Readit NewsIt seems SEO for this sort of thing is dead, so another funnel/channel is needed. Also, CS enrollment seems to be down this past fall for the first time in a while (based on the CERP pulse survey).
But maybe there is still a market for that sort of educational content.
1) Engineering investment at companies generally pays off in multiples of what is spent on engineering time. Say you pay 10 engineers $200k / year each and the features those 10 engineers build grow yearly revenue by $10M. That’s a 4x ROI and clearly a good deal. (Of course, this only applies up to some ceiling; not every company has enough TAM to grow as big as Amazon).
2) Giving engineers near-unlimited access to token usage means they can create even more features, in a way that still produces positive ROI per token. This is the part I disagree with most. It’s complicated. You cannot just ship infinite slop and make money. It glosses over massive complexity in how software is delivered and used.
3) Therefore (so the argument goes) you should not cap tokens and should encourage engineers to use as many as possible.
Like I said, I don’t agree with this argument. But the key thing here is step 1. Engineering time is an investment to grow revenue. If you really could get positive ROI per token in revenue growth, you should buy infinite tokens until you hit the ceiling of your business.
Of course, the real world does not work like this.
In my experience it takes humans to know what to build to generate revenue, and most of the time building that product is not spent coding at all. Coding is like the last step. Spending $1k/day in tokens only makes sense if you know exactly what to build already to generate this revenue. Otherwise you are building what exactly? Is the LLM also doing the job of the business side of the house to decide what to build?
Somebody needs to explain to my lying eyes where these 100xers are hiding. They seem to live in comments on the internet, but I'm not seeing the teams around me increase their output by two orders of magnitude.
This is generous, to the say the least.
Will your bespoke LLM code have that? Every issue will actually be an issue in production experienced by your customers, that will have to be identified (better have good logging and instrumentation), and fixed in your codebase.
I shut it down a while ago because the number of bots overtake traffic. The site had quite a bit of human traffic (enough to bring in a few hundred bucks a month in ad revenue, and a few hundred more in subscription revenue), however, the AI scrapers really started ramping up and the only way I could realistically continue would be to pay a lot more for hosting/infrastructure.
I had put a ton of time into building out content...thousands of hours, only to have scrapers ignore robots, bypass cloudflare (they didn't have any AI products at the time), and overwhelm my measly infrastructure.
Even now, with the domain pointed at NOTHING, it gets almost 100,000 hits a month. There is NO SERVER on the other end. It is a dead link. The stats come from Cloudflare, where the domain name is hosted.
I'm curious if there are any lawyers who'd be willing to take someone like me on contingency for a large copyright lawsuit.
But I think it’s plausible that SaaS companies will be easier to start with AI coding, and with lower costs (thanks to AI) they will be able to get into the black with a smaller addressable market. So each one can have a different mix of fewer features, for different segments of customers, at lower prices.
The result would be a loss of pricing power by the incumbent do-everything big guys: no more baked-in 10% annual increases. Which is still a pretty big change in their economics. And therefore valuations.
On the other hand, deeply understanding how models work and where they fall short, how to set up, organize, and maintain context, and which tools and workflows support that tends to last much longer. When something like the “Ralph loop” blows up on social media (and dies just as fast), the interesting question is: what problem was it trying to solve, and how did it do it differently from alternatives? Thinking through those problems is like training a muscle, and that muscle stays useful even as the underlying technology evolves.
Now because of models improving, context sizes getting bigger, and commercial offerings improving I hardly hear about them.