taxpayer to fund idiot companies like Anthropic and OpenAI that are spending $1000 in costs to earn $100.
I am starting to feel like the entire industry is lazy: we need fundamental new research in energy and compute efficient AI. I do love seeing non-LLM research efforts and more being done with much smaller task-focused models, but the overall approach we are taking in the USA is f$cking crazy. I fear we are going to lose big-time on this one.
* If I don't know how to do something, llms can get me started really fast. Basically it distills the time taken to research something to a small amount.
* if I know something well, I find myself trying to guide the llm to make the best decisions. I haven't reached the state of completely letting go and trusting the llm yet, because the llm doesn't make good long term decisions
* when working alone, I see the biggest productivity boost in ai and where I can get things done.
* when working in a team, llms are not useful at all and can sometimes be a bottleneck. Not everyone uses llms the same, sharing context as a team is way harder than it should be. People don't want to collaborate. People can't communicate properly.
* so for me, solo engineers or really small teams benefit the most from llms. Larger teams and organizations will struggle because there's simply too much human overheads to overcome. This is currently matching what I'm seeing in posts these days
I think companies will need fewer engineers but there will be more companies.
Now: 100 companies who employ 1,000 engineers each
What we are transitioning to: 1000 companies who employ 10 engineers each
What will happen in the future: 10,000 companies who employ 1 engineer each
Same number of engineers.
We are about to enter an era of explosive software production, not from big tech but from small companies. I don't think this will only apply to the software industry. I expect this to apply to every industry.
The seniors today who have got to senior status by writing code manually will be different than seniors of tomorrow, who got to senior status using AI tools.
Maybe people will become more of generalists rather than specialists.
That’s putting it mildly. I think it’s going to be interesting to see what happens when an entire generation of software developers who’ve only ever known “just ask the LLM to do it” are unleashed on the world. I think these people will have close to no understanding of how computing works on a fundamental level. Sort of like the difference between Gen-X/millenial (and earlier) developers who grew up having to interact with computers primarily through CLIs (e.g., DOS), having to at least have some understanding of memory management, low-level programming, etc. versus the Gen-Z developers who’ve only ever known computers through extremely high level interfaces like iPads.
Sure, maybe you would have caught the bug if you wrote assembly instead of C. But the C programmer still released much better software than you faster. By the time you shipped v1 in assembly, the C program has already iterated 100 times and found product market fit.
Someone who is good at writing code isn't always good at making money.
And large companies. The first half of my career was spent writing internal software for large companies. I believe it's still the case that the majority of software written is for internal software. AI will be a boon for these use cases as it will make it easier for every company big and small to have custom software for its exact use case(s).
Big cos often have the problem of defining the internal problems they’re trying to solve. Once identified they have to create organizational permission structures to allow the solutions. Then they need to stay on tasks long enough to build and use the software to solve the problem.
Only one of these steps is easily improved with AI.
When Engineering Budget Managers see their AI bills rising, they will fire the bottom 5-10% every 6-12 months and increase the AI assistant budget for the high performers, giving them even more leverage.
Our team shrunk by 50% but we are serving 200% more customers. Every time a dev left, we thought we're screwed. We just leveraged AI more and more. We are also serving our customers better too with higher retention rates. When we onboard a customer with custom demands, we used to have meetings about the ROI. Now we just build the custom demands in the time we took to meet to discuss whether we should even do it.
Today, I maintain a few repos critical to the business without even knowing the programming language they are written in. The original developers left the company. All I know is what is suppose to go into the service and what is suppose to come out. When there is a bug, I ask the AI why. The AI almost always finds it. When I need to change something, I double and triple check the logic and I know how to test the changes.
No, a normal person without a background in software engineering can't do this. That's why I still have a job. But how I spend my time as a software engineer has changed drastically and so has my productivity.
When a software dev say AI doesn't increase their productivity, it truly does feel like they're using it wrong or don't know how to use it.
During the promo review, people will look how many projects were done and the impact of those projects.
This would be strange, because all other technology development in history has taken things the exact opposite direction; larger companies that can do things on scale and outcompete smaller ones.
This would be strange, because all other technology development in history has taken things the exact opposite direction; larger companies that can do things on scale and outcompete smaller ones.
Youtube allowed many more small media production companies - sometimes just one person in their garage.
