And not normal for a company that has been at it this long.

The Apple II went on sale on June 10th, 1977. Visicalc went on sale October 17th, 1979- 860 days separate the two. ChatGPT was opened to the public on November 30th, 2022, which was 1219 days ago- almost 50% more time has elapsed than between the Apple II and Visicalc.

Without me trying to be snarky why do you feel spreadsheet software launching is comparable to this scenario?

Visicalc is often described as the killer app of the first generation Personal Computer(1). It was the product that drove them into every small business in the country, that blew up sales of personal computers and brought them out of the realm of hobbyists into enterprise. And, honestly, I think Visicalc and spreadsheets are still a greater benefit than what I've seen out of generative AI today. And that happened a lot faster than where we are today with generative AI. Apple had enormous actual profits by 1980 (Apple IPO'd in 1980 with a 21% operating margin). So I think that a lot of the "just got to give it more time" argument misses that the previous computer based revolutions that we know about productized and threw off gobs of cash a heck of a lot faster than this one has.

If the end result of this is "certain classes of white collar workers are 10-25% more productive" (which is the best results I can extrapolate from what I've seen so far) then it's really hard to imagine how OpenAI can return a profit to their investors.

1: https://en.wikipedia.org/wiki/VisiCalc#Killer_app is pretty much the normal narrative on Visicalc and its importance to the Personal Computer.

>If the end result of this is "certain classes of white collar workers are 10-25% more productive" (which is the best results I can extrapolate from what I've seen so far) then it's really hard to imagine how OpenAI can return a profit to their investors.

If we take this as face value, and say that the absolute best case scenario is there are literally no other uses for AI but helping programmers program faster, given 4.4 million software devs, with an average cost to the company of $200,000 (working off the US here, including benefits/levels/whatever should be close), those 4.4 million devs with 20% productivity would save roughly 176 billion dollars a year.

Some companies will cut jobs, some will expand features, but that's the gist. And it's hard not to see the magnitude of improvement that's come in just 3 years, though if that leads to a 'moat' is yet to be seen.

> If we take this as face value, and say that the absolute best case scenario is there are literally no other uses for AI but helping programmers program faster, given 4.4 million software devs, with an average cost to the company of $200,000 (working off the US here, including benefits/levels/whatever should be close), those 4.4 million devs with 20% productivity would save roughly 176 billion dollars a year.

I don't think that's necessarily out of line with struggling to return a profit to investors though: an individual company is only ever going to capture a tiny fraction of the productivity improvements it enables its customer base to make[1], its own cost base is unusually high for tech, and investors are seeking a 10x+ return on an $852B valuation for a company that isn't even the market leader in that segment (which isn't the only segment, but it's the optimum B2B one). You can have a great business with a great value proposition and a sustainable moat and still not generate the desired returns on investment at a $852B valuation.

[1]and that's productivity improvements over the best-known free models, not productivity improvements over reading StackOverflow

Thanks for the in depth explanation. I was definitely not up on my tech history here. :)

Sorry, I forgot that for many engineers this is, in fact, their first time going through a technology cycle like this, and so would need more explanation. I am too young for Visicalc myself, but the cycle that I saw while I was in high school- the dot-com bubble- doesn't have convenient, easy to mark out dates like the PC does.

Thinking... Thinking... Tim Berners-Lee proposing HTTP in 1989 is kinda like the original Attention is All You Need paper, I guess? Netscape 1.0 release in December 1994 is ChatGPT 1.0? And then Amazon.com opened up to the public in July 1995 and then IPO'd in May 1997 (after raising less than 10 million dollars in two funding rounds). But once again we have the business side of these previous cycles moving much faster than this one.

WOW. That does really drive home the perspective. I was an adolescent during those years and it did seem quick then, but that's an insane pace in retrospect.

Amazon is perhaps a counter-example to your point, though, to be fair. It seems to me they did a lot of spaghetti throwing while making accounting losses for a good number of years. Granted, they did it on OpenAI's dining budget.

"It seems to me they did a lot of spaghetti throwing while making accounting losses for a good number of years."

