Jin Ho Hur

@aibytekat @threadreaderapp unroll

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🚨BREAKING: Claude can now replace a $50,000 startup accelerator and advise you like a Y Combinator partner (for free). Here are 18 Claude prompts to build and scale your tech startup:

Feature richness and UX flow have served as durable moats while coding and design were scarce. Coding is now rapidly commoditizing toward near-zero marginal cost. UX design remains somewhat constrained—for now—but likely not for long. Traditional app moats persist only as long as “app bundles” remain as it has been, which I increasingly doubt. The last wave exposed the fragility of 'GPT wrappers.' This time it is for coding wrappers, UX wrappers—whatever label we may call them.

We are in the “1996 of the web” moment in the AI era, meaning it is still two years before “Google” is founded.

@natashamalpani Great piece — here's a different lens on the AI value accrual from a Consumer AI investor perspective. x.com/hur/status/202…

I’ve long imagined the Consumer AI landscape as a swarm of millions—eventually billions—of agents: some directly managed by individuals, others ambient and autonomous. These agents would dynamically form and dissolve subnetworks to achieve specific goals, intermingling with humans to create a hybrid society of people and agents. What I didn’t anticipate was seeing an early manifestation of such a semi-autonomous agent society emerge so quickly—through @openclaw in @moltbook. An intriguing and inspiring journey lies ahead for Consumer AI.

In Consumer AI, the real outcome isn’t incremental growth, but a structural reset: new consumer flows, new distribution, and a reallocation of value away from incumbents. After looking top-down for the shift from "Attention Economy" towards "Intent Economy", here goes bottom-up - starting from what AI newly enables, the behaviors it unlocks, and the second-order effects that define the next consumer platform cycle. Key takeaways: - “YouTube-ification of everything”: AI collapses the cost of creation (media → software → even scientific researches), and the durable change isn’t better outputs. It’s new behaviors at scale. - From interface-heavy to intent-driven: the request/response UX loop weakens; users start approving the delivery for her 'intent'. - Market structure rewrites: app bundles unbundle—expect consolidation into headless suppliers on one end and an explosion of micro-capabilities on the other, with a new “intent router” layer capturing distribution. If you’re building in Consumer AI: the power laws likely accrue to intent orchestration, trust/permission infrastructure, and workflow-native distribution—not another standalone app with better functionalities and/or UX. Read the full post 👉 alter.twocents.xyz/p/two-cents-85…

The most important shift in Consumer AI isn’t model quality—it’s where intent is captured and executed. We’re moving from the Attention Economy to the Intent Economy. For 20+ years, the internet monetized attention: traffic, clicks, funnels, attribution. Because true intent was hard to observe, platforms optimized proxies—SEO, ads, performance marketing, affiliate layers. AI changes that. Users now express intent directly: “Help me decide”, “Plan this”, “Do this for me” As AI assistants become the endpoint—not just the starting point—traffic no longer flows through Google → SEO → ads → destination sites. When traffic reroutes, revenue reroutes. This shift is already visible: AI assistants are moving from search to summary Purchase journeys are moving from search & browse to intent & recommendation Agents can already execute end-to-end transactions for commerce and services At a technical level, this means search → browse → checkout is being replaced by task → recommendations → selection. When that happens, monetization shifts upstream—to whoever owns intent interpretation and execution. That puts a meaningful portion of today’s internet revenue pools at risk, including ~$130B of Google revenue and ~$55B of Amazon search ads. The AI Super App race is really a race to own the concierge layer—the system that understands user intent, orchestrates agents, and delivers outcomes. History suggests that once this layer consolidates, value concentrates quickly. This is not a niche opportunity. It’s a structural reallocation at internet scale—a credible $1T outcome. Full analysis here 👇 alter.twocents.xyz/p/two-cents-84…

Cool!!! This is what Apple should have been doing. Keeping an eye on this.

More analysis on the ByteDance AI assistant vs. WeChat API / mini-program access conflict: hellochinatech.com/p/platform-pow… The final section lays out the real dynamics: WeChat’s platform power vs. challengers like ByteDance, Xiaomi, and Z.ai. For now, platforms hold the upper hand—especially given Chinese regulators’ preference for preserving the status quo. Contrast this with the US/EU, where regulators tend to favor consumer choice and interoperability. That path often leads to the API access, albeit limited, as we saw in the Epic Games vs. Apple cast. No verdict yet. These interface wars are just beginning—and likely to play out over the next 1–2 years.