Shopify allowed many more small retailers.
Steam & cheap game engines allowed many more indie game developers instead of just a few big studios.
It likely depends on the stage of the tech development. I can see Youtube channels consolidating into a few very large channels. But today, there are far more media production companies than 30 years ago.
Privacy is non existent, every word said and message sent at the office is recorded but the benefits we saw were amazing.
Meetings are work, as much as IPC and network calls are work. Just because they're not fun, or what you like to do, it doesn't mean they're any less of a work.
I think you're analyzing things from a tactical perspective, without considering strategic considerations. For example, have you considered that it might not be desirable for CPUs to be just fast, or fast at all? is CISC faster than RISC? different architectural considerations based on different strategic goals right?
If you're an order picker at an amazon warehouse, raw speed is important. being able to execute a simpler and more fixed set of instructions (RISC), and at greater speed is more desirable. if you're an IT worker, less so. IT is generally a cost-center, except for companies that sell IT services or software. if you're in a cost center, then you exist for non-profit-related strategic reasons, such as to help the rest of the company work efficiently, be resilient, compete, be secure. Some people exist in case they're needed some day, others are needed critically but not frequently, yet others are needed frequently but not critically. being able to execute complex and critical tasks reliably and in short order is more desirable for some workers. Being fast in a human context also means being easily bored, or it could mean lots of bullshit work needs to be invented to keep the person busy and happy.
I'd suggest taking that compsci approach but considering not just the varying tasks and workloads, but also the diversity of goals and user cases of users (decision makers/managers in companies). There are deeper topics with regards or strategy and decision making surrounding the state machines of incentives and punishments, and decision maker organization (hierarchical, flat, hub-and-spoke,full-mesh,etc..).
But I like your and OP's analogy. Also, the productivity claims are coming from the guys in main memory or even disk, far removed from where the crunching is taking place. At those latency magnitudes, even riding a turtle would appear like a huge productivity gain.
Once you do have a billion dollar product protecting it requires spending time, money and people to keep running. Because building a new one is a lot more effort than protecting existing one from melting.
Once you have revenue you have downside to protect. Pre-revenue the worst that can happen is that you have to start again knowing more than you did.
https://newsletter.semianalysis.com/p/claude-code-is-the-inf...
You rebutted by claiming 4% of open source contributions are AI generated.
GP countered (somewhat indirectly) by arguing that contributions don’t indicate quality, and thus wasn’t sufficient to qualify as “amazing AI-generated open source projects.”
Personally, I agree. The presence of AI contributions is not sufficient to demonstrate “amazing AI-generated open-source projects.” To demonstrate that, you’d need to point to specific projects that were largely generated by AI.
The only big AI-generated projects I’ve heard of are Steve Yegge’s GasTown and Beads, and by all accounts those are complete slop, to the point that Beads has a community dedicated to teaching people how to uninstall it. (Just hearsay. I haven’t looked into them myself.)
So at this point, I’d say the burden of proof is on you, as the original goalposts have not been met.
Edit: Or, at least, I don’t think 4% is enough to demonstrate the level of productivity GP was asking for.
4% for a single tool used in a particular way (many are out there using AI tools in a way that doesn't make it clear the code was AI authored) is an incredible amount. Don't see how you can look at that and see 'not enough'.
The vast majority of people using these tools aren't announcing it to the world. Why would they ? They use it, it works and that's that.
> amazing
Nobody moved the goal posts.
Keep licking those boots.
- https://github.com/simonw/sqlite-history-json
- https://github.com/simonw/sqlite-ast
- https://github.com/simonw/showboat - 292 stars
- https://github.com/simonw/datasette-showboat
- https://github.com/simonw/rodney - 290 stars and 4 contributors who aren't me or Claude
- https://github.com/simonw/chartroom
Noting the star counts here because they are a very loose indication that someone other than me has found them useful.
Asking for "amazing" open source projects in this case is not asking out of genuine curiosity or want for debate, it is a rhetorical question asked out of frustration at the general trajectory of AI and who profits off of it -- namely the boot-wearers.
Also small businesses aren't going to publish blog posts saying "we saved $500 on graphic design this week!"
And make your own brushes.
Before the printing presses came along, putting up flyers was not even imaginable.
Signs for businesses used to hand carved.
