Bryan Kim

Consumer AI ended 2025 looking winner-take-most: ChatGPT held 800–900 million weekly active users, while only 9% of consumers paid for more than one of ChatGPT, Gemini, Claude, and Cursor. For most of the year, fewer than 10% of ChatGPT users visited another major provider. Olivia also cited Gemini as having added an estimated 35% of its scale on the web and about 40% on mobile, while Claude, Grok, and Perplexity each sat near 8–10%. Anish Acharya’s brand framing was simple: “ChatGPT is like the Kleenex of AI.”

Gemini was the live threat because viral creative models coincided with accelerating growth: desktop users rose 155% year over year versus ChatGPT’s 23%. Gemini reached roughly half of ChatGPT’s mobile scale on Android but only 17% on iOS—“everywhere” yet still “nowhere” in consumer habit. Justine Moore thinks it could get there if it sustains its image-and-video launches, though ChatGPT’s guided templates make the first creation far easier than Gemini’s blank box.

The year’s consumer model breakthrough was image and video models combining realism, reasoning, retrieval, and multiple media. ChatGPT 4.0 image’s Ghibli moment, Sora 2, Veo 3, and Nano Banana showed that accurate details, search-backed logos, consistent characters, and audio combined with video can create viral demand. The next architecture is “anything in to anything out,” potentially merging text intelligence, images, video, and editing into one model.

The labs’ distribution does not automatically produce successful vertical products, creating the panel’s clearest startup opening for 2026. Pulse, Atlas, group chats, Sora, Stitch, Gems, and Opal have not become breakout standalone consumer interfaces; NotebookLM was the notable exception. Bryan Kim’s caveat is that high-frequency assistants will remain hard to displace wherever the product is primarily text in and text out.

Sora 2 proved demand for AI video creation, not yet for an AI-native social network. A small creator cohort generated content for TikTok, Instagram, X, and Reddit, while in-app consumption, remixing, and commenting did not seem as strong as initially; the better analogy was “CapCut,” not TikTok. Bryan’s bull case is that humor could create a new status game through prompting skill and cultural awareness. Anish asked whether exporting still makes TikTok with Sora videos “strictly better.”

The most defensible near-term market may be prosumer and enterprise workflows, where depth of usage can invert traditional consumer economics. ChatGPT enterprise usage was said to be up roughly 8–9x year over year, while Claude and Comet showed the value of persistent workflows and cross-tool context. Usage charges above subscriptions are already producing consumer AI products with more than 100% revenue retention: “Maybe all of AI is actually a power user story.”

Compute remains the strategic constraint: labs must trade training against inference and entertainment traffic against coding intelligence, while focused application companies avoid that internal conflict. Anish said xAI was “probably the only” model company not bottlenecked on compute, “from my understanding,” while first-party-only labs also leave room for multi-model products serving power users. With model quality now sufficient to “build a real, scalable app,” the closing hope was that 2026 becomes a huge year for consumer builders.

🔗 Original source & video: Where does consumer AI stand at the end of 2025?

The episode opens with Erik Torenberg saying Elon is reaching out and Meta is offering a $1 billion acquisition offer. Roy Lee responds that six months earlier he was a random college kid in a dorm and now feels at the center of tech, while the more astonishing development is how correct his virality assumptions have been.

Cluely’s central bet is that distribution can discover the product faster than traditional product development can discover demand. The team wrote its first code 10 weeks before the episode, launched with a barely functioning product, and inserted sales calls into its videos as a test; Roy says that experiment produced “over a million dollars of enterprise revenue coming in.” With more than 1 billion Cluely views, aggregate usage now points the team toward its stickiest uses and product direction.

The discussion frames Twitter as roughly two years behind Instagram and other platforms in understanding short-form virality, while Roy’s broader claim is that people on X and LinkedIn are behind. Those platforms reward accessible, controversial content, while tech creators optimize for intellectual status and produce material “maybe like 200 people in the world can actually understand.” His supposedly extreme videos are less controversial on Instagram or TikTok, where creators compete with content insinuating public felonies.

Cluely has reorganized marketing around demonstrated algorithmic mastery rather than conventional credentials. Bryan Kim puts Roy in roughly the top 0.1% in the world at distribution. Every full-time employee has over 100,000 followers on some social platform, while more than 60 contractors are paid per video to produce clips—including batches of five 10-second videos—that can generate millions of views. Roy’s provocation: if a head of marketing lacks 100,000 followers, “you need to replace them.”