They actually didn't. They knew what they were doing. Bezos had a proper background in Finance, unlike Altman.

Yeah I started programming in 2006 as a kid and then entered professional work in 2016 I guess as still a kid depending on your perspective :P

This is really the first meteoric rise in tech I've seen / am experiencing first hand.

All I will say is make sure to enjoy your knees while they don't hurt, whippersnapper.

I took it the other way, spreadsheets shook up the world way more than AI has (to date) - it's possible that history will look back and count AI as the bigger "thing" but if I had to pick a killer app, VisiCalc and computer spreadsheets in general would beat ChatGPT.

Good link from the other day about spreadsheet impact on business https://davidoks.blog/p/how-the-spreadsheet-reshaped-america

Visicalc is widely regarded to be the first "killer app" for the Apple computer. Perhaps even the first "killer app" period.

VisiCalc was the killer app.

Ah got it. I wasn’t drawing that connection. Thanks

VisiCalc was "the" killer app for early micros, but being able to edit a written text on screen and then print it out with letter-like quality was nothing to sneeze at, either. This was plausibly a key gain in efficiency for the service sector, perhaps comparable to the 10%~25% that's now being talked about re: LLM's (which is huge on a secular basis).

IMO, the AI companies are trying to be both T-Mobile and Google Doc at the same time. Even Apple is struggling with being both the platform and the product. The issue with OpenAI is that the platform has no moat (other than money) and the product can be easily copied. In the game console world, the platforms have patents and trademarks, and games are not easily produced.

I'm a retired engineering manager so judge me appropriately :-) I've had 1000 good chats with ChatGPT on a wide range of topics. I build personal Excel and Access applications but not any real programming. I don't need workflow automation although I will dabble with Codex. I'm curious why I should abandon what works for Claude.

You shouldn't. No need to rush to buy a TI-84 to do simple arithmetic. I don't use either because I can learn just find from docs and textbooks. And I don't have that many problems to solve with computing.

The Apple II was so simple (by today's standards) that it came with a complete printed circuit diagram. Visicalc was so simple it was written by two guys in a year.

AI is so many orders of magnitude more complex that the comparison is not really useful.

This complexity requires a lot of money- from investors- to sustain. If those investors don't see a return on their investment before they get too anxious, then no more money will be invested and the business is dead. So that would suggest that there will be even less patience from the money than the investors in Apple had. If you are correct that this greater complexity actually makes it harder to productize, then it is hard to see how frontier model generative AI will be viable under a VC funded domain.

It is entirely plausible to me that there are great technologies that are impossible to reach via the normal means of VC/investor financed capitalism. I certainly have encountered market failures requiring extremely patient money (usually in the form of government subsidies) to produce a useful product that eventually does have market value. That has worked many times in the past. But so far generative AI has not had that, and looking at my non-technology friends, I very much doubt that there would be much support among them for government subsidies of AI companies. AI companies have made too many people unhappy, served as too much of a punching bag, to be in a good position politically for that.

Which is a good thing. Elon has showed the world, the only thing that limits the upper bound is bureaucracy, extreme risk-averse and no culture for experimenting.

More and more companies will start operating on the correct reward/risk curve or else getting crushed by firms who do. OpenAI has forced Google, Apple, Meta out of their comfort zone because they know OpenAI will eat their lunch

True, Elon has really been achieving win after win with Tesla and Twitter.

Literally every part of this comment is confusing. Elon hasn't shown anyone anything interesting in at least a decade. OpenAI hasn't forced Apple to do anything - LLMs aren't impinging on hardware or bundled services, and this literally seems right up Google's alley (and they're arguably better at it than OpenAI has demonstrated, now that first-mover-ish is long past).

I suppose Meta's recent comfort zone was simply a stupid bet on VR, so sure, maybe one part of the comment isn't confusing.

I don't understand what you think you're seeing.

For most companies an $850B valuation means you figured out the business model. Here it seems to mean you convinced enough people that figuring it out is inevitable. Those are very different bets.

I'm curious how long it took Google to reach that valuation.