AI Consumer Products
Consumer-facing AI apps, personal assistants, social products, productivity tools, entertainment, companionship, and mainstream adoption.
Figma’s CEO Says AI Makes Average Work Easier to Ignore
Figma co-founder and chief executive Dylan Field argues in a Hard Fork interview that AI is not killing design so much as making average work cheaper and more abundant. Field’s case is that writers, designers and software makers will be judged less on their ability to produce a first draft or prototype than on whether they can give it a distinctive voice, point of view and level of craft. He expects design work to broaden rather than disappear, even as AI labs push further into application software.
Snap’s Specs Face a Public-Market Test After Years of AR Spending
On Diet TBPN, John Coogan and Jordi Hays used Snap’s new Specs as the clearest case for a broader skepticism: technically strong demos do not answer whether a company can create demand, an ecosystem, or a rational return on capital. They argued that Snap’s AR work might look fundable as a startup but is harder to defend inside a public company whose stock has fallen sharply and whose core ads business could be run more profitably. The same standard shaped their read on Taste Labs, AI export-control fights, and SpaceX’s valuation: the hard question is whether impressive capability can be converted into durable business control.
Camera AirPods Would Give Siri Visual Context in Apple’s 2027 Push
Bloomberg’s Mark Gurman says Apple is preparing a dense 2026 and 2027 hardware cycle that includes its first foldable iPhone, a second-generation foldable, a 20th-anniversary iPhone and camera-equipped AirPods. Gurman argues the AirPods cameras are meant not for photography or facial recognition but to give Siri visual context about a user’s surroundings, while Snap’s new Specs show the same broader push toward ambient, augmented computing despite high prices and limited near-term adoption.
Apple’s Revamped Siri May Be Good Enough to Ease Its AI Crisis
Bloomberg’s Mark Gurman argues that Apple’s revamped Siri is not a leap ahead of ChatGPT, Gemini or Claude, but may be good enough to stabilize Apple’s position in AI. Speaking with Ed Ludlow, Gurman said the new Siri finally delivers on much of the assistant promise Apple made years ago, while still falling short on advanced tasks such as deep research, long-document summaries and creating spreadsheets or slide decks. His case is that Apple can ease its AI crisis if Siri now handles the everyday questions and device-assistant tasks most of its 2bn-plus users actually need.
GRU Space’s Moon Hotel Depends on Turning Lunar Dirt Into Infrastructure
Skyler Chan of GRU Space argues that the company’s proposed lunar hotel is less a tourism stunt than a test case for building infrastructure from the moon itself. In an interview with Jason Calacanis and Lon Harris, Chan said GRU’s core bet is that concentrated sunlight can melt lunar regolith into durable building material, reducing the need to haul construction supplies from Earth; the episode also used a contested rumor about Anthropic to examine how closely frontier AI labs are becoming tied to U.S. national-security institutions.
AI Market Power Is Moving Beyond the Frontier Model
Alex Kantrowitz and Ranjan Roy argue that the AI market is shifting away from standalone model capability and toward control of infrastructure, access and workflow layers. Their discussion frames SpaceX’s IPO as a public-market AI-cloud story that complicates OpenAI’s ambitions, Anthropic’s Fable rollout as a case where safety policy also looks like market power, and OpenAI’s possible price cuts as a test of whether frontier models can remain premium products. Apple’s Siri, in their telling, matters for the same reason: usefulness may come less from the best model than from where the model sits.
Familiar Pain Is Often Mistaken for Relationship Chemistry
Relationship coach and writer Quinlan Walther argues that partner choice is less a measure of inherent worth than a test of self-trust. In her account, people often repeat familiar emotional patterns — mistaking anxiety for chemistry, empathy for obligation, or a wound for a partner — because those patterns feel safer than unfamiliar forms of love. Breaking the cycle, she says, requires knowing what one wants, tolerating the feelings that follow, setting boundaries, and choosing from values rather than fear.
ElevenMusic Turns Music Discovery Into AI Remixing and Prompted Creation
ElevenLabs presents ElevenMusic as a music platform that begins with discovery and turns listening into creation. The onboarding shows users moving between Explore, where they can browse and remix tracks from more than 4,000 independent and emerging artists, and Studio, where they can upload material or generate new tracks from prompts. Its central argument is practical: the main user skill is not production technique but writing a specific musical brief that gives the model enough genre, mood, instrumentation, vocal, and energy cues to produce a closer result.
