Surveys show public panic about AI's impact on jobs and society. However, revealed preferences—actual user behavior—show massive, enthusiastic adoption for daily tasks, from work to personal relationships. Watch what people do, not what they say.
The computer industry originally chose a "hyper-literal mathematical machine" path over a "human brain model" based on neural networks, a theory that existed since the 1940s. The current AI wave represents the long-delayed success of that alternate, abandoned path.
While an operating company must commit to a single, coherent strategy, a venture portfolio can invest in opposing models simultaneously (e.g., big vs. small models, open vs. closed source). This allows VCs to win regardless of which future unfolds.
The common critique of AI application companies as "GPT wrappers" with no moat is proving false. The best startups are evolving beyond using a single third-party model. They are using dozens of models and, crucially, are backward-integrating to build their own custom AI models optimized for their specific domain.
GPUs were designed for graphics, not AI. It was a "twist of fate" that their massively parallel architecture suited AI workloads. Chips designed from scratch for AI would be much more efficient, opening the door for new startups to build better, more specialized hardware and challenge incumbents.
The current GPU shortage is a temporary state. In any commodity-like market, a shortage creates a glut, and vice-versa. The immense profits generated by companies like NVIDIA are a "bat signal" for competition, ensuring massive future build-out and a subsequent drop in unit costs.
The cost of AI, priced in "tokens by the drink," is falling dramatically. All inputs are on a downward cost curve, leading to a hyper-deflationary effect on the price of intelligence. This, in turn, fuels massive demand elasticity as more use cases become economically viable.
Unlike electricity or the internet itself, which required massive physical infrastructure build-outs over decades, AI can be "downloaded" instantly by 5+ billion people. The internet acts as a pre-built carrier wave, enabling a rate of adoption never before seen in technological history.
Big tech companies are offering their most advanced AI models via a "tokens by the drink" pricing model. This is incredible for startups, as it provides access to the world's most magical technology on a usage basis, allowing them to get started and scale without massive upfront capital investment.
Andreessen argues that Silicon Valley's core strength is not any specific technology, but its unique ecosystem for recycling talent and capital from previous cycles into new ones. This creates the critical mass and enthusiasm needed for each technological revolution, like AI, to take off.
Marc Andreessen observes that once a company demonstrates a new AI capability is possible, competitors can catch up rapidly. This suggests that first-mover advantage in AI might be less durable than in previous tech waves, as seen with companies like XAI matching state-of-the-art models in under a year.
The naive view is that lower prices are always better for customers. However, higher prices generate higher margins, which can be reinvested into R&D. This allows the vendor to improve the product much faster, ultimately delivering more value and making the customer better off than with a cheaper, stagnant product.
While the most powerful AI will reside in large "god models" (like supercomputers), the majority of the market volume will come from smaller, specialized models. These will cascade down in size and cost, eventually being embedded in every device, much like microchips proliferated from mainframes.
The emergence of high-quality, open-source AI models from China (like Kimi and DeepSeek) has shifted the conversation in Washington D.C. It reframes AI development from a domestic regulatory risk to a geopolitical foot race, reducing the appetite for restrictive legislation that could cede leadership to China.
The EU's AI Act has been so restrictive that it has largely killed native AI development in Europe. The regulation is so punitive that even major American companies like Apple and Meta are choosing not to launch their leading-edge AI capabilities there, demonstrating the chilling effect of preemptive, overbearing regulation.