Then printed. A store sign was still produced by a team of professionals, but small businesses coils reasonably afford to print a sign. Not often updated, but it existed.
Then desktop publishing took off. Now lone graphic designers could design and send work off to a print shop. Small businesses could now afford regularly updated menus, signage, and even adverts and flyers.
Now small businesses can make their own creatives. AI can change stylesheets, write ad copy, and generate promotional photos.
Does any of this have the artistry of hand carved signs from 600 years ago? Of course not.
But the point is technology gives individuals control.
People have been painting with red and yellow ochre and soot for at least 50K years for sure, and probably several hundred thousand years in truth. You don't need a brush, you have fingers or a twig.
The walls on the streets of Pompeii are full of advertising -- they had an election going on and people just scribbled slogans and such on walls. You don't need flyers lol.
The idea that signs or advertising was "artistry" is deeply ahistorical. The reason old stuff looks real fancy is because labor was extremely cheap and materials were expensive.
Compare those to the pigments used (mixed up!) by professional painters, and then to what printers could make.
If you wanted to paint fine art in the 1400s you were possibly making your own canvases, your own paint brushes, and your own paints.
And on top of that you had to be a skilled painter!
> The walls on the streets of Pompeii are full of advertising -- they had an election going on and people just scribbled slogans and such on walls. You don't need flyers lol.
The American revolution included a lot of propaganda courtesy of printing presses and some very rich financers who had a vested interest in a revolution occuring.
Pamphlets everywhere. It is one thing to scribble on a wall, it is another to produce messages at a mass scale.
That sense of scale has been multiplied yet again by AI.
..a month
..multiplied by how many small businesses globally?
Personally I have noticed strange effects, where I previously would have reached for a software package to make something or solve an issue, its now often faster for me to write a specific program just for my use case. Just this weekend I needed a reel with a specific look to post on instagram but instead of trying to use something like after effects, i could quickly cobble together a program that was using css transforms that outputted a series of images I could tie together with ffmpeg.
About a month ago I was unhappy with the commercial ticketing systems, they were both expensive and opaque so I made my own. Obviously for a case like that you need discipline and testing when you take peoples money, so there was a lot of focus on end to end testing.
I have a few more examples like this, but to make this work you need to approach using LLMs with a certain amount of rigour. The hardest part is to prevent drift in the model. There are a certain number things you can do to make the model grounded in reality.
When the tool doesn’t have a reproducer, it’ll happily invent a story and you’ll debug the story. If you ground the root cause in for example a test, the model can get context enough to actually solve the problem.
Another issue is that you need to read and understand code quickly, but its no real difference from working with other developers. When tests are passing I usually do a PR to myself and then review as I usually would do.
A prerequisite is that you need tight specs, but those can also be generated if you are experienced enough. You need enough domain intuition to know what ‘done’ means and what to measure.
Personally I think the bottleneck will go from trying to get into a flow state to write solutions to analyze the problem space and verification.
Lots of these project have a lifespan of a week and will never ever be maintained. When you pour blood and sweat in a projet you get attached to it, when you vibe code it in an afternoon and it's not and instant hit you move on to the next one.
Or even the simple utility of having a chatbot. They’re not popular because they’re useless
Which to me says it’s more likely that people under estimate corporate inertia
- reviews for code
- asking stakeholders opinions
- SDLC latency (things taking forever to test)
- tickets
- documentations/diagrams
- presentations
Many of these require review. The review hell doesn't magically stop at Open source projects. These things happen internally too.
Other white collar business/bullshit job (ala Graeber) work is meeting with people, “aligning expectations”, getting consensus, making slides/decks to communicate those thoughts, thinking about market positioning, etc.
Maybe tools like Cowork can help to find files, identify tickets, pull in information, write Excel formulas, etc.
What’s different about coding is no one actually cares about code as output from a business standpoint. The code is the end destination for decided business processes. I think, for that reason, that code is uniquely well adapted to LLM takeover.
But I’m not so sure about other white-collar jobs. If anything, AI tooling just makes everyone move faster. But an LLM automating a new feature release and drafting a press release and hopping on a sales call to sell the product is (IMO) further off than turning a detailed prompt into a fully functional codebase autonomously.
