Ramp's hiring philosophy prioritizes a candidate's trajectory and learning velocity ("slope") over their current experience level ("intercept"). They find young, driven individuals with high potential and give them significant responsibility. This approach cultivates a highly talented and loyal team that outperforms what they could afford to hire on the open market.
While OpenAI's projected multi-billion dollar losses seem astronomical, they mirror the historical capital burns of companies like Uber, which spent heavily to secure market dominance. If the end goal is a long-term monopoly on the AI interface, such a massive investment can be justified as a necessary cost to secure a generational asset.
Traffic from ChatGPT to e-commerce sites converts at an exceptionally high rate (12% for one brand, compared to a typical 1-2%). This demonstrates that users turning to AI for product research have extremely high purchase intent by the time they click a link, making AI chat a powerful and potentially lucrative channel for advertisers.
Despite theories that Google will offer its AI for free to bankrupt competitors, its deep-seated corporate culture of high margins (historically 80%+) makes a prolonged, zero-profit strategy difficult. As a public company, Google faces immense investor pressure to monetize new technologies quickly, unlike a startup.
Specialized chips (ASICs) like Google's TPU lack the flexibility needed in the early stages of AI development. AMD's CEO asserts that general-purpose GPUs will remain the majority of the market because developers need the freedom to experiment with new models and algorithms, a capability that cannot be hard-coded into purpose-built silicon.
Despite the hype around large language models, they represent a minority of AI compute usage at a tech giant like Meta. The vast majority of AI capital expenditure is dedicated to other tasks like content recommendation and ad placement, highlighting the continued importance of diverse, non-LLM AI systems in large-scale operations.
Companies like DeepMind, Meta, and SSI are using increasingly futuristic job titles like "Post-AGI Research" and "Safe Superintelligence Researcher." This isn't just semantics; it's a branding strategy to attract elite talent by framing their work as being on the absolute cutting edge, creating distinct sub-genres within the AI research community.
Unlike the asset-light software era dominated by venture equity, the current AI and defense tech cycle is asset-heavy, requiring massive capital for hardware and infrastructure. This fundamental shift makes private credit a necessary financing tool for growth companies, forcing a mental model change away from Silicon Valley's traditional debt aversion.
According to Apollo's co-president, increasing questions around the off-balance-sheet debt used by AI labs to finance GPUs will pressure them to go public sooner than anticipated. An IPO would provide access to more traditional and transparent capital markets, such as convertible debt and public equity, to fund their massive infrastructure needs.
Fintech giant Ramp attributes its early hiring success to building in New York City. Unlike the hyper-competitive, short-tenure culture of Silicon Valley at the time, NYC offered a pool of talented engineers seeking long-term roles. This talent arbitrage allowed Ramp to build a stable, high-quality team and "punch way above its weight."