Bryan backed Cluely after seeing it turn attention into dollars, but his larger thesis required moving beyond his old consumer-investing framework while retaining some belief in its core. He previously favored slowly crafted products with retention and network effects; rapidly changing AI models made that insufficient because an incumbent’s next release can erase a feature. In this phase, founders must enjoy “building the plane as it’s falling down the cliff,” making momentum across product and distribution the current moat.

The product thesis is that AI should inhabit a translucent overlay that sees the screen and hears audio, not remain trapped in a separate window. The UX emerged from 20–30 Interview Coder iterations, when Roy and co-founder Neil needed to view generated code and their own work simultaneously. Roy frames Cluely as “pre-launch”: saturate the market with the category now, then release the fuller product to an audience already primed to associate invisible AI with Cluely.

Roy concedes the overlay is technically simple and likely to be copied, so defensibility currently rests on winning a land grab. He argues Cluely might distribute better than OpenAI and says there is enough of a case to “probably bet on us” at roughly a “30,000× discount,” while controversy makes its marketing anti-fragile: attacks create supporters, opponents, and more attention. His guardrails are narrower than “triple down on everything”—“never punch down,” remain authentic, and let genuine respect remain visible.

🔗 Original source & video: Building Cluely: The Viral AI Startup that raised $15M in 10 Weeks w/ Roy Lee

Consumer tech has not stopped producing breakouts; AI has changed their shape. Olivia Moore identifies ChatGPT as the clearest mass-market winner, alongside Midjourney, ElevenLabs, Black Forest Labs, Kling, and Veo 3 across different modalities. These wins often emerged from model-centric research teams rather than familiar social-product playbooks. The opportunity now shifts toward teams that can turn increasingly accessible models into products around a potentially still-missing layer: human connection.

AI is overturning consumer software’s historically weak monetization. Where $50 a year once looked strong, consumers now “very happily” pay $200 a month, Google’s top consumer SKU reaches $250 a month, and usage credits make revenue retention meaningfully exceed user retention. Deep Research can replace 10 hours of work, while generative video feels like a “magical mystery box”; Anish Acharya’s endpoint is future consumer spending organized around “food, rent, software.”

Consumer virality is becoming enterprise lead generation, not merely an acquisition loop. ElevenLabs moved from memes, voice clones, and game mods into large contracts before reaching every mainstream consumer; companies can inspect payments, discover 40-plus employees at one customer, and open a sales conversation. AI mandates make enterprise buyers unusually willing to turn a viral toy into production infrastructure.

In this phase, shipping velocity may matter more than a static moat. One panelist’s “come to Jesus moment” was that moat-first investments were not necessarily winning; the leaders broke molds, launched models quickly, captured mindshare, converted traffic into revenue, and funded the next iteration. Traditional defensibility can follow through workflow lock-in, proprietary libraries, and segmented quality frontiers.

The first native AI social network remains unsolved because social products require real emotional stakes. Perfectly generated pictures of users looking happy in ideal settings may lack the vulnerability that makes a network matter, while most AI expression still flows through Facebook, Reddit, and Reels. Possibilities include sharing the “essence” users reveal to ChatGPT, creating profiles that contain what a person knows, and using AI to recommend collaborators, friends, or dates.

Voice is moving from a previously unworkable interface category to a foundational AI primitive. Earlier technologies never made voice a workable substrate; generative models now support companions, voice products such as Granola, and enterprise calling, including sensitive financial-services workflows burdened by offshore centers with 300% annual turnover. Erik’s contrarian call is that AI will eventually intermediate the highest-stakes negotiation, sale, or act of persuasion—not merely customer support.

Companions may strengthen human relationships, but excessive agreeability is the unresolved product risk. Eleven of the top 50 apps in the discussion’s cited list were companion products, spanning friends, coaching, nutrition, and AI girlfriends. The sharpest counterexample to dystopian forecasts was a Character.AI user who credited his AI girlfriend with teaching him enough social fluency to find a “3D GF”; the warning is that an agent which never pushes back may train users badly for reciprocal relationships.

The next platform may be an always-on layer across phones, AirPods, screens, and recording devices. Seven billion phones give mobile a huge installed base, but local models, wearable pins, and agents that see and act could deliver continuous coaching and introductions. AirPods are “hiding in plain sight”; adoption will also require new etiquette for recording and AI presence.

🔗 Original source & video: The State of Consumer Tech in the Age of AI