Unlike past platform shifts that caught many off-guard, the AI wave is universally anticipated. This 'consensus innovation' intensifies all existing competitive pressures, as every investor—from mega-funds to accelerators—is aggressively pursuing the same perceived opportunities, pushing factors like Power Law belief to an extreme.

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.

The current fundraising environment is the most binary in recent memory. Startups with the "right" narrative—AI-native, elite incubator pedigree, explosive growth—get funded easily. Companies with solid but non-hype metrics, like classic SaaS growers, are finding it nearly impossible to raise capital. The middle market has vanished.

Before GenAI, the key question for seed investors was whether a product created real value. Now, with AI enabling obvious value creation, the primary concern has become defensibility. Investors are now focused on a startup's ability to compete with big tech, incumbents, and foundation models.

The recent surge in demo days and YC-style incubators from major VCs is a delayed reaction to the valuation boom of two years ago. These programs are a strategic play to get cheap, early-stage access to a wide portfolio of AI companies, de-risking entry into a hyped and uncertain market where good ideas are hard to differentiate.

For a proven, hyper-growth AI company, traditional business risks (market, operational, tech) are minimal. The sole risk for a late-stage investor is overpaying for several years of future growth that may decelerate faster than anticipated.

Unlike prior tech cycles with a clear direction, the AI wave has a deep divide. SaaS vendors see AI enhancing existing applications, while venture capitalists bet that AI models will subsume and replace the entire SaaS application layer, creating massive disruption.

Contrary to common belief, the earliest AI startups often command higher relative valuations than established growth-stage AI companies, whose revenue multiples are becoming more rational and comparable to public market comps.

Perplexity's CEO argues that building foundational models is not necessary for success. By focusing on the end-to-end consumer experience and leveraging increasingly commoditized models, startups can build a highly valuable business without needing billions in funding for model training.