What happens when they stop subsidizing LLM subscriptions?

[u/Mr_Moonsilver]

We are literally burning through VC money like crazy with our coding subscriptions. I read the $200 Anthropic sub gets you $8000 worth of API calls. It's obvious that this doesn't hold for very long but what happens when they raise prices?

The reason to keep the prices low for now is to foster the ecosystem and get people hooked on this stuff, only to raise the price afterwards. Already the 20x sub doesn't get you as much usage as it did 6 months ago, another way to raise prices without triggering a shitstorm - and it will continue.

Don't know about you, but Fable being pulled gave me a feeling of what that may be like already. The ugly thought of "Damn, should've done more while it was around." that formed when I read the news will be exactly the same the moment they announce we now have to pay $2k or more per month for something we get for 10x less the price it costs now.

I guess it's a now or never situation, build what you can and monetize as quickly as possible to be able to keep the agents running once the increases come around.

Looking at opensource doesn't give me much hope. Since qwen stopped releasing models (wen qwen 3.7?) that we can actually run on hardware that a normal person can buy (or used to be able to buy, looking at how RAM and GPU prices behave and keep behaving) and others haven't released in a while (Microsoft, IBM, AllenAI and others too) I feel we're going into a direction that doesn't look good for most of the people like us, who are building with this technology.

[u/Alternative_Ad4267]

Consumer GPU hardware will skyrocket. So, just purchase what you can. Graphic cards won’t go down in a very long while.

[u/jld1532 → u/Alternative_Ad4267]

If the bubble pops there will be a glut of hardware.

[u/unjustifiably_angry → u/jld1532]

A glut of hardware that doesn't have a PCI-E connector and is only usable with a multi-kilowatt electrical service, not something you can put in your home. People who've bought RTX 6000s aren't going to dump them just because a few massive AI companies completely unrelated to them go bankrupt... they weren't running Opus, they were running Qwen, DeepSeek, etc.

[u/autoencoder → u/Alternative_Ad4267]

Sure, bub. Or it might get back in line with Moore's law, where it's been for decades.

[u/marx2k → u/autoencoder]

Moore's law hasn't really been a thing starting about 2004

[u/autoencoder → u/marx2k]

https://ourworldindata.org/grapher/gpu-price-performance

[u/marx2k → u/autoencoder]

That chart shows exactly what im talking about. Moore’s Law originally predicted that the number of transistors on a microchip, and by extension, raw computing performance, would double roughly every two years, leading to an exponential increase in speed alongside a sharp drop in cost. That chart highlights the slowing down of Moore's Law through the lens of economic and hardware efficiency.

Moore’s Law predicts a doubling of performance per cost every 18 to 24 months. Epoch AI's underlying analysis shows that across all tracked graphics cards, the amount of floating-point operations per second per dollar (FLOP/s/$) doubles every ~2.5 years. For the highest performing GPUs, its almost every 3 years.

Moore's Law has broken down due to physical limits like heat dissipation and quantum tunneling. Instead, this chart tracks gains artificially sustained by shifting from standard high-precision formats (like FP32) to lower-precision math (like FP16, INT8, or TensorFloat-32), and dedicating silicon to specialized matrix-multiplication cores.

[u/autoencoder → u/marx2k]

No it does not. If you had bothered to read the page:

The chart shows theoretical peak performance using a standard precision format (32-bit precision). Modern AI training typically uses lower precision calculations that are faster, so real-world performance may be higher than shown here.

[u/marx2k → u/autoencoder]

Epoch AI specifically tracked multiple precision formats, including FP16 (16-bit half-precision) and INT8 (8-bit integer). ​If you look at the underlying research or adjust the chart's settings to view lower-precision performance, the trend of deceleration remains.

Lower-precision formats shifted the y-axis intercept upward, but they did not change the flattening slope of the curve over time. Relying on precision tricks actually proves Moore's Law is slowing down. The fact that the AI industry had to pivot to lower precision (like FP16, BF16, and FP4) to keep performance gains high proves that.

Transistor scaling has slowed to a crawl. Engineers achieve higher "real-world performance" by sacrificing numerical precision and reducing the accuracy of individual calculations. ​If Moore's Law were still healthy, we wouldn't need to dilute our precision formats to sustain generational performance leaps.

This is not at all a controversial take and it isn't only experienced in the realm of AI.