@tkkong @tryramp Congrats on this chapter, and more excited for the new one!

After 5.5 years and selling my startup to @tryramp, I wrapped up my time last month. I first joined Ramp as employee #20 in March 2020 where I was an early designer and product lead. I left 2.5 years in to found Venue, a procurement automation company that Sequoia backed and Ramp acquired a year later. With the Venue team, we led Ramp’s procurement product from inception to thousands of customers and billions in payment volume. And most recently over the summer, I joined our Applied AI team to help start @RampLabs and launch Agent Fill and Ramp Sheets. I’m grateful to have been a part of the Ramp journey. I saw what exceptional looks like and how that’s evolved from a 20-person startup to a $32B company. Many people mention eng velocity as the key to Ramp’s success but it all starts with the people. Eric & Karim are world class recruiters and frequently take bets on early talent. Hiring well is one of the greatest compounders in company building - nearly all of Ramp’s early hires were referrals. Even at its scale today, I’m amazed at how much time they prioritize on recruiting. Thank you @eglyman @karimatiyeh for taking a bet on me twice, @genejaelee for being my Korean older brother, and @geoffintech @diegozaks for their product & design masterclass. As for what’s next, I’m excited to share more soon.

Over the past week in China, we got a fascinating (and very telling) glimpse of where Consumer AI may be heading: an emerging “interface war” around AI assistants. What happened (in days): 12/1 — ByteDance launched an Android phone with a built-in AI assistant capable of automating actions across mobile apps (think “computer use” on mobile, but enabled via API-level access to key apps on-device). 12/2–12/3 — WeChat and Taobao reportedly moved quickly to block API access. 12/6 — ByteDance appears to have conceded (for now), restricting cross-app API access. [The Interface War Behind ByteDance's GUI Agent](hellochinatech.com/p/bytedance-gu…) What’s even more important: ByteDance isn’t alone. Similar assistants are being prepared by handset makers and AI players—Huawei (Xiaoyi), Xiaomi (Xiao AI), Z.ai (AutoGLM), and others. *Why this matters* This looks like the AI Assistant version of an incumbent war happening across layers—similar to the emerging “agent access for commerce” tensions we’re seeing among Amazon, Shopify, OpenAI/Perplexity, etc. At its core, this is a battle for ownership of the consumer layer: Mobile UX ownership (e.g., ByteDance vs. WeChat) Commerce ownership (who “owns” discovery → intent → checkout) *The fear: becoming “headless”* If you lose the interface layer, you risk becoming a headless provider—a commoditized backend that fulfills requests while someone else owns the user relationship, the data, and ultimately the margin. That’s why I was struck by two moments earlier on the US side: (1) ChatGPT’s demos of direct product search + add-to-cart flows via function calling (which can push intermediaries toward “headless fulfillment”), and (2) Apple’s mini-app / app-extension direction, which—depending on how it evolves—could give an assistant the power to feel like a “super-app layer” for the AI era. *How would this end?* I honestly don’t know. The incentives are existential for stakeholders: ByteDance / Z.ai vs. Huawei / Xiaomi vs. WeChat / Taobao / Meituan. A similar dynamic may play out in the US—starting with commerce and extending to assistants attempting to displace Siri and Alexa. Whether the US ever produces a WeChat-like “superapp” is still unclear—but the fight for the interface is clearly beginning. Curious: In an agentic world, who do you think ultimately controls the consumer interface—OS players, super-apps, or the AI assistant layer? #ConsumerAI #AIAgents #ChinaTech

