Weeb

After taking some time off to refocus, I’ve evolved from building in crypto communities to studying and building digital growth systems. Here’s what that means, and what I’m building next 🧵

@andrewchen This flips where the network effect lives. In traditional marketplaces, the edge was aggregating supply. In agentic systems, the edge shifts to controlling the interface where demand is expressed. That’s where the new moat forms.

marketplace startups are destined to be massively reinvented by AI. The weak form is already happening, where we use LLMs for customer support, supply/demand matching, etc. That’s easy The strong form is to figure out how much of the supply side of the marketplace can be turned agentic and ultimately, robotic. “Uber for X” will have consumers requesting robots to do X. Every on-demand service of the 2010s will instruct a robotaxi or delivery robot. Or if you’re prev used a marketplace to hire X, then you “hire” an agent instead. You won’t need to app developer, because there’s agents to build your app This will impact marketplace cos differently. Of course some marketplaces - like Airbnb - inherently work in the physical and will leverage AI around the core value prop. And some are bound to lose their network effects as matching fragmented supply/demand turns into an AI problem. Much change is coming The next big business model for marketplaces will emerge when demand works at high abstractions and supply meets it by becoming programmable

@pitdesi agents → identity → governance → action

lol even the Trumps are doing agentic payments

@OfficialLoganK @GoogleAIStudio We’re watching coding move from a skill to an interface. When databases, auth, and agents are all one click away, the bottleneck shifts from building to knowing what to build.

Introducing the all new vibe coding experience in @GoogleAIStudio, feating: - One click database support - Sign in with Google support - A new coding agent powered by Antigravity - Multiplayer + backend app support and so much more coming soon! x.com/GoogleAIStudio…

I am curious where people are seeing this shift most clearly right now. Feels like it’s happening unevenly across industries.

AI is collapsing the cost of execution. So the advantage is moving up the stack: from building → to deciding → to distributing. Most people haven’t caught up yet. Here’s what’s actually happening ↓

@saylor @Strategy Risk-adjusted returns depend on the framework you use. If your time horizon is long enough, volatility starts to look like noise and asymmetry becomes the signal. That’s where the real debate is.

If you’re looking for risk-adjusted returns, there is no second best. $STRC

@HarryStebbings @gradypb @andrew__reed That question is getting harder to answer in the AI era. Execution cycles are compressing so fast that “best days ahead” depends less on what you’ve built and more on how quickly you can adapt. Longevity is starting to look like rate of iteration.

The one question I always ask with companies is: Do I believe their best days are ahead or behind them? With Sequoia being run by @gradypb and @andrew__reed it was so clear to me I am so excited for their days ahead. Seeing this with the return of the OG, that excitement goes up another level.

If you’re building right now: Don’t just ask “can we build this?” Ask: “should this exist, and who will it reach?”

Execution used to be the moat. Now it’s the baseline. Strategy and distribution are the new edge.

@levie Agents are forcing enterprises to rethink the stack. It’s no longer just: data → software → workflows Now it’s: data → agents → decisions → actions Which is why identity, governance, and interoperability suddenly become the real bottlenecks.

Had meetings and a dinner with 20+ enterprise AI and IT leaders today. Lots of interesting conversations around the state of AI in large enterprises, especially regulated businesses. Here are some of general trends: * Agents are clearly the big thing. Enterprises moving from talking about chatbots to agents, though we’re still very early. Coding is still the dominant agentic use-case being adopted thus far, with other categories of across knowledge work starting to emerge. Lots of agentic work moving from pilots and PoCs into production, and some enterprises had lots of active live use-cases. * Agentic use-cases span every part of a business, from back office operations to client facing experiences from sales to customer onboarding workflows. General feeling is that agentic workflows will hit every part of an organization, often with biggest focus on delivering better for customers, getting better insights and intelligence from data and documents, speeding up high ROI workflows with agents, and so on. Very limited discussion on pure cost cutting. * Data and AI governance still remain core challenges. Getting data and content into a spot that agents can securely and easily operate on remains a huge task for more organizations. Years of data management fragmentation that wasn’t a problem now is an issue for enterprises looking to adopt agents. And governing what agents can do with data in a workflow still a major topic. * Identity emerging as a big topic. Can the agent have access to everything you have? In a world of dozens of agents working on behalf, potentially too much data exposure and scope for the agents. How do we manage agents with partitioned level of access to your information? * Lots of emerging questions on how we will budget for tokens across use-cases and teams. Companies don’t want to constrain use-cases, but equally need to be mindful of ultimate token budgets. This is going to become a bigger part of OpEx over time, and probably won’t make sense to be considered an IT budget anymore. Likely needs to be factored into the rest of operating expenses. * Interoperability is key. Every enterprise is deploying multiple AI systems right now, and it’s unlikely that there’s going to be a single platform to rule them all. Customers are getting savvier on how to handle agent interoperability, and this will be one of the biggest drivers of an AI stack going forward. Lots more takeaways than just this, but needless to say the momentum is building but equally enterprises are acutely aware of the change management and work ahead. Lots of opportunity right now.

@amrishrau The role doesn’t disappear. It compresses. Less roadmap management, more product judgment + distribution thinking.

Product Management will see a massive change. PMs will become lot more technical or they will become product marketing managers. The current won’t exist.

@reddy2go Decentralization scales trust in digital systems. But real-world outcomes require coordination, not just consensus. That’s where most “onchain everything” ideas break down.

> whether decentralized systems can reliably produce real-world outcomes pertinent point to ponder 🤔

@pitdesi Design doesn’t disappear. It gets absorbed. When the tools improve, the differentiation moves from execution to taste and decision-making.

Figma got crushed on this one :/ Anecdotally seeing more companies task design work to the product team already.