Companion Apps Open Twice as Often as TikTok. a16z Only Funds Weird but Working

Nathan Labenz sat down with Anish Acharya and Justine Moore, partners on the a16z Consumer team, just after they published an investment thesis they call the Abundance Agenda. The word they want you to hear is abundance. Every big platform shift they have lived through — the internet, mobile, and smaller ones like fintech and crypto — produced a burst of new companies and, more to the point, made consumer lives better. Things that used to be luxuries got cheaper, then accessible, then close to commodities.

That is why they are fired up. AI feels, to them, like one of the largest platform shifts of their lifetimes, and so many of the most interesting companies in their portfolio were built around the previous one. Labenz came in already converted. Preparing for the episode, he tried at least ten AI-native apps; the music generator Suno impressed him most, down to an AI-themed reggae track he used as the show's intro. The Abundance Agenda deck itself was built in Gamma, an AI presentation tool that had already been on the podcast. Moore's Twitter feed of product experiments is, Labenz says, a must-follow if you want to keep up.

The first market they are underwriting is creativity and productivity. Acharya's philosophy is that everyone is born creative. Taste then gets more sophisticated while, for most people, the skill atrophies, so you cannot compose the music you want to hear or paint the pictures you want to see — sometimes you cannot even write the email you mean. The new technology changes that. If you have good taste, you can make good art, and that has not been true before. The floor on participation drops. Midjourney and Ideogram, he says, are already seeing enormous pull in both usage and revenue. He does not pretend to know the market size. The pull is the evidence.

Productivity is the inverse. What blocks great work is administrative overhead, plus the gap between tools that help you work and the ability to hand the work off. AI, in their telling, drives that tax toward zero and starts doing the work instead of helping with it, which raises the ceiling. One side lowers the floor on who can create. The other raises the ceiling on what a professional can ship.

The second market is companionship. Everyone knows the need for human connection. Their hot take is that the *human* part may be overstated, and that people just need connection. Public data, Acharya says, shows people using companion apps twice as often as social apps — twice as often as TikTok or Instagram. Something is landing at a depth that usage intensity is hard to explain otherwise. The opportunity they see is to tailor those experiences so people flourish: feel accepted, feel good about themselves, feel that their particular kind of weird is not a solo act.

The third market is wellness and personal growth. Personalized teaching is associated with gains on the order of two standard deviations. Personalized financial advice tends to mean people manage money better. A team looking after physical and mental health improves well-being. The problem is price. Those setups are a great vision for people with resources. AI's job, they argue, is to collapse the cost until what used to be a rich person's stack becomes an everyday good.

How they prosecute those markets is, Acharya says, a little different from how they have done it before. First, they look for the intersection of weird and working. Products that do not feel serious tend to be the ones that win. That is one reason incumbents do not just take consumer, and one reason first-time founders tend to outperform second-time founders in the category: their tolerance for being embarrassed, and for shipping something weird, is much higher. Related, they do not believe they can predict consumers. They can only observe them. They are in the observation business, not in the business of having a vision for what people should want. Culture sits on top of all of it. a16z has always lived at the intersection of culture and technology, and AI is enabling a set of creative fields that you cannot talk about as pure tech.

Moore picks up on the product side. Beyond chat, the first killer consumer use case was content generation: type a prompt, get an image, a song, a video. Early energy went into foundation models. Now there are enough publicly accessible models — open-source and closed APIs — that the opportunity is shifting to workflow. Everyday people should not have to be prompt engineers or bounce through a pile of editors. Generation, revision, and iteration should feel like sitting next to a brainstorming partner who is also an editor. Midjourney variations and Pika's canvas edits are early versions of that: not a one-off artifact you download, but something you can change a little.

She is equally interested in bringing the open-source frontier to a browser. Around the world, people are fine-tuning base models into LoRAs and checkpoints, then chaining image models in ComfyUI into whole videos and 3D textures. Historically that has belonged to whoever has a 4090 at home. The next consumer wave, she hopes, puts that cutting edge into apps anyone can open. On the less artsy side of productivity, the pitch is an assistant, chief of staff, or data analyst for every role — research, numbers, a pros-and-cons list, a stack of emails — and, later, products that listen through software or hardware and interject instead of waiting for a prompt.