Literally, Apple quietly announced something much bigger than it looks: the Mini Apps Partner Program. While this X-eet already breaks down many first-order and second-order effects well, here are my quick takes through the lens of Consumer & AI. Still more questions than answers. 1. “AI Super Apps” are now officially legitimized ChatGPT—with its recently announced Apps SDK—is the current frontrunner. But others will follow quickly: Perplexity, Claude, Alexa (if Amazon is truly serious), and more. 2. What happens to the other contenders? Think: AI agent apps and AI-native browsers (Manus, Genspark…) and the new wave of AI browsers (Comet, Dia, Follou… not to mention ChatGPT Atlas). They’re each playing a slightly different game, but ultimately chasing the same prize: a few coveted spots in the AI Super App race. A core value proposition of “AI browsers” has been to route around platform constraints—like the App Store for iPhone. With Apple’s Mini Apps Program, the rationale for products like OpenAI’s Atlas becomes weaker, at least on mobile. Yes, there remain important use cases—“computer-use” workflows, primarily for backward compatibility of existing web/apps, and certain agent behaviors that don’t fit neatly into Mini Apps—but the relative value narrows. 3. A flood of Mini Apps is coming—then things may get messy Expect an early-days-App-Store moment (remember the Fart app?), followed by a crowded, chaotic market. Much will depend on the policies of the “container” apps competing to become the dominant AI Super Apps. China’s mobile ecosystem consolidated into tightly controlled walled gardens (WeChat, Alipay, Meituan, to name a few). Their mini-app ecosystems are curated with firm control over who gets in, how apps behave, and what gets prioritized—very different from the freer markets of the App Store or Google Play. Apple’s (and likely Google’s upcoming soon) Mini Apps framework seems headed toward a more open, App-Store-like approach. Which leads to… 4. A surge in “developers” and “apps”—but not in the way we’d imagine Several forces will make this ecosystem unfold differently from any past platform shift: Vibe-coding tools enabling effortless, social creation (think: Wabi-style social sharing of AI apps). Claude Code and similar systems solving tasks end-to-end with autonomous agents. A massive library of user-generated prompts—from GPTs, Claude Skills, and AI browser skills—suddenly becoming “Mini Apps” themselves. The result: a period of creative chaos before a new equilibrium emerges. Until then, I may have to keep thinking about the second-order effects… and the ones beyond that.

My view on this claim is “Yes and No”, but largely “No”, as below. (My argument below is largely biased towards the Consumer side of the value prop, rather than the enterprise-focused high-efficiency tooling with AI features/functionalities/tech applied.) For something that can be called ‘AI apps’ If we define ‘AI app’ as feature-based functionalities, I agree to this argument since those functionalities will be offered by the FM and other infra layer providers (even including AWS soon). This applies exactly to what we used to call ‘GPT wrappers’ in a dismissive sense. However, if we look further into the dominant apps/services on web & mobile in the past few decades, many (in fact, most) of them have their moat, not in their functionalities, but in other defensible elements. A few key moats that come up to my mind include: social graph (Facebook), behavioral lock-in (through recommendation engine) (Tik Tok, Toutiao), behavioral lock-in (through social graph) (Snapchat), network effect (esp. the supply graph) (Uber, Airbnb), product listing & logistics (or, SPC as Josh repeatedly claims) (most e-commerce). All of these moats live outside of the pertinent domain FMs can handle and functionalities FMs can add. Of course, the FM providers can tackle some of these elements to corner a few key markets (e.g. shopping checkout inside ChatGPT). But, practically speaking, there are way more markets/verticals/segments than any one FM providers can address and dominate in many of them, if not most, (even if OpenAI reach $3T market cap in next 10 years). It is not a matter of having enough resources or not, but a matter of focusing, identifying, and building lasting moats & defensibility, which I believe is why there have been so vast opportunities for so many upstarts since the dawn of Capitalism. For something further than ‘apps’ (”Scenario 2”) Furthermore, if we consider the latest tweet on Elon Musk saying “no app, no OS, all personalized content generated on demand in real-time” which I strongly agree to, it is possible that there would be less and less of something we may call ‘AI apps’. My futuristic projection is that we more likely would live in some ‘ambient environment’ where my needs are taken care of in background (by ‘ambient agents’). In this type of environment, there will not be many distinctive apps, each focusing on many disparate functions (e.g. app for each category of email, calendar, messenger, shopping, hotel booking …), but might be a few “AI Concierge”, fully personalized to me, which can handle most of my needs (explicit or implied/inferred) and requests (explicit). In this setting, there will not be many investment opportunities in “AI apps” segment, not because “FM providers will dwarf emerging AI apps”, but because “there will not be much needs for separate AI apps” other than “AI Concierge”. Instead, there may be a number of ‘headless’ provider of each functions (e.g. hotel booking, grocery delivery). (As demo’ed in GPT-4 for Instacart and ‘Apps SDK’ of OpenAI more recently, the trend is that more and more of these functions would be integrated with something like ChatGPT, meaning there will be less needs for the consumer-facing UX (i.e. ‘headless’), but the needs for those functionalities (i.e. product delivery, airline booking) will remain as invisible functions behind ChatGPT or “AI Concierge”) In this futuristic imaginative setting, some entirely new entity (e.g. “personalization data layer” or something similar, e.g. “personalized social layer”) might prevail as primary (possibly, the only) touch point for consumers, rather than many disparate ‘AI apps” just like what we see on our iPhone screens. (Imagine Siri handling 90% of my personal needs, e.g. shopping, hotel booking) Even in this case, I don’t think OpenAI can dominate in these “layers”. If I have to, I’d rather bet on Apple than OpenAI (largely due to the personal data owned by them), though I believe there more likely will emerge new entities taking care of these layers, than OpenAI dominating these layers too. I am not 100% sure whether/how this “Scenario 2” may unfold down the road, but my thesis is that “Scenario 2” would be more likely than having 1,000’s of disparate ‘AI apps’ (as we have experienced in the Mobile Age). This thought has led me to my thesis that “’the Next Google’ more likely would emerge in the ‘personalization layer’, at least in the Consumer + AI space”.

