In a rising market, the investors taking the most risk generate the highest returns, making them appear brilliant. However, this same aggression ensures they will be hurt the most when the market turns. This dynamic creates a powerful incentive to increase risk-taking, often just before a downturn.
Even with strong revenue growth, founders should seriously consider M&A offers if their Total Addressable Market (TAM) isn't expanding at a faster rate. A stagnant TAM indicates a future ceiling on value creation, and selling may be the optimal outcome before hitting that wall.
The global payroll market is a 'Wild West' with no dominant, scaled vendor like ADP in the U.S. This makes it a more attractive greenfield opportunity for companies like Deal, compared to the U.S. market where players like Rippling must fight to replace entrenched incumbents in a saturated market.
CIOs report that the unbudgeted 'soft costs' of implementing AI—training, onboarding, and business process change—are the highest they've ever seen. This extreme cost and effort will make companies highly reluctant to switch AI vendors, creating strong defensibility and lock-in for the platforms chosen during this initial wave.
Unlike traditional B2B markets where only ~5% of customers are buying at any time, the AI boom has pushed nearly 100% of companies to seek solutions at once. This temporary gold rush warps perception of market size, creating a risk of over-investment similar to the COVID-era software bubble.
OpenAI's strategy involves getting partners like Oracle and Microsoft to bear the immense balance sheet risk of building data centers and securing chips. OpenAI provides the demand catalyst but avoids the fixed asset downside, positioning itself to capture the majority of the upside while its partners become commodity compute providers.
Poolside, an AI coding company, building its own data center is a terrifying signal for the industry. It suggests that competing at the software layer now requires massive, direct investment in fixed assets. This escalates the capital intensity of AI startups from millions to potentially billions, fundamentally changing the investment landscape.
Spotify's early success stemmed from launching in smaller European countries where record labels had less focus. This allowed them to secure more favorable licensing deals and avoid the costly legal battles and poor margins that strangled their US-based competitors, enabling them to reach critical mass first.
The current mass-adoption phase for AI tools means buying decisions that would normally take 5-7 years are being compressed into 1-2 years. Startups that don't secure customers now risk being shut out, as enterprises will lock in with their chosen vendors for the subsequent half-decade.
The restaurant industry, served by Toast, is the largest B2B vertical. For a new vertical SaaS AI company to justify a valuation exceeding $22B, its target market must be even larger. Since few, if any, verticals are bigger than restaurants, this sets a practical valuation cap and a crucial diligence question for investors.
Despite perceptions of quick wealth, venture capital is a long-term game. Investors can face periods of 10 years or more without receiving any cash distributions (carry) from their funds. This illiquidity and delayed gratification stand in stark contrast to the more immediate payouts seen in public markets or big tech compensation.
The traditional tech compensation hierarchy has inverted. Top AI engineers at companies like Meta are receiving four-year liquid stock packages worth a billion dollars, surpassing the illiquid, long-term carry of even the most successful venture capitalists. This marks a significant shift in the most lucrative roles in tech.