The companies that are entirely AI-dependent may need to raise prices dramatically as AI prices go up. Not being dependent on LLMs for your fundamental product’s value will be a major advantage, at least in pricing.
It's similar to how if you know what you're doing you can manage a simple VPS and scale a lot more cost effectively than something like vercel.
In a saturated market margins are everything. You can't necessarily afford to be giving all your margins to anthropic and vercel.
Their might always be llms, but the dependence is an interesting topic.
But I'm also afraid / certain that LLMs are able to figure out legacy code (as long as enough fits in their context window), so it's tenuous at best.
Also, funny you mentioned HTML / CSS because for a while (...in the 90's / 2000's) it looked like nobody needed to actually learn those because of tools like Dreamweaver / Frontpage.
Even the briefest of Google searches show they make around the same as any other enterprise dev if not slightly less.
Say you are a good coder now, but you are becoming a custodian, checking the llm work will slowly erode your skills. Maybe if you got a good memory or an amazing skillset it might be some time, but if you don’t use it, you lose it.
FAANG new grads make more. If the COBOL devs had upskilled throughout their career they'd be Senior Staff/Principal+ and making 5-10x more than they do today.
I think that was about 10 years ago…
It's not that clear. Sure, hardware prices are going up due to the extremely tight supply, but AI models are also improving quickly to the point where a cheap mid-level model today does what the frontier model did a year ago. For the very largest models, I think the latter effect dominates quite easily.
> It's not that clear. Sure, hardware prices are going up due to the extremely tight supply, but AI models are also improving quickly to the point where a cheap mid-level model today does what the frontier model did a year ago.
I agree; I got some coding value out of Qwen for $10/m (unlimited tokens); a nice harness (and some tight coding practices) lowers the distance between SOTA and 6mo second-tier models.
If I can get 80% of the way to Anthropic's or OpenAI's SOTA models using 10$/m with unlimited tokens, guess what I am going to do...
How do you explain that capabilities being equal, the cost per token is going down dramatically?
If you play around with the math, you quickly realize that even if we heavily quantize models down to INT4 to save memory, simply scaling the context window (which everyone wants now) immediately eats back whatever VRAM we just saved. The underlying math is extremely unforgiving without fundamentally changing the architecture.
Your company may have the resources to effectively shift to cheaper models without service degradation, but your AI tooling vendors might not. If you pay for 5 different AI-driven tools, that's 5 different ways your upstream costs may increase that you'll need to pass on to customers as well.
Inference prices droped like 90 percent in that time (a combination of cheaper models, implicit caching, service levels, different providers and other optimizations).
Quality went up. Quantity of results went up. Speed went up.
Service level that we provide to our clients went up massively and justfied better deals. Headcount went down.
What's not to like?
Sadly, this is already happening.
Actually some of our analysts are empowered by the tools at their disposal. Their jobs are safe and necessary. Others were let go.
Clients are happy to get fuller picture of their universe, which drives more informed decissions . Everybody wins.
I think more specifically not being dependent on someone else's LLM hardware. IMO having OSS models on dedicated hardware could still be plenty viable for many businesses, granted it'll be some time before future OSS reaches today's SOTA models in performance.
On a small scale that's a tragedy, but there's plenty of analysts that predict an economic crash and recession because there's trillions invested in this technology.
This is the “Building my entire livelihood on Facebook, oh no what?” all over again.
Oh no sorry I forgot, your laptops LLM can draw a potato, let me invest in you.
> We just had a realization during a demo call the other day
These tools have been around for years now. As they've improved, dependency on them has grown. How is any organization only just realizing this?
That's like only noticing the rising water level once it starts flooding the second floor of the house.
So its all a house of cards now, and the moment the bubble bursts is when local open inference has closed the gap. looks like chinese and smaller players already go hard into this direction.
Many users will also seek to go local as insurance against rug pulls from the proprietary models side (We're not quite sure if the third-party inference market will grow enough to provide robust competition), but ultimately if you want to make good utilization of your hardware as a single user you'll also be pushed towards mostly running long batch tasks, not realtime chat (except tiny models) or human-assisted coding.
Hyperscalers are spending a fortune so we think AI = API, but renting intelligence is a business model, not a technical inevitability.
Shameless link to my post on this: https://mjeggleton.com/blog/AIs-mainframe-moment
It's just another instance of cloud dependency, and people should've learned something from that over the last two decades.
So we thought, hmm, “wonder if they are increasing prices to deal with AI costs,” and then projected that into a future where costs go up.
We don’t have this dependence ourselves, so this seems to be a competitive advantage for us on pricing.
Or they'll price the true cost in from the start, and make massive profits until the VC subsidies end... I know which one I'd do.
And I don't really mean new businesses that are entirely built around LLMs, rather existing ones that pivoted to be LLM-dependent – yet still have non-LLM-dependent competitors.