Apple’s New Siri Tests Who Controls the Default AI Assistant
John Coogan and Jordi Hays read Apple’s WWDC as a test of whether the company can turn its long-delayed Siri promise into a defensible AI interface without giving up control of defaults, privacy, and the iPhone camera. The Diet TBPN segment argues that Apple’s AI story is less about a single keynote than about older bets now becoming technically possible, while Anthropic’s Claude Fable release and Meta’s data-center training push show the same shift toward long-running inference and physical AI infrastructure.
Tiimo Wants Siri to Make Adaptive Planning Less Manual
Tiimo co-founders Melissa Azari and Helene Nørlem told Bloomberg Technology that Apple’s AI and accessibility work could help make adaptive planning support less manual and easier to reach across devices. Their argument is not that a more capable Siri should replace Tiimo, Apple’s 2025 iPhone App of the Year, but that system-level intelligence could reduce the cognitive load of planning for users with neurodivergent or otherwise less visible needs.
Apple’s Siri Overhaul Tests Its Cross-Device AI Strategy
Carolina Milanesi, president and principal analyst at Creative Strategies, argues that Apple’s next Siri overhaul should be judged less as a ChatGPT rival than as a test of whether Apple can make AI useful across the devices its customers already own. In a Bloomberg Tech discussion with Ed Ludlow, she said Apple’s advantage is embedded, cross-device intelligence, but that pressure is rising as consumers form daily habits with assistants such as ChatGPT and Claude.
Apple’s Siri Overhaul Tests Whether AI Can Become an Operating-System Layer
Bloomberg’s WWDC preview frames Apple’s AI challenge as a test of integration rather than invention. Mark Gurman reports that Apple is expected to use the conference to make Siri more capable across apps, screens, personal data and web search, moving it from a weak voice assistant toward an operating-system layer; Carolina Milanesi and Paul Hudson argue that its value will depend on whether that layer is consistent, private and useful across Apple devices.
Apple’s AI Advantage Is the Operating System, Not the Model
Alex Kantrowitz and Ranjan Roy argue that Apple’s reported WWDC AI plan is strategically plausible because it puts AI at the operating-system layer, where Apple still has unmatched distribution, but they remain skeptical that the company can execute after years of weak Siri and Apple Intelligence rollouts. The discussion extends that same question of control to Anthropic, whose safety warnings sit uneasily beside its push toward scale, and to Microsoft and OpenAI, whose partnership is turning into competition as each moves toward the other’s territory.
AI Agents Threaten Google’s Control of Search, Chrome, and Gmail
M.G. Siegler, author of Spyglass.org, argues on Big Technology that Google’s AI risk is shifting from model performance to control of the next software interface. In a conversation with Alex Kantrowitz, he says Anthropic and OpenAI are moving faster in coding agents and computer-use workflows that could make search, browsers, Gmail and other web products less central to users’ daily work. The discussion extends that frame to Apple’s WWDC, Meta’s subscription sprawl and Anthropic’s confidential IPO filing, but the core claim is that the AI race is increasingly about who operates the computer on the user’s behalf.
Cognitive Surrender Is the Core Risk for AI Product Teams
Tony Fadell, the iPod creator, iPhone co-creator and Nest founder, argues that AI raises the value of product judgment rather than replacing it. In a conversation with Lenny Rachitsky, Fadell says builders should use AI to prototype and accelerate bounded work, but not “cognitively surrender” decisions about architecture, taste, marketing, ethics or what is worth building. His broader case is that great products still come from opinionated judgment applied to real pain, new technology and the full customer journey, not from tools that merely make shipping easier.
Anthropic Frames IPO Path as Capital Access for Frontier AI
Anthropic president and co-founder Daniela Amodei told Bloomberg’s Shirin Ghaffary that the company’s push toward public markets, compute deals and government work should be understood as the operating reality of frontier AI, not as a race for symbolic leadership. She argued that Anthropic needs access to large amounts of capital because model training and inference are expensive, but said the company is trying to scale cautiously: buying compute it can use, widening access to powerful models only after defenders get a head start, and maintaining red lines in national-security work.