If you weren’t doing much of that before, I struggled to think of how you were doing much engineering at all, save some more niche extremely technical roles where many of those questions were already answered, but even still, I should expect you’re having those kinds of discussions, just more efficiently and with other engineers.
The vast majority of software engineers in the world. The most widespread management culture is that where a team's manager is the interface towards the rest of the organization and the engineers themselves don't do any alignment/consensus/business thinking, which is the manager's exclusive job.
I used to work like that and I loved it. My managers were decent and they allowed me to focus on my technical skills. Then, due to those technical skills I'd acquired, I somehow got hired at Google, stayed there nearly a decade but hated all the OKR crap, perf and the continuous self-promotion I was obliged to do.
I'd suspect the kind that's going away.
I don’t agree with it or believe it’s smart but it’s the world we live in
* meeting with people, yes, on calls, on chats, sometimes even on phone
* “aligning expectations”, yes, because of the next point
* getting consensus, yes, inevitably or how else do we decide what to do and how to do it?
* making slides/decks to communicate that, not anymore, but this is a specific tool of the job, like programming in Java vs in Python.
* thinking about market positioning, no, but this is what only a few people in an organization have agency on.
* etc? Yes, for example don't piss off other people, help custumers using the product, identify new functionalities that could help us deliver a better product, prioritize them and then back to getting consensus.
That use case is definitely delegated to LLMs by many people. That said, I don't think it translates into linear productivity gains. Most white collar work isn't so fast-paced that if you save an hour making slides, you're going to reap some big productivity benefit. What are you going to do, make five more decks about the same thing? Respond to every email twice? Or just pat yourself on the back and browse Reddit for a while?
It doesn't help that these LLM-generated slides probably contain inaccuracies or other weirdness that someone else will need to fix down the line, so your gains are another person's loss.
But if you get deep into an enterprise, you'll find there are so many irreducible complexities (as Stephen Wolfram might coin them), that you really need a fully agentically empowered worker — meaning a human — to make progress. AI is not there yet.
It doesn’t capture everyone’s experience when you say thinking is the smaller part of programming.
I don’t even believe a regular person is capable of producing good quality code without thinking 2x the amount they are coding
WHOAH WHOAH WHOAH WHOAH STOP. No coder I've ever met has thought that thinking was anything other than the BIGGEST allocation of time when coding. Nobody is putting their typing words-per-minute on their resume because typing has never been the problem.
I'm absolutely baffled that you think the job that requires some of the most thinking, by far, is somehow less cognitively intense than sending emails and making slide decks.
I honestly think a project managers job is actually a lot easier to automate, if you're going to go there (not that I'm hoping for anyone's job to be automated away). It's a lot easier for an engineer to learn the industry and business than it is for a project manager to learn how to keep their vibe code from spilling private keys all over the internet.
huh? maybe im in the minority, but the thinking:coding has always been 80:20 spend a ton of time thinking and drawing, then write once and debug a bit, and it works
this hasnt really changed with Llm coding either, except that for the same amount of thinking, you get more code output
this software (which i am not related to or promoting) is better at investment planning and tax planning than over 90% of RIAs in the US. It will automate RIA to the point that trading software automated stock broking. This will reduce the average RIA fee from 1% per year to 0.20% or even 0.10% per year just like mutual fund fees dropped in the early 00s
more expensive silly companies will exist, but the cheap ones get the scale. SP500 index funds have over 1 trillion in the top 3 providers. cathy wood has like 6-7 billion.
BNYMellon is the custodian of $50 trillion of investment assets. robinhood has $324bn.
silly companies get the headlines though
Do they also make you write your own performance review and set your own objectives?
This is basically the same story I have heard both my own place of employment and also from a number of friends. There is a "need" for AI usage, even if the value proposition is undefined (or, as I would expect, non-existent) for most businesses.
Not to get off on a tangent but this has got to be a "tell" for how much a company is managed by formula and how much it's actually got thinking people running things. Every time I've had to write my own review I fill out the form with some corporatese bullshit, my supervisor approves it and adds some more bullshit, it disappears into HR and I never hear anything about it until it's time for the next review, and it starts over again. There isn't even reference to any of my "objectives" from the last review, because that review has simply disappeared.
But I'm sure some HR exec is checking boxes for following "best practices" in employee evaluation.
