As AI and better tools commoditize software creation, traditional technology moats are shrinking. The new defensible advantages are forms of liquidity: aggregated data, marketplace activity, or social interactions. These network effects are harder for competitors to replicate than code or features.

The historic rotation between asset-light (tech) and asset-heavy (commodities) industries is breaking down. AI requires massive physical infrastructure (data centers), turning 'bits' companies into 'atoms' companies and creating huge new demand for energy and materials.

AI acts as a multiplier, not an equalizer. Companies with strong moats like proprietary data and network effects will leverage AI to accelerate growth, while those with commoditized offerings will see their decline hasten.

Infrastructure investing, once seen as stable (e.g., toll roads), is now linked to the fast-paced tech sector via AI's needs. This introduces a new risk: rapid technological upgrades can devalue physical assets like cooling systems overnight, creating tech-like volatility.

The 50-year supremacy of asset-light software may be an anomaly. If AI makes software creation nearly free, economic value will shift back to the historical mean: tangible assets like infrastructure, energy, and regulated, liability-bearing businesses that touch the physical world.

Much like 'big data' evolved from a competitive advantage into a widely available commodity, AI models will likely follow the same path. So many sources will offer powerful models that they will cease to be a unique differentiator or a durable moat for businesses.

The massive capital expenditure to train a frontier AI model becomes nearly worthless in months as competitors release superior models. This makes trained models a uniquely fast-depreciating asset, creating immense pressure on labs to monetize quickly through API access or investor hype before their technological advantage evaporates completely.

Contrary to current hype, the long-term value of AI and robotics won't be held by the technology creators. Instead, it will be captured by vertically integrated physical businesses that use this increasingly cheap and replicable technology to create defensible moats through superior operations, speed, and cost.

Contrary to the 'winner-takes-all' narrative, the rapid pace of innovation in AI is leading to a different outcome. As rival labs quickly match or exceed each other's model capabilities, the underlying Large Language Models (LLMs) risk becoming commodities, making it difficult for any single player to justify stratospheric valuations long-term.