Public-Market Capital Is Becoming an AI Infrastructure Advantage
TBPN’s John Coogan and Jordi Hays use Alphabet’s reported $80bn equity raise, Berkshire Hathaway’s investment and a run of founder interviews to argue that AI is pushing capital markets and operating infrastructure back to the center of technology strategy. Their case is that the advantage is moving to companies that can finance enormous compute buildouts, unify fragmented data, own service businesses where AI can be deployed, and build the physical systems — from data centers to space logistics — that make AI useful.
Only 18% of AI Coding Spend Is Shipping Into Products
Alex Kantrowitz and Ranjan Roy argue that the warning signs around the AI boom are less about a single spending scare than about a widening gap between AI usage and demonstrable value. Kantrowitz focuses on enterprise token spending that is not translating into shipped products, while Roy warns that “token maxing,” circular cloud financing and private-market valuation anchors are turning a promising technology into a reflexive capital cycle. Their discussion extends that concern from Anthropic’s surge past OpenAI to Robinhood’s AI trading plans and new data-for-services bargains, all pointing to the same test: whether AI adoption can become disciplined before the financial structure around it outruns the returns.
OpenAI CFO Says Compute Scarcity Will Define Its Next Phase
OpenAI CFO Sarah Friar used an All-In interview to frame the company less as an IPO candidate chasing public-market timing than as an infrastructure-scale AI business trying to finance scarce compute, broaden distribution, and defend the intelligence layer between users and the underlying technology. Friar argued that OpenAI’s consumer and enterprise products are meant to compound off the same foundation, even as the company raises unprecedented capital, diversifies cloud and chip supply, and considers ads without letting sponsored results distort ChatGPT.
Screen Fatigue Is Driving New Markets for Physical Consumer Products
Sam Parr and Shaan Puri use a My First Million episode to test seven unconventional business ideas against a narrower question: whether each points to real demand or just novelty. Their strongest cases are for anti-phone hardware, social wellness formats, physical screen-free media and VR trade training, where they argue odd-looking products attach to existing pressures such as phone addiction, screen fatigue and labor shortages. They are more skeptical of ideas that rely on unverifiable claims or inflated mission language, including AI pet translation and clinical-trial prediction markets.
The AI Era Tests Which Human Frictions Are Worth Keeping
Tim Ferriss, Nirav Savjani, George Mack and Chris Williamson use a wide-ranging “Rabbit Hole” conversation to argue that the AI era’s central problem is not raw intelligence but judgment about what to retain, remove and resist. Across memory, ambient AI, future interfaces, neuromodulation, religion and consumer convenience, they return to the same claim: systems and societies that eliminate friction can also weaken attention, meaning and value. The discussion treats forgetting, restraint and selective resistance as human advantages that technology will have to learn rather than merely overcome.
AI Moves Medical Alerts From Fall Response to Fall Prevention
LogicMark chief executive Chia-Lin Simmons argues that medical-alert technology for older adults has remained too reactive, built around emergency buttons that assume a user can call for help after a fall. In an interview with Craig Smith, she describes LogicMark’s shift toward AI-supported monitoring that builds individual baselines from activity, sleep, medication and location patterns, then flags signs of decline before a crisis. Simmons says the aim is not to replace human responders, but to give families, caregivers and monitoring services earlier signals that can help more seniors age at home safely.
AI Replicas of Ex-Partners Turn Breakup Archives Into Training Data
Chris Williamson, Matt McCusker, Andrew Huberman and Tom Segura examine a use of AI built from intimate archives: people feeding old texts, photos and potentially recordings into chatbots that imitate ex-partners. Williamson frames the practice as a way users present as coping after a breakup, but the speakers largely argue it risks preserving the emotional pattern a breakup is meant to end, while raising unresolved questions about consent, ownership and the repurposing of private relationship data.
Loblaw Says AI Now Generates 46.9% of Its Code
Lauren Steinberg, Loblaw’s chief digital officer, argues that OpenAI tools are already changing both employee work and customer-facing retail flows at Canada’s largest retailer. She says ChatGPT Enterprise is available to every Loblaw colleague, Codex is contributing to internal code-generation and pull-request-linked productivity gains, and ChatGPT-powered PC Express can move a shopper from a dinner question to a local, priced basket. The case is supported by Loblaw’s own on-screen examples and internal data, rather than an independent audit.