In my first year I didn’t know any better, so I tried to set myself some actual objectives (learn to use XYZ, improve test coverage by X%, measurable stuff that would actually help).
Fortunately my manager showed me how to do it correctly, so now my goals are to “differentiate with expertise” and to “empower through better solutions”.
Every year I open up the self-review, grade myself a 5/5 on these absurd, unmeasurable goals, my manager approves it, and it disappears off somewhere into the layers and layers of ever-higher management where nobody cares to look at it.
Figure A6 on page 45: Current and expected AI adoption by industry
Figure A11 on page 51: Realised and expected impacts of AI on employment by industry
Figure A12 on page 52: Realised and expected impacts of AI on productivity by industry
These seem to roughly line up with my expectations that the more customer facing or physical product your industry is, the lower the usage and impact of AI. (construction, retail)
A little bit surprising is "Accom & Food" being 4th highest for productivity impact in A12. I wonder how they are using it.
LLMs are impressive and flexible tools, but people expect them to be transformative, and they're only transformative in narrow ways. The places they shine are quite low-level: transcription, translation, image recognition, search, solving clearly specified problems using well-known APIs, etc. There's value in these, but I'm not seeing the sort of universal accelerant that some people are anticipating.
Give it a year or two and let things settle down and (assuming the music is still playing at that time) you might see more dinosaurs start to wander this way.
“Autofishers” are large boats with nets that bring in fish in vast quantities that you then buy at a wholesale market, a supermarket a bit later, or they flash freeze and sell it to you over the next 6-9 months.
Yet there’s still a thriving industry selling fishing gear. Because people like to fish. And because you can rarely buy fish as fresh as what you catch yourself.
Again, it’s not a great analogy, but I dunno. I doubt AGI, if it does come, will end up working the way people think it will.
For my team at least, the productivity boost is difficult to quantify objectively. Our products and services have still tons of issues that AI isn't going to solve magically.
It's pretty clear that AI is allowing to move faster for some tasks, but it's also detrimental for other things. We're going to learn how to use these tools more efficiently, but right now, I'm not convinced about the productivity gain.
What improvements have you noticed over that time?
It seems like the models coming out in the last several weeks are dramatically superior to those mid-last year. Does that match your experience?
The moment of realisation happen for a lot of normoid business people when they see claude make a DCF spreadsheet or search emails
claude is also smart because it visually shows the user as it resizes the columns, changes colours, etc. Seeing the computer do things makes the normoid SEE the AI despite it being much slower
Replace excel and office stuff with ai model entirely then people will pay attention.
iterating over work in excel and seeing it update correctly is exactly what people want. If they get it working in MSWord it will pick up even faster.
If the average office worker can get the benefit of AI by installing an add-on into the same office software they have been using since 2000 (the entire professional career of anyone under the age of 45), then they will do so. its also really easy to sell to companies because they dont have to redesign their teams or software stack, or even train people that much. the board can easily agree to budget $20 a head for claude pro
the other thing normies like is they can put in huge legacy spreadsheets and find all the errors
Microsoft365 has 400 million paid seats
FWIW Gemini inside Google apps is just as bad.
As a CEO I see it as a massive clog up of vast amounts of content that somebody will need to check. A DDoS of any text-based system.
The other day I got a document of 155 pages in Whatsapp. Thanx. Same with pull requests. Who will check all this?
The answer to that, for some, is more AI.
I had a peer explain that the PRs created by AI are now too large and difficult to understand. They were concerned that bugs would crop up after merging the code. Their solution, was to use another AI to review the code... However, this did not solve the problem of not knowing what the code does. They had a solution for that as well... ask AI to prepare a quiz and then deliver it to the engineer to check their understanding of the code.
The question was asked - does using AI mean best-practices should no longer be followed? There were some in the conversation who answered, "probably yes".
> Who will check all this?
So yeah, I think the real answer to that is... no one.
> My own updated analysis suggests a US productivity increase of roughly 2.7 per cent for 2025. This is a near doubling from the sluggish 1.4 per cent annual average that characterised the past decade.
good for 3 clicks: https://giftarticle.ft.com/giftarticle/actions/redeem/97861f...
Until the handoff tax is lower than the cost of just doing it yourself, the ROI isn't going to be there for most engineering workflows.
