GitHub Copilot and similar tools make good developers more productive. This alone is a genuine use case with some associated value. Is it enough to justify the valuations of OpenAI etc? Probably not by itself. But I expect other industries have similar productivity boosts where people learn to use the tools appropriately.
What’s the total business opportunity of making all knowledge workers 10% more productive (to pick a more modest goal than outright replacement)?
Generative AI only really helps (a meaningful amount) developers where writing code is actually the bottleneck. I have not been in a such a position for years and years.
Same experience here. Today I spent 5 hours debugging code, and wondering how I could put 3 contradicting specifications inside while negotiating some weird stuff that does not concerns me, and then I spent 3 hours deleting 100 lines of code and writing 100 lines of code to please everyone. I fail to see how a LLM could have helped me, and I've been doing this for more than 10 years.
> GitHub Copilot and similar tools make good developers more productive.
Do we have any empirical evidence of this? It seems like it'd be an easy experiment to run - task a number of teams with building a particular product, some with Copilot and some without, and see what happens.
I've tried copilot myself, and at times it makes me feel more productive, but I can't tell if it's truly helping me overall.
It’s doesn’t matter if a minority of passion techies will still be up for new tech, if the average developer just wanting to get the job done and relying on LLMs finds it harder, it will be a significant barrier.
Asking Claude this morning. Seems pretty reasonable and contains the warning about accuracy.
> Michael P. Taylor reassigned Brachiosaurus brancai to the new genus Giraffatitan in 2009. The species became Giraffatitan brancai based on significant anatomical differences from the type species Brachiosaurus altithorax.
> Given that this is quite specific paleontological taxonomy information, I should note that while I aim to be accurate, I may hallucinate details for such specialized questions. You may want to verify this information independently.
Thanks! Yes, that is one motivation. But we also want to learn about how AIs interact with real money and how they make decisions on when to give money to people. Upcoming contests will involve tasks such debating with people, convincing the AI with emotional arguments, etc.
I agree that a lot of human output is stochastic parrot-like. But now and again at least, some human or group of humans appear to come up with something truly original and brilliant. This is something I have yet to see from LLM-based AI, and my guess is it would take a different paradigm to create machines that think in this sense.
I prefer to think of it as “test before commit” or “test before push”.
If I create a pull request with well written code and tests to match, and you can’t tell which came first, the code or the tests, it doesn’t matter in what order they were created.
At least here in Denmark it is very common to start with a bike that is build without pedals (and chain etc). It works really well, one of my kids could ride her pedal bike before 4 years old thanks to starting on a “løbecykel”.
He switched seamlessly from løbecykel to this one with the pedals removed, it was just the same thing, only a bit larger !
Then when he got comfortable with it, I added back the pedals. He got the hang of pedaling in ~10 minutes and a couple of runs down the street. Ever since he has been bike-mobile ! Never even thought about helper wheels.
Great question. Right now, it's first come, first served, and makers can schedule their launches up to 30 days in advance. I don't plan to introduce any paid options to skip the line, it's important to me that it stays fair for everyone.
If it ever gets to a point where demand grows too much, I would explore ways to keep things manageable while still giving every product it's moment to shine. Thanks for brining this up!
Will most organisations be building out their own AI Infrastructure? I would guess in most cases they would either use APIs to existing models (OpenAI etc) or use cloud providers where they need to run and train their own models. Is there a risk that cloud providers cannot meet the demand?
Yes, and those companies are a minority. I wouldn't be surprised if AI providers developed solutions for them. Almost immediately we saw tinybox emerge, old Tesla cards doubled and tripled in price on eBay, and Nvidia announced Digits so there's definitely interest in offline solutions.
What’s the total business opportunity of making all knowledge workers 10% more productive (to pick a more modest goal than outright replacement)?