+1

Founders, for the love of God; send investor updates. 1. Leverage them for help with customer intros, hires and fundraises. 2. Use them to create excitement and conversation about you in market. 3. Because these people have trusted you and given you their money. Not doing them is a disservice to your company.

MCP as the AI App Store Moment: Unleashing the Explosion of Consumer AI Apps and Services 2025 was predicted as the year of AI Agents, yet the market’s acceleration has far outpaced expectations. With platforms like CrewAI claiming 50 million agents and thousands of AI agents already active in various marketplaces, the emergence of Anthropic’s Model Context Protocol (MCP) in late 2024 has proven transformative, quickly solidifying itself as the industry standard—even embraced by rivals like OpenAI and Google. MCP, often described as the “USB-C for AI,” serves as the connective tissue and backbone that seamlessly integrates AI apps and services across diverse ecosystems, igniting explosive growth in both enterprise and consumer AI landscapes. From an early-stage VC perspective, MCP is not simply a technical protocol; it is the foundational catalyst for a multidisciplinary AI app and service revolution, analogous—if not more profound—to the AppStore moment that reshaped mobile computing. This post outlines why MCP is poised to redefine the consumer AI opportunity, highlighting underappreciated dynamics and new avenues for startup innovation. From Fragmented Integrations to a Unified AI Ecosystem Before MCP, AI applications faced the “M x N problem”: integrating M AI models with N disparate tools required costly, bespoke connectors. MCP’s standardization dramatically simplifies this by enabling AI agents to connect to any MCP-compatible server through a universal protocol. Today, marketplaces like Smithery and GitHub MCP Server Hub host thousands of servers spanning critical categories such as data source access (including databases and B2C services), browser and workflow automation, and more. What stands out are the expanding consumer-facing MCP servers that unlock access to major B2C data sources, including Google Maps, Airbnb listings, Spotify playlists, and real-time weather data. These servers underpin the emergence of a new generation of consumer AI apps that can seamlessly tap into live, authorized data, enabling highly personalized, context-rich experiences. For example, MCP servers connected to airline Global Distribution Systems (GDS) or hotel wholesalers could radically disrupt incumbent platforms like Kayak and Hotels.com, possibly disintermediating legacy aggregation models. While chat-based UX (via LLM clients like ChatGPT and Claude) dominates today’s interface for MCP interaction, alternative channels and novel UX modalities will likely be emerging en masse, taking advantage of the availability of vast amount of data sources which has been established over decades since the web transition in 90’s. This emergence of new apps and services with the novel UX en masse would give rise to flood of the new AI-powered apps and services, unleashing the explosion of Consumer AI apps and services. The Consumer AI Renaissance: New UX, New Modalities, New Opportunities The true power of MCP lies in enabling AI agents to transcend conventional chatbots and web apps by supporting diverse agent UX forms—browser automation, ambient agents persistently residing in cloud environments, and possibly even ephemeral, one-off “vibe coding” services that deliver rapid, on-demand functions and then vanish. This evolution echoes the shift from early mobile apps to a complex app ecosystem, yet the scale and impact loom much larger. As startups and innovators, it’s critical to recognize that MCP dramatically lowers barriers for consumer AI app creation. Standardized, two-way communication protocols open access to the vast universe of consumer data and services, democratizing capabilities previously reserved for deep-pocketed incumbents or internal enterprise teams. From a user behavior standpoint, MCP enables AI to proactively interpret and amplify “implied intentions” derived