However, there's another factor. The J-curve for IT happened in a different era. No matter when you jumped on the bandwagon, things just kept getting faster, easier, and cheaper. Moore's law was relentless. The exponential growth phase of the J-curve for AI, if there is one, is going to be heavily damped by the enshitification phase of the winning AI companies. They are currently incurring massive debt in order to gain an edge on their competition. Whatever companies are left standing in a couple of years are going to have to raise the funds to service and pay back that debt. The investment required to compete in AI is so massive that cheaper competition may not arise, and a small number of (or single) winner could put anyone dependent on AI into a financial bind. Will growth really be exponential if this happens and the benefits aren't clearly worth it?
The best possible outcome may be for the bubble to pop, the current batch of AI companies to go bankrupt, and for AI capability to be built back better and cheaper as computation becomes cheaper.
Yeah, if your Fortune 500 workplace is claiming to be leveraging AI because it has a few dozen relatively tech illiterate employees using it to write their em dash/emoji riddled emails about wellness sessions and teams invites for trivia events… there’s not going to be a noticeable uptick in productivity.
The real productivity comes from tooling that no sufficiently risk adverse pubco IS department is going to let their employees use, because when all of their incentives point to saying no to installing anything ever, the idea of giving the permissions required for agentic AI to do anything useful is a non-starter.
The non code parts (about 90% of the work) is taking the same amount of time though.
[0]: See page 2: https://www.nber.org/system/files/working_papers/w34836/w348...
CEOs are now on the downside of the hype curve.
They went from “Get me some of that AI!” after first hearing about it, to “Why are we not seeing any savings? Shut this boondoggle down!” now that we’re a few years into bubble, the business math isn’t working, and they only see burning piles of cash.
I don't have a point, just that it's an unlikely unity.
Then I started working on some basic grpc/fullstack crap that I absolutely do not care about, at all, but needs to be done and uses internal frameworks that are not well documented, and now Claude is my best friend at work.
The best part is everyone else’s AI code still sucks, because they ask it to do stupid crap and don’t apply any critical thinking skills to it, so I just tell AI to re-do it but don’t fuck up the error handling and use constants instead of hardcoding strings like a middle schooler, and now I’m a 100x developer fearlessly leading the charge to usher in the AI era as I play the new No Man’s Sky update on my other PC and wait for whatever agent to finish crap.
trying to hacksmash Claude into outputting something it simply can't just produces endless mess. or getting into a fight pointing out issues with what it's doing and it just piles on extra layer upon layer of gunk. but meanwhile if you ask it to boilerplate an entire SaaS around the hard part, it's done in about 15 seconds.
of course this says nothing about the costs of long term maintainability, and I think everyone by now recognises what that's going to look like
Maybe this bothers me more than it should.
I bet many CEO PA are using AI for many tasks. It's typically a role where AI is very useful. Answering emails, moving meetings around, booking and buying a bunch of crap.
So I’m not even in the “it’s useless” camp, but it’s frankly only situationally useful outside of new greenfield stuff. Maybe that is the problem?
And Ask DeepWiki is a great shortcut for finding the right context… Granted this is open source and DW is free.
Is it the specific nature of your work?
There are some real changes in day to day software development. Programmers seem to be spending a lot of time prompting LLMs these days. Some more than others. But the trend is pretty hard to deny at this point. That snowballed in just 6-7 months from mostly working in IDEs to mostly working in Agentic coding tools. Codex was barely usable before the summer (I'm biased to that since that is what I use but it wasn't that far behind Claude Code). Their cli tool got a lot more usable in autumn and by Christmas I was using it more and more. The Desktop app release and the new model releases only three weeks ago really spiked my usage. Claude Code was a bit earlier but saw a similar massive increase in utility and usability.
It is still early days. This report cannot possibly take into account these massive improvements that hav been playing out over essentially just the last few months. This time last year, Agentic coding was barely usable. You had isolated early adopters of Claude Code, Cursor, and similar tools. Compare to what we have now, these tools weren't very good.
In the business world things are delayed much more. We programmers have the advantage that many/most of our tools are highly scriptable (by design) and easy to figure out for LLMs. As soon as AI coders figured out how to patch tool calling into LLMs there was this massive leap in utility as LLMs suddenly gained feedback loops based on existing tools that it could suddenly just use.