AI Photo Analysis Is Moving From Skin Care to Cosmetic Advice
George Mack, Nirav Savjani, Tim Ferriss and Chris Williamson argue that image-capable AI is moving from practical skin-care triage into cosmetic judgment. Mack says Gemini identified a fungal skin treatment that years of doctors and lifestyle changes had missed; Savjani says the same photo-upload pattern is now driving looksmaxing tools that recommend facial changes, procedures and appearance edits. The discussion turns on a boundary the speakers see becoming harder to police: when AI advises what to do to a face, it can also normalize a version of that face that no longer matches reality.
MTV’s Cable Moat Collapsed When Everyone Became a Broadcaster
Tom Freston, the former MTV Networks chief executive, tells Sam Parr that MTV’s rise came from pairing scarce cable distribution with a company built to read youth culture faster than the broadcast incumbents. In his account, MTV and Nickelodeon succeeded by defining audiences narrowly, hiring culturally immersed outsiders, taking fast creative risks, and turning attention into subscriber fees, advertising, and intellectual property. The same model came under pressure when social media made distribution abundant and weakened the gatekeeping advantage that had made cable channels powerful.
Home Humanoid Robots Still Face Cost, Trust, and Dexterity Hurdles
Financial Times technology reporter Cristina Criddle examines whether humanoid robots are close to becoming consumer products, as companies including 1X, Tesla and Figure move from stage demonstrations to home-use pitches. The case is that rapid gains in mobility and AI have made household robots more plausible, but Criddle’s reporting also stresses the unresolved barriers: high prices, limited dexterity, uneven performance and doubts over whether a human-shaped machine is the most practical way to automate chores.
Snowflake Rally Reflects AI Demand More Than Amazon Deal
Bloomberg Technology framed Snowflake’s 34% stock surge less as a reaction to its $6 billion Amazon Web Services deal than as a repricing of its AI software position. Snowflake chief executive Sridhar Ramaswamy pointed to stronger product revenue, higher retention and adoption of tools such as Cortex, while Bloomberg’s Brody Ford argued the AWS agreement mainly helps answer how Snowflake can manage the infrastructure costs of building AI features.
Apple Plans to Make Siri a System-Wide AI Interface
Bloomberg’s Mark Gurman says Apple is preparing a broad Siri overhaul for iOS 27 that would turn the assistant into a system-wide AI interface rather than a voice tool. The changes, expected to be announced at Apple’s June 8 Worldwide Developers Conference, include a standalone chatbot-style Siri app and a “Search or Ask” interface for typing requests, searching the device and web, and invoking AI tools across the iPhone. Gurman argues Apple’s advantage is distribution across more than two billion devices, even as Siri trails ChatGPT and Gemini in AI credibility.
YC Says Internal Agents Need Shared Context, Tools, and Trust
YC’s Pete Koomen argues that building “superintelligence” inside a company requires more than adding AI features to existing software: agents need access to the organization’s shared context, tools and accumulated work. In a Lightcone discussion with Garry Tan, Jared Friedman, Diana Hu and Harj Taggar, Koomen describes how YC’s internal agent system became useful once it could query a unified company database, reuse hundreds of internal tools and turn repeated judgment into improving skills. The broader claim is that AI-native organizations will depend as much on trust, transparency and broad access as on model capability.
Synthetic Intimacy, Surveillance, and Stimulation Are Raising the Cost of Impulse
Chris Williamson’s inaugural Mostly Wise conversation with Andrew Huberman, Matt McCusker and Tom Segura uses health advice, comedy, AI replicas and conspiracy talk to examine where useful tools become distortions. Huberman repeatedly argues for moderation and mechanism over slogans — from low-dose tadalafil and sleep protocols to cannabis, sunscreen and self-control — while Segura and McCusker test those claims against comedy, parenting and lived experience. The broader case is that modern life increasingly requires judgment about thresholds: when optimization becomes rumination, evidence becomes pattern-seeking, and synthetic intimacy or surveillance starts to reshape ordinary behavior.
AI Demand Broadens Beyond Hyperscalers Into Software, Devices and Space
Ivan Feinseth, chief investment officer at Tigress Financial, argued on Bloomberg Technology that the AI investment case is already broader than the hyperscale capex cycle and the next wave of AI IPOs. He pointed to Microsoft’s Azure and Copilot revenue, Adobe’s underrecognized AI content tools, Garmin’s health-and-wellness devices and SpaceX’s long-duration space story, while cautioning that AI-native IPOs may draw strong initial demand but will still have to prove themselves as public companies.
