While more data and compute yield linear improvements, true step-function advances in AI come from unpredictable algorithmic breakthroughs like Transformers. These creative ideas are the most difficult to innovate on and represent the highest-leverage, yet riskiest, area for investment and research focus.

The AI race has been a prisoner's dilemma where companies spend massively, fearing competitors will pull ahead. As the cost of next-gen systems like Blackwell and Rubin becomes astronomical, the sheer economics will force a shift. Decision-making will be dominated by ROI calculations rather than the existential dread of slowing down.

In the current market, AI companies see explosive growth through two primary vectors: attaching to the massive AI compute spend or directly replacing human labor. Companies merely using AI to improve an existing product without hitting one of these drivers risk being discounted as they lack a clear, exponential growth narrative.

For years, access to compute was the primary bottleneck in AI development. Now, as public web data is largely exhausted, the limiting factor is access to high-quality, proprietary data from enterprises and human experts. This shifts the focus from building massive infrastructure to forming data partnerships and expertise.

Instead of relying on hyped benchmarks, the truest measure of the AI industry's progress is the physical build-out of data centers. Tracking permits, power consumption, and satellite imagery reveals the concrete, multi-billion dollar bets being placed, offering a grounded view that challenges both extreme skeptics and believers.

For consumer products like ChatGPT, models are already good enough for common queries. However, for complex enterprise tasks like coding, performance is far from solved. This gives model providers a durable path to sustained revenue growth through continued quality improvements aimed at professionals.

While on-device AI for consumer gadgets is hyped, its most impactful application is in B2B robotics. Deploying AI models on drones for safety, defense, or industrial tasks where network connectivity is unreliable unlocks far more value. The focus should be on robotics and enterprise portability, not just consumer privacy.

Unlike traditional SaaS where high switching costs prevent price wars, the AI market faces a unique threat. The portability of prompts and reliance on interchangeable models could enable rapid commoditization. A price war could be "terrifying" and "brutal" for the entire ecosystem, posing a significant downside risk.

For the first time, a disruptive technology's most advanced capabilities are available to the public from day one via consumer apps. An individual with a smartphone has access to the same state-of-the-art AI as a top VC or Fortune 500 CEO, making it the most democratic technology in history.