In today's market, achieving massive growth is seen as the hardest problem to solve. Investors are comfortable backing companies with initially poor retention or margins, like early ChatGPT, as long as they demonstrate hypergrowth. The belief is that growth is paramount, and other metrics can be optimized over time.
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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.
Redpoint Ventures' Erica Brescia describes a shift in their investment thesis for the AI era. They are now more likely to back young, "high-velocity" founders who "run through walls to win" over those with traditional domain expertise. Sheer speed, storytelling, and determination are becoming more critical selection criteria.
Since today's AI companies grow too fast to have multi-year renewal data, investors must adapt their diligence. The focus shifts from long-term retention to short-cycle retention and, crucially, deep product engagement. High usage is the best leading indicator of future stickiness and value.
Redpoint Ventures' Erica Brescia states the current investment thesis for AI application-layer companies: disregard margins entirely for now. The focus should be on aggressive growth, raising capital, and building a brand to be seen as the category winner, even if the product is still early and unprofitable. This is a "play to win" strategy.
Lin warns that much of today's AI revenue is 'experimental,' where customers test solutions without long-term commitment. He calls annualizing this pilot revenue 'a joke.' He advises founders to prioritize slower, high-quality, high-retention revenue over fast, low-quality growth that will eventually churn.
The current AI hype cycle can create misleading top-of-funnel metrics. The only companies that will survive are those demonstrating strong, above-benchmark user and revenue retention. It has become the ultimate litmus test for whether a product provides real, lasting value beyond the initial curiosity.
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.
Venture capitalists may value a solid $15M revenue company at zero. Their model is not built on backing good businesses, but on funding 'upside options'—companies with the potential for explosive, outlier growth, even if they are currently unprofitable.
The industry glorifies aggressive revenue growth, but scaling an unprofitable model is a trap. If a business isn't profitable at $1 million, it will only amplify its losses at $5 million. Sustainable growth requires a strong financial foundation and a focus on the bottom line, not just the top.
Many AI startups prioritize growth, leading to unsustainable gross margins (below 15%) due to high compute costs. This is a ticking time bomb. Eventually, these companies must undertake a costly, time-consuming re-architecture to optimize for cost and build a viable business.
