Contrary to the remote-first trend, Crisp.ai's founder advises against a fully distributed model for initial product development. He argues for gathering the core team in one physical location to harness the energy and efficiency of in-person collaboration. Distributed teams are better suited for iterating on an already established product.
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In a remote environment, immediate access to colleagues isn't always possible. A GPT loaded with context about your company and cofounders' thinking can act as a thought partner, helping you overcome the "blank slate" problem without scheduling a meeting.
The biggest downside of remote work isn't lost productivity, but the elimination of serendipity. It removes the chance encounters that lead to friendships, mentorship, and cross-pollination of ideas. For those needing to build a network, the convenience of working from home comes at the high cost of isolation and stunted growth.
AI will make the traditional "product pod" structure obsolete for design. Designers, empowered to learn contexts faster and cover more ground, will operate in a more fluid, centralized team. They will be deployed across entire user journeys that span multiple teams, rather than being calcified within a single product area.
CZI's Biohub model hinges on a simple principle: physically seating biologists and engineers from different institutions (Stanford, UCSF, Berkeley) together. This direct proximity fosters collaboration and creates hybrid experts, overcoming the institutional silos often reinforced by traditional grant-based funding.
In an AI-driven world, product teams should operate like a busy shipyard: seemingly chaotic but underpinned by high skill and careful communication. This cross-functional pod (PM, Eng, Design, Research, Data, Marketing) collaborates constantly, breaking down traditional processes like standups.
The traditional, linear handoff from product spec to design to code is collapsing. Roles and stages are blurring, with interactive prototypes replacing static documents and the design file itself becoming the central place for the entire team to align and collaborate.
In AI, low prototyping costs and customer uncertainty make the traditional research-first PM model obsolete. The new approach is to build a prototype quickly, show it to customers to discover possibilities, and then iterate based on their reactions, effectively building the solution before the problem is fully defined.
To launch new products and compete with agile startups, embed a small "incubation seller" team directly within the technology organization. This model ensures tight alignment between product, engineering, and the first revenue-generating efforts, mirroring the cross-functional approach of an early-stage company.
Stripe's Experimental Projects Team discovered that embedding its members directly within existing product and infrastructure teams leads to higher success rates. These "embedded projects" are more likely to reach escape velocity and be successfully adopted by the business, contrasting with the common model of an isolated R&D or innovation lab.
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.
