| >As does the economic picture that justified the massive infrastructure building that’s now broadly funded by a complex network of debt. I have a genuine question and I'd like to hear people's good faith thoughts on this. There's a fair case that open models are threatening to institutions who spent a lot on training proprietary SOTA models. But to my understanding, the massive investment spend (much of it debt backed as you note) is on data centers, chips, physical infra. Yes, this infra is needed to train the models, but it is also needed to serve inference. Perhaps the costs associated with training SOTA models is ultimately a "bust" given open models eroding the SOTA closed model performance advantage. But demand for inference is skyrocketing and there seems to be no end in sight. The physical hardware underpinning inference is in fact a scarce good (currently, and this seems sustainable at least over mid-term). And inference is a scarce service as such. I know that cost of inference constantly goes down as models, technical infrastructure, and applied AI techniques become more efficient (specialized SLMs etc). So this puts downward pressure on prices. But still... demand for inference is just growing like crazy regardless. Putting upwards pressure on prices. Doesn't this mean that all the spending on AI infra is much better positioned to get positive ROI regardless of the type of model being served? Put another way, models seem to be commoditizing, but physical hardware is not (currently). The vast majority of the AI boom spend is on hardware to my understanding (even training capex can be repurposed for inference). Doesn't this suggest that the economics for the "railroads level of build-out spending" are healthier than they might seem at first glance? |
It seems exceedingly unlikely that we will see growth sufficient to justify the level of investment. But as you say that doesn't necessarily mean a worst case scenario either. An investment can be bad without being ruinous after all.