To stay relevant, tech platform companies must obsessively follow developers and startups. They are the primary source of insight into emerging workloads and platform requirements. This isn't just for partnerships, but for fundamental product strategy and learning.
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OpenAI embraces the 'platform paradox' by selling API access to startups that compete directly with its own apps like ChatGPT. The strategy is to foster a broad ecosystem, believing that enabling competitors is necessary to avoid losing the platform race entirely.
Top product teams like those at OpenAI don't just monitor high-level KPIs. They maintain a fanatical obsession with understanding the 'why' behind every micro-trend. When a metric shifts even slightly, they dig relentlessly to uncover the underlying user behavior or market dynamic causing it.
A platform's immediate user is the developer. However, to demonstrate true value, you must also understand and solve for the developer's end customer. This "two-hop" thinking is essential for connecting platform work to tangible business outcomes, not just internal technical improvements.
There appears to be a predictable 5-10 year lag between a startup's innovation gaining traction (e.g., Calendly) and a tech giant commoditizing it as a feature (e.g., Google Calendar's scheduling). This "commoditization window" is the crucial timeframe for a startup to build a brand, network effects, and a durable moat.
As AI makes software creation faster and cheaper, the market will flood with products. In this environment of abundance, a strong brand, point of view, taste, and high-quality design become the most critical factors for a product to stand out and win customers.
As AI and no-code tools make software easier to build, technological advantage is no longer a defensible moat. The most successful companies now win through unique distribution advantages, such as founder-led content or deep community building. Go-to-market strategy has surpassed product as the key differentiator.
It's not enough for platform PMs to interview their direct users (developers). To build truly enabling platforms, you must also gain wider context by sitting in on the developers' own customer interviews. This provides deep empathy for the entire value chain, leading to better platform decisions.
Large platforms focus on massive opportunities right in front of them ('gold bricks at their feet'). They consciously ignore even valuable markets that require more effort ('gold bricks 100 feet away'). This strategic neglect creates defensible spaces for startups in those niche areas.
Instead of building a single-purpose application (first-order thinking), successful AI product strategy involves creating platforms that enable users to build their own solutions (second-order thinking). This approach targets a much larger opportunity by empowering users to create custom workflows.
The rapid evolution of AI makes traditional product development cycles too slow. GitHub's CPO advises that every AI feature is a search for product-market fit. The best strategy is to find five customers with a shared problem and build openly with them, iterating daily rather than building in isolation for weeks.