This has not happened yet for the vast majority of business tools. There are lots of permission and security issues. Proprietary tools that are hard to integrate with. Even things like wordprocessors, spreadsheets, presentation tools, and email/calendar tools remain poorly integrated. You can really see Apple, MS, and Google struggle with this. They are all taking baby steps here but the state of the art is still "copy this blob of text in your tool". Forget about it respecting your document theme, or structure. Agentic tool usage is not widely spread outside the software engineering community yet.
The net result is that the business world still has a lot of drudgery in the form of people manually copying data around between UIs that are mostly not accessible to agentic tools yet. Also many users aren't that tool savvy to begin with. It's unreasonable to expect people like that to be impacted a lot by AI this early in the game. There's a lot of this stuff that is in scope for automating with agentic tools. Most of it is a lot less hard than the type of stuff programmers already deal with in their lives.
Most of the effects this will have on the industry will play out over the next few years. We've seen nothing yet. Especially bigger companies will do so very conservatively. They are mostly incapable of rapid change. Just look at how slow the big trillion dollar companies are themselves with eating their own dog food. And they literally invented and bootstrapped most of this stuff. The rest of the industry is worse at this.
The good news is that the main challenges at this point are non technical: organizational lag, security practices, low level API/UI plumbing to facilitate agentic tool usage, etc. None of this stuff requires further leaps in AI model quality. But doing the actual work to make this happen is not a fast process. From proof of concept to reality is a slow process. Five years would be exceptionally fast. That might actually happen given the massive impact this stuff might have.
No. BOON. A BOON to workplace productivity.
And then the writer doubles down on the error by proving it was not a typo, ending the sentence with "...was for several years a bust."
In the past 6 months, I've gone from Copilot to Cursor to Conductor. It's really the shift to Conductor that convinced me that I crossed into a new reality of software work. It is now possible to code at a scale dramatically higher than before.
This has not yet translated into shipping at far higher magnitude. There are still big friction points and bottlenecks. Some will need to be resolved with technology, others will need organizational solutions.
But this is crystal clear to me: there is a clear path to companies getting software value to the end customer much more rapidly.
I would compare the ongoing revolution to the advent of the Web for software delivery. When features didn't have to be scheduled for release in physical shipments, it unlocked radically different approaches to product development, most clearly illustrated in The Agile Manifesto. You could also do real-time experiments to optimize product outcomes.
I'm not here to say that this is all going to be OK. It won't be for a lot of people. Some companies are going to make tremendous mistakes and generate tremendous waste. Many of the concerns around GenAI are deadly,serious.
But I also have zero doubt that the companies that most effectively embrace the new possibilities are going to run circles around their competition.
It's a weird feeling when people argue against me in this, because I've seen too much. It's like arguing with flat-earthers. I've never personally circumnavigated Antarctica, but me being wrong would invalidate so many facts my frame of reality depends on.
To me, the question isn't about the capabilities of the technology. It's whether we actually want the future it unlocks. That's the discussion I wish we were having. Even if it's hard for me to see what choice there is. Capitalism and geopolitical competition are incredible forces to reckon with, and AI is being driven hard by both.
Unfortunately I think most of the stuff they make will be shit, but they will build it very productively.
I predict a golden age for experienced developers! There will be an uncountable number of poorly designed apps with scaling issues. And many of them will be funded.
This is not good. When all that matters is how viral your app is, people no longer compete on features and quality of life.
"Perfect! Let's delve into the problem with the engine. Based on the symptoms you describe, the likely cause is a blown head gasket..."
filling in pdf documents is effectively the job of millions of people around the world
So you lose a lot of benefits to the time sync, but since people tend to have their eye glaze over when the correction rate is low, you may still miss the 2% anyway.
This is going to put a stop to a lot of ideas that sound reasonable on paper.
I think in retrospect it's going to look very silly.
Which ones? OpenAI? Microsoft? Anthropic?
“Oh cool, copilot is in excel! I’m going to ask it a question about the data in the spreadsheet that it’s literally appearing beside natively in-app, or for help with a formula!”
“Wait what, it’s saying it can’t see anything or read from the currently displayed worksheet? Why is it inside the application then? Why would I want an outdated version of ChatGPT with no useful context or ability to read/do anything inside all my Office applications?”