I don't see a justification for high valuations of companies that aim to build an "AI Software engineer". If something like Devin really succeeds, then anyone can use their product to simply build their own competing AI engineer. There's no moat, it's just another LLM wrapper SaaS.
Yep. The reality is folks building these types of companies are trying to get acquired as quickly as possible before the house of cards fall. This has led to a huge speculative rush of acquisitions to avoid FOMO later.
The technology is nowhere close to what they're hoping for and incremental progress isn't getting us there.
If we get true AGI agents, anyone can also build a multi-billion dollar tech companies on the cheap.
> If we get true AGI agents, anyone can also build a multi-billion dollar tech companies on the cheap.
That's not how the economy works...
You're right - AGI would be unfathomable, it would be more productive than a quadrillion earths entirely populated by MIT valedictorians who just drank 2 espressos each. "Multi-billion dollar" would be a silly valuation.
I don't see this. The ai software engineer that succeeds, maybe it's because of a mixture of very complicated architecture derived from novel research etc. You can't replicate that with just hiring more human engineers, it takes time and effort and elite hiring. Plus enterprise support etc.
Devin etc will give you let's say 10x more engineering power, but not necessarily elite one.
This is my exact takeaway too, and I'm always surprised it doesn't get mentioned often. If AI is truly groundbreaking, then shouldn't AI be able to re-implement itself? Which, to me, would imply that every AI company is not only full of software devs cannibalizing themselves, but the companies themselves also are.
i advise you to not take marketing lines too literally and be so casually dismissive as a result. you will miss a lot of good investments and startups this way and (worse) be lulled into a false sense of comfort and security.
There are any number of tools that already make that promise. Turns out it’s still hard to complete projects and bring them to market.
This is true for LLMs themselves. If a new LLM is really better than all the other ones then it can be used to help improve other LLMs.