Ask HN: Why aren't companies with unlimited AI tokens not crushing it?
I hear of software companies with unlimited Claude and Cursor subscriptions offered to their employees. But I am not reading stories of these companies with such massive token resources crushing their markets with either profit growth or revenue growth. Given that their token costs are effectively uncapped, why am I not reading of their growth on the other side of the PNL?I hear of companies with unlimited Claude and Cursor subscriptions, but I am not reading stories of these companies with such massive resources crushing their markets with either profit growth or revenue growth. Given that their
What am I missing?
3 comments
The people selling those services have worked really hard to get everyone else to think that more AI usage inevitably leads to more success (and inversely, that too little of it is a signal that you're being left behind), but I have yet to see this actually backed up by anything else than marketing.
LLMs remain a tool, they amplify what's there (good and bad), but the real predictor of success is the humans using it. Think of it as baking: adding LLMs to your toolbox is a lot like getting moving from mixing dough by hand to having a stand mixer. You'll be able to mix a lot more dough with the mixer, maybe even have your chefs and apprentices do something else than mixing dough for hours. Even if you deck out your kitchen with the latest machines though, your bread will only be as good as the knowhow of your people and the recipes you use. The same applies to software.
One of the key selling points of LLMs has been that it makes execution cheaper (in software-land, at least). This is arguable, but assuming it does, execution is still not the bottleneck as they'd like you to think. In my experience, deciding what to build, collecting insights on what's out there and how the rubber meets the road with users, and building organizational alignment on the path ahead remains the hardest, most tedious part of the process. LLMs may help with some of it, but in the end, this is still people-driven, and regardless of how many tokens or fancy models you have at your disposal, that's what will decide if your solution to a problem stands out against your competition.
LLMs remain a tool, they amplify what's there (good and bad), but the real predictor of success is the humans using it. Think of it as baking: adding LLMs to your toolbox is a lot like getting moving from mixing dough by hand to having a stand mixer. You'll be able to mix a lot more dough with the mixer, maybe even have your chefs and apprentices do something else than mixing dough for hours. Even if you deck out your kitchen with the latest machines though, your bread will only be as good as the knowhow of your people and the recipes you use. The same applies to software.
One of the key selling points of LLMs has been that it makes execution cheaper (in software-land, at least). This is arguable, but assuming it does, execution is still not the bottleneck as they'd like you to think. In my experience, deciding what to build, collecting insights on what's out there and how the rubber meets the road with users, and building organizational alignment on the path ahead remains the hardest, most tedious part of the process. LLMs may help with some of it, but in the end, this is still people-driven, and regardless of how many tokens or fancy models you have at your disposal, that's what will decide if your solution to a problem stands out against your competition.
token spend does not necessarily correlate with revenue
if all them have 'unlimited AI tokens', then that isn't an edge anymore, it becomes just the 'basic', the baseline.