A market bifurcation is underway where investors prioritize AI startups with extreme growth rates over traditional SaaS companies. This creates a "changing of the guard," forcing established SaaS players to adopt AI aggressively or risk being devalued as legacy assets, while AI-native firms command premium valuations.

Companies feel immense pressure to integrate AI to stay competitive, leading to massive spending. However, this rush means they lack the infrastructure to measure ROI, creating a paradox of anxious investment without clear proof of value.

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.

Unlike previous tech waves that trickled down from large institutions, AI adoption is inverted. Individuals are the fastest adopters, followed by small businesses, with large corporations and governments lagging. This reverses the traditional power dynamic of technology access and creates new market opportunities.

In AI M&A, recency is key. Companies pre-ChatGPT often had to rewrite their entire stack and relearn skills, making their experience less relevant. Acquiring a company with post-ChatGPT experience ensures their tech and knowledge are current, not already obsolete.

Unlike mobile or internet shifts that created openings for startups, AI is an "accelerating technology." Large companies can integrate it quickly, closing the competitive window for new entrants much faster than in previous platform shifts. The moat is no longer product execution but customer insight.

Wikipedia was initially dismissed by academia as unreliable. Over 15 years, its decentralized, community-driven model built immense trust, making it a universally accepted source of truth. This journey from skepticism to indispensability may serve as a blueprint for how society ultimately embraces and integrates artificial intelligence.

In the current AI landscape, knowledge and assumptions become obsolete within months, not years. This rapid pace of evolution creates significant stress, as investors and founders must constantly re-educate themselves to make informed decisions. Relying on past knowledge is a quick path to failure.

Many technical leaders initially dismissed generative AI for its failures on simple logical tasks. However, its rapid, tangible improvement over a short period forces a re-evaluation and a crucial mindset shift towards adoption to avoid being left behind.