Labenz asks where they actually find new products. Acharya's answer is Twitter and Reddit, plus using the things, even when compliance hates it. It is hard to be authentic to the space, or to have real intuition, if you are not in the products. So they put ComfyUI, Automatic1111, and ControlNet on their own laptops and try to understand what is happening at a product-instinct level, not just an investor-metrics level. Moore adds Discord. The joke on the team is that an anonymous Twitter account or Discord handle might be the CEO of a giant company or a 16-year-old in the Philippines who found an app and became an expert. New platforms let far more people show up as experts.

The Twitter algorithm is good at surfacing what gets engagement. Reddit and Discord are where people tinker without trying to drive eyeballs. Moore's three favorite subreddits at the time were LocalLLaMA, r/aivideo, and r/ComfyUI — curated groups pushing language models, video, and image generation. ChatGPT and Stable Diffusion subreddits are fine if you just want to see what is popping. There is a running joke that AGI will be posted to LocalLLaMA by someone running a 4090 in a basement. It does not seem that far from the truth to her. Sam Altman has even teased developments on Reddit, possibly shitposting, possibly not.

Labenz's own company, Waymark, writes TV commercials for small businesses. On a B2B basis with TV platforms it works. Direct to an SMB, they hit a pattern he thinks is representative: inference is expensive, so they need a $19 or $29 monthly conversion, and a significant share of people pay, solve the problem, and cancel. Nothing was wrong, they say. They just do not know when they will need it again. Acharya answers in three layers. He spent five years at Credit Karma, which he puts at about 120 million U.S. members; a lot of that growth was television, a format digital startups treat as dated and which still performs. Inference cost, second, is falling every day. Third, their partner Alex Rampell talks about high-NPS churn versus low-NPS churn. Low-NPS is I hated this and I am never coming back. High-NPS is I am an episodic user. Model that somewhere between recurring SaaS and a one-time transaction. Content generation will be episodic, and that is okay. You can still build a great business. You just should not treat that churn like the other kind. Labenz says this is the first time he has heard an investor say they would give credit to something other than strict recurring revenue. In the past people told him one-time purchases count for zero.

His counter-idea is an AI bundle: a hundred dollars a month, cable-style, a thousand channels, most of them never watched, money split among the apps. Moore thinks some version will happen eventually. It has not taken off because the category is still fragmenting. Image generators already specialize — anime, photorealism, text — so it is hard to pack what everybody wants. Acharya is more radical. A bundle might be a bridge. The long-term trajectory, if you are a business owner, is that you do not want to think about marketing. You want customers to show up at a profitable acquisition cost. An AI CMO, or an AI-powered back office, would choose which products to buy, when to subscribe, when to churn, and how to use them, while you work on the craft. Fermat, a site-generation company, is the example: it generates *n* variants of a website, runs the multivariate tests, and serves each visitor the winner. Labenz's summary: the threat to the AI bundle is the AI procurement department.

He also cannot find a good image-to-video app. A photo of his kid making a snow angel still melts the face — Will Smith eating spaghetti territory. Runway, Pika, and Genmo, Moore says, are each good at different styles. Runway plus motion brush is the one to try on a photorealistic kid, pinning the face while the arms move. Pika is stronger on stylized cartoon and anime. Open-source video has moved fast: ModelScope, then ZeroScope, then Hotshot, with consistency getting better. Why so few APIs? Video is expensive and slow, and the quality is not yet commercial. Teams at the frontier are still racing the best model; once they have it, and once consumer numbers confirm it, APIs will follow. If they talk again in three months, six months, or by year-end, she expects a different conversation.

Labenz wonders why they overweight weird. Email is not weird. He has been impressed by Shortwave's RAG, and yet there is still no killer email assistant. Acharya's filter is simple: if it is a feature you can get promoted for building at Google, it will get built at Google. AI email composition is sustaining innovation for an incumbent. a16z is an investor in Superhuman; he likes the category, he is just less focused on what incumbents will do. Some of the 'not weird' jobs will still get redone in a weird form. Voice as an interface never worked, and now it does. A voice-first inbox — reviewing mail with an EA instead of walking linearly through blocks of text — is a stretch for the Gmail team and a live idea for Superhuman, Shortwave, or a company starting today.

On the macro split, Elad Gil has told the same show he expects 80% of the value to existing platforms and 20% to startups, adding that 20% of this shift is still huge. Acharya almost reverses it: 80-20 to startups. Incumbents will get more efficient and more profitable, products will get cheaper, more people will have access — that is abundance inside the old companies. The history of platform shifts, though, is that new markets are way bigger than anyone predicts, and they favor the platform-native startup. If they have this conversation in two years and it is still Elad's 80-20, he will be surprised. He will also be disappointed.