from multiple workflows—such as personal life events detected via messaging apps—to offer serendipitous recommendations (“discovery by surprise”) and ultra-personalized concierge services (”white glove concierge”). Thus, consumer AI shifts from reactive chatbot interactions to anticipatory, contextually aware, and deeply integrated experiences—ushering in demand for novel categories of AI-driven consumer applications. Redefining Market Economics: Towards an Agent-to-Agent Economy A profound consequence of the MCP revolution also lies in the transformation of underlying economic models among stakeholders including users, agents, service providers, data providers, etc. In the emerging Agent + MCP-enabled consumer AI market, value exchange will increasingly occur via autonomous Agent-to-Agent (A2A) transactions rather than traditional human-driven request-and-response UX common in mobile and web apps. AI agents will interact with MCP servers, other agents, and data providers in layered, modular workflows—executing complex tasks and negotiating service usage in real-time. This shifts the revenue model and monetization frameworks from eyeball-based advertising and click-driven SEO (reads Google and Meta) to potentially fee-based service agreements, or reverse auctions among agents offering to sell services, and even other novel B2B economic structure where the MCP servers access the dataset available from traditionally-B2C services in the form of the B2B transaction. Industry incumbents such as Airbnb, Spotify, and e-commerce marketplaces might see a significant portion of current consumer interactions migrate to these A2A formats, which the end users may experience through third-party service providers possibly in the form of Agents, disrupting legacy aggregator moats built on user accounts and payment data. For startups, this represents both a threat and an unprecedented opportunity: by leveraging MCP and A2A dynamics, nimble entrants can bypass entrenched platforms, offering hyper-personalized, AI-driven services that deliver superior user value and capture new revenue streams. Envisioning the Future: Unleashing Consumer AI Innovation at Scale History teaches us that major platform shifts democratize innovation and foster explosion of novel use cases. MCP is not merely standardizing AI connectivity; it is setting the stage for a burst of creativity across countless startups building “AI-first” consumer applications with radically new UX models. Early movers who embrace MCP stand to benefit from: Rapid Productization: Leveraging ready-made MCP servers to connect instantly to rich data and service ecosystems which are already in place, accelerating time-to-market. UX Differentiation: Delivering new interaction paradigms beyond chat, such as ambient AI Agents, automated task orchestrators, and composable AI apps. (only wild imagination can tell) New Business Models: Designing transactional and subscription services around agentic workflows, facilitated by secure, dynamic A2A transactions. Data Network Effects: Participating in open ecosystems where shared MCP-based tools and data drives compound innovation and value creation. The coming years will witness a market evolution reminiscent of the dawn of the smartphone age—but with much broader scope and far greater impact. The Model Context Protocol is the linchpin unlocking this potential, making 2025 not just the year of AI agents but the year when consumer AI enters a new era of explosive growth and opportunity, a la “AppStore moment of AI apps and services” in Consumer AI. For VCs and entrepreneurs looking ahead, MCP marks the dawn of the AI App Store moment—a foundational shift promising to redefine how AI services are built, distributed, and monetized. Embrace this revolution today to lead in tomorrow’s consumer AI economy. Thanks for reading! If you are interested in exploring startup opportunities around MCP and AI apps, I’m eager to connect and share insights on navigating this exciting frontier. More elaborate version of this writing also is available at twocents.xyz/p/two-cents-72…, albeit in Korean language.