Drawing from the biological principle that cells stop dividing to protect an organism's integrity, companies should moderate growth. Pushing beyond a sustainable rate (e.g., >20% annually) can introduce "mutations" like cultural drift, jeopardizing long-term survival for short-term scale.

The current AI market is like hot, moving fat in a skillet—fluid and competitive. The key strategic question is predicting when "the heat comes off and then everything's fixed." This "congealing" moment will lock in market leaders and make disruption much harder, marking the end of the wild early phase.

Major tech shifts don't immediately destroy jobs. First, they create a "recruiting cycle" with high demand for labor to build the new infrastructure (e.g., car factories). These new, higher-paying jobs attract workers from old industries before those legacy sectors eventually decline.

In a technology boom like the AI trade, capital first flows to core enablers (e.g., NVIDIA). The cycle then extends to first-derivative plays (e.g., data center power) and then to riskier nth-derivative ideas (e.g., quantum computing), which act as leveraged bets and are the first to crash.

Three economists won a Nobel Prize for framing 'creative destruction' as the engine of modern progress. Unlike pre-industrial eras with stagnant growth, the last 200 years have seen constant improvement because society allows new technologies like cars to destroy old industries like horse transport.

A consistent pattern shows innovators adopting the models of legacy players they displaced. YouTube creating cable-like bundles, Coinbase mirroring traditional banks, and Facebook becoming new media illustrates a natural lifecycle where disruptors eventually converge with the industries they set out to revolutionize.

Historical technology cycles suggest that the AI sector will almost certainly face a 'trough of disillusionment.' This occurs when massive capital expenditure fails to produce satisfactory short-term returns or adoption rates, leading to a market correction. The expert would be 'shocked' if this cycle avoided it.

The dot-com era saw ~2,000 companies go public, but only a dozen survived meaningfully. The current AI wave will likely follow a similar pattern, with most companies failing or being acquired despite the hype. Founders should prepare for this reality by considering their exit strategy early.

The biotech ecosystem is a continuous conveyor belt from seed funding to IPO, culminating in acquisition by large biopharma. The recent industry-wide stall wasn't a failure of science, but a halt in M&A activity that backed up the entire system.