Labenz's canonical objection is Salesforce: they will add an AI layer before anyone displaces them with an AI-first CRM. Acharya's challenge is whether you even need the category. BI, he argues, is an interface between a CEO question and a database. An LLM can generate the query, return dashboards, and decide which charts belong in the inbox every morning. Software ate the world; now AI eats software. Labenz hears an implicit bet that models are nowhere near topping out — even GPT-4 still fails messy text-to-SQL benchmarks. Moore splits the difference. They believe models will keep getting better, and they believe the workflow around the model is the job. The first wave was prompt in, artifact out. The next wave is the accuracy layer, the double-check, RAG, the product you reach for when the answer is wrong. Fine-tuning, DreamBooth, and LoRAs also let you make artistically opinionated specialists without training a new foundation model. GPT-4V, Acharya adds, is still under-explored as a product primitive. If the models freeze, Labenz says, there is a decade of plugging GPT-4 in everywhere. If they leap, there are a few years of ripping things out and plugging new models into the holes.

Labenz has been waiting for someone to run the Uber strategy on AI doctors: ignore what you are told you can do, go straight to the public, be so good that users love you and regulators look like the villains. Lawyers, too. A link to drgupta.ai sits in the chat with that posture. Why has nobody really done it? Acharya does not want to overweight an area they do not live in daily, but a16z did invest in Hippocratic, which is building a foundation model for doctors-and-nurses-as-a-service. Even Uber cared whether the car exploded. You still need the specialized data. And everything is early. ChatGPT and the current Midjourney iterations are, at best, 14 months old. If they talk in 12 months and nothing like that is at scale, he will be surprised. Moore adds the cold water: healthcare and finance are categories where an individual can be fined, arrested, or stripped of a license. Systems and regulation take longer. There is a reason Uber was ride-share, not a hospital.

Labenz's own rule is both, for now. On a serious medical issue he would talk to a human doctor and to an AI, and would feel he was missing out with only one. He is more bullish on the nearer mental-health track. Over fifty years the stigma around therapy collapsed, demand went from a few people to a lot of people, therapists are short, and prices are high. Acharya has been using Rosebud, an AI journal that talks back; it feels weird to say, he admits, but chatting with a box produces real feelings of well-being. Labenz is about to have Eugenia Kuyda of Replika on for a second time. A Replika paper from late 2021 — before ChatGPT — reported that use of the app reduced suicidal ideation for about 3% of the users they studied. The line of hers he has never forgotten: they could not build a bot that could talk, but they could make one that could listen.

What worries him is the next wave. The App Store is crowding, blitzscaling plus pay-per-click can produce parasitic products, and that is the thing that brings regulation. Moore's team has spent a lot of time in companionship, including apps banned from stores that then moved to web, Telegram, or Discord. Consumers, she says, vote with time and money. Clicky, scammy, low-quality products have not shown durable acquisition, engagement, or retention. Take the judgment lens off. The questions are: is a segment really engaged, do they retain, do they tell friends because it added value? It is usually obvious in a download when the bot is not listening and is only trying to upsell Pro or ads. Acharya's proxy metric is the share of people who leave feeling good. Instagram is a global popularity contest most people lose. TikTok lets more people participate in the trend. Companion apps, he bets, send an even higher share of users away feeling fulfilled. Regulation may come. He does not think the trigger will be users who want an abusive relationship with a companion.

Labenz pushes one layer further. He has been red-teaming AI calling agents in public. Spoiler: they will do almost anything — a ransom call claiming you have someone's kid, a cloned Trump voice walking back an immigration position. Adults spending their own time is one thing. Agents that go out into society and talk to people who may not know they are talking to an AI is another. Acharya's three-part answer: builders' intentions in the space are, as a group, extremely positive; portfolio companies and others are already working on labeling AI content; and media literacy is underestimated. Our parents cannot spot spam email. We can. The next generation will grow an equivalent intuition for AI. Labenz's own fear, as a founder, is GDPR-for-AI before the benefits compound. A little self-governance, he thinks, postpones that day. Asked for a request for startups, they do not want to presuppose what works. Come one, come all. Nothing is too weird. If it feels like a *Black Mirror* episode, they are probably interested.