Major Chatbots Fail Forum AI Tests on Election News Accuracy
Forum AI CEO Campbell Brown told Bloomberg Technology that major chatbots are failing basic tests on news, elections, and geopolitics because model companies have not prioritized measuring those tasks. Citing Forum AI’s NewsBench study of more than 3,100 prompts across ChatGPT, Gemini, Claude, and Grok, Brown said the systems showed high rates of factual error, ideological bias, and weak sourcing, including reliance on state-run media. Her proposed fix is independent evaluation, rather than AI companies “grading their own homework.”
AI’s Value Is Shifting From Model Demos to Distribution and Measurement
Google’s problem at I/O, Jordi Hays argued, was no longer proving that its AI models are impressive, but making Gemini useful rather than redundant across products investors now increasingly view as part of a full-stack AI business. The TBPN discussion extended that framing across the rest of the show: AI’s value, the hosts and guests argued, depends less on model spectacle than on distribution, workflow integration, economics and adoption by institutions. That distinction ran from Google’s risk of crowding users with Gemini entry points to SendCutSend’s physical capacity constraints, Commure’s push to automate healthcare administration, and METR’s effort to turn frontier-model risk into something auditable.
Spotify Uses Semantic IDs to Make LLMs Recommend Catalog Items
Spotify’s Shivam Verma argues that LLM-era personalization requires translating both users and catalog items into forms a model can process alongside language. In his account, Spotify combines long-term user embeddings, Semantic IDs that turn tracks and episodes into token sequences, and soft tokens that project a listener’s profile into an LLM’s embedding space. The aim is a generative recommender that can produce catalog-native recommendations without full fine-tuning, while still relying on traditional ranking layers for production use.
AI Backlash Reaches Commencement as Graduates Face a Reshaped Job Market
Jason Calacanis and Alex Wilhelm argue that the boos greeting pro-AI commencement speeches are a visible sign of AI’s legitimacy problem with new graduates entering the workforce. On This Week in Startups, they frame the reaction less as technophobia than as distrust: students have already seen AI weaken academic norms, threaten entry-level work, concentrate wealth around frontier labs, and expand systems of surveillance and data capture. Their discussion returns to a central question: whether workers, founders, consumers, and citizens have any meaningful control over the AI systems now reshaping their choices.
AI Growth Is Running Into Power, Memory, and Inference Bottlenecks
TBPN’s discussion recast the AI boom around physical and economic bottlenecks — power, cooling, chip scarcity, inference cost and memory — rather than model ambition alone. Mike Isaac, Rowan Trollope and Dean Leitersdorf described an industry where local utilities, low-level inference optimization and fast state management are becoming central constraints, a capacity problem the hosts also saw in the whey protein shortage. Everlane’s reported sale to Shein pointed to a different limit: Hays argued that venture-backed ethical basics struggled against price pressure, brand preference and the demand for sustained growth. Joanna Stern supplied the adoption constraint, arguing from her reporting that AI’s progress will be judged through trust, job anxiety, children’s safety and whether new devices ease or deepen phone dependence.
Apple Plans Siri Chatbot With Auto-Delete and Shorter Memory
Bloomberg’s Mark Gurman says Apple is preparing to make privacy the defining claim of its next Siri update, expected to be announced at WWDC, rather than competing only on chatbot capability. Gurman reports that the revamped assistant will let users automatically delete conversations after set periods and will retain less memory than many rivals, a trade-off Apple is likely to present as consistent with its long-running privacy pitch.
AI Can Support Human Connection, but It Cannot Replace Reciprocity
AI companionship has moved from fringe behavior into ordinary emotional life, touching romance, parenting, work and grief, sextech expert Bryony Cole argues. Her concern is not that AI intimacy must be rejected, but that people should decide deliberately whether these systems help build human connection or begin to replace the friction, reciprocity and presence that relationships require.
AI’s Demo Phase Is Giving Way to Infrastructure and Compliance Fights
On Diet TBPN, John Coogan and Jordi Hays framed the day’s AI news around the point where software claims meet physical, financial and political constraints. Coogan argued that the Sanders-AOC data center proposal is less a simple moratorium fight than a question of definitions, grid costs and who pays for externalities, while Hays said local objections cannot simply be dismissed. Across segments on ChatGPT personal finance, circular revenue, office prompting, Tesla’s lead and a possible SpaceX IPO, the show treated AI’s next phase as an institutional test rather than a demo problem.
Economic Entanglement, Not Decoupling, Defines the New China Bargain
Salesforce CEO Marc Benioff joined the All-In hosts for a discussion that framed U.S.-China relations, enterprise AI, and the software selloff around the same question: when dependence is a stabilizer and when it becomes leverage. Benioff argued that more trade with China can lower conflict risk and that large software platforms remain valuable because AI still needs trusted customer data, cash-flowing distribution, and enterprise deployment. David Friedberg, Chamath Palihapitiya, and Jason Calacanis extended the argument across Taiwan, chips, AI assistants, El Niño-driven food risk, and private-market SPVs, where interconnection can either absorb shocks or transmit them.
AI Companions Are Tempting Because They Make Relationships Too Easy
Joanna Stern, author of I Am Not a Robot, argues on Big Technology Podcast that AI’s most plausible near-term role is not as a standalone gadget or replacement professional, but as a second layer on devices, workflows, and relationships people already use. Drawing on a year of trying to put AI into daily life, she says the tools can be genuinely useful in wearables, medical interpretation, and solo work, while chatbot companionship exposes a more troubling risk: systems that are always available, agreeable, and easier than human relationships.
Suno Bets That Making Songs Can Become a Mass Consumer Medium
Suno founder and CEO Mikey Shulman argues that AI music should not be understood as a cheaper substitute for streaming catalogs, but as a new form of active consumer entertainment. In a conversation with Sequoia’s Sonya Huang, he says Suno’s technical choices — modeling raw sound, prioritizing full songs, and using preference data rather than conventional benchmarks — support a product thesis that making music can be as much the point as listening to it. Shulman also frames partnerships with labels such as Warner as central to building new participatory music formats, not as a concession to incumbents.
AI Companies Are Running Into Infrastructure, Distribution, and Trust Bottlenecks
TBPN’s discussion argued that AI’s value is now being tested less in model demos than in the bottlenecks around deployment: inference speed, power, workflow integration and access to customers. Cerebras was framed as a public-market bet on faster inference, while Giga Energy’s data-center business showed how scarce powered shells have become part of the AI supply chain. The same bottleneck logic appeared outside core AI, from Audemars Piguet using Swatch as an official low-cost entry point to Augustus, with conditional OCC approval, trying to rebuild dollar clearing as a national bank.
Travel AI Needs Visual Agents, Not Chatbot Booking Flows
Airbnb chief executive Brian Chesky argues that today’s AI chatbots are the wrong interface for travel and e-commerce, even as AI becomes central to how Airbnb operates. In a live TBPN conversation, Chesky said consumer AI’s next wave will depend on richer, more visual and collaborative agentic products, not text-first chat boxes or another round of enterprise software. He also tied Airbnb’s recent growth reacceleration to more hands-on “founder mode” management, saying AI makes operating intensity more important rather than less.
Prediction-Market Scandals Spur Calls for Insider-Trading Rules
Hard Fork’s Kevin Roose and Casey Newton argue that prediction markets have entered a more dangerous phase, with recent scandals showing how liquid event-betting platforms can reward insider knowledge, manipulation and even national-security breaches before regulators have caught up. The episode broadens that concern into a larger question about technologies whose incentives are outrunning public rules, through Joanna Stern’s year-long test of AI in daily life and Rachel Cohn’s reporting from a Brooklyn school trying to resist the commodification of attention.
Apple Turns to Outside AI Models as Siri Falls Behind
Bloomberg’s Mark Gurman says Apple’s reported plan to let users choose outside AI models is a platform move driven partly by weakness in its own technology. Apple aims to make Siri and Apple Intelligence good enough as defaults while allowing services such as ChatGPT, Gemini and Claude to power some features on the iPhone, he argues. Gurman says that could help users in the short term, but it does not remove Apple’s need to build stronger AI of its own for future hardware.
Airbnb Is Rebuilding Around Identity, Not Homes, for AI
Airbnb’s challenge in the AI era is less a feature rollout than a company reinvention, chief executive Brian Chesky argues in a conversation with Patrick O’Shaughnessy. Chesky says the company has to move beyond a business still identified mainly with homes, rebuild around identity and personal preferences, and do so without damaging a large public platform that hosts and investors depend on. His answer is a more hands-on operating model: fewer abstraction layers, smaller elite teams closer to users, continuous recruiting, and a CEO directly engaged with the work.