For the past two years, most of the venture conversation around AI has been about the enterprise. AI agents for sales. AI copilots for developers. AI tools for customer support. AI infrastructure. AI security. AI replacing, augmenting or re-bundling every workflow inside the company.
That makes sense. Enterprise budgets are large, ROI is easier to explain, and the buyer has a spreadsheet.
But while the market has been obsessing over enterprise AI, something equally important has been happening on the consumer side: AI has quietly become a daily habit.
Hundreds of millions of consumers are already using AI assistants, image generators, AI video tools, AI music apps, coding tools, study companions, shopping assistants and character bots. According to Menlo Ventures, more than 60% of U.S. adults used AI in the first half of 2025, while consumer AI spend was still only around $12 billion, with just a small percentage of users paying. That gap between adoption and monetisation is exactly where the opportunity sits.
A16Z’s latest ranking of the top 100 generative AI consumer apps tells a similar story. ChatGPT remains the dominant consumer AI product, reportedly reaching around 900 million weekly active users, but the category is no longer just about one assistant. Gemini, Claude, Grok, Perplexity, Character.AI, creative tools, coding apps, AI video platforms and emerging agents are all training consumers to expect software that understands intent, generates output, remembers context and increasingly acts on their behalf.
This is the part that matters for founders: consumer AI is not one category. It is a new interface layer for every consumer category.
Dating is being rebuilt around better matching, coaching, trust and intent. Shopping is moving from search and comparison to delegation. Gaming is shifting from static content pipelines to AI-assisted studios, generative worlds and live personalisation. Entertainment is moving from passive consumption to creation, remixing and synthetic media. Personal assistants are becoming context layers across work, life and commerce.
In other words, the next generation of consumer startups will not simply “add AI” to existing apps. They will re-architect the experience around AI from first principles.
AI changes the interface. It does not remove the laws of consumer investing.
Consumer investing has always been unforgiving. The best companies look obvious in hindsight, but early on they often look messy, weird or too niche. The market does not care about your narrative. It cares whether people use the product, tell their friends, pay for it and keep coming back.
AI does not change that.
If anything, it makes the bar higher. AI makes it easier to build, easier to launch and easier to create something that looks impressive for five minutes. That means attention gets more expensive and novelty decays faster.
For AI consumer startups, I care much less about the first demo and much more about the following:
- What does the D1, D7 and D30 retention curve look like?
- Are the best cohorts improving or deteriorating?
- Is there a natural frequency of use?
- Are users generating, consuming or transacting repeatedly?
- Is there evidence of willingness to pay?
- Does paid acquisition work, or is ROAS negative once the AI costs are included?
- Are users inviting others, sharing outputs or creating organic distribution?
- Does the product get better with more context?
- Is this a feature, a workflow, a network or a habit?
The most dangerous trap in consumer AI is confusing intensity of reaction with retention. A user can be amazed once and never return. They can post a screenshot and churn. They can join the Discord and never activate. They can generate 20 images in one evening and never pay.
That is not a consumer company. That is entertainment.
The best AI consumer startups create a loop. The user does something, gets value, leaves behind context or content, and has a reason to return. Over time, the product should know more, personalise better, produce better outputs, or become more useful because of the user’s history.
The AI magic moment matters. But the cohort curve matters more.
The LLMs are now consumer platforms
The large model companies are not just infrastructure providers anymore. They are consumer platforms.
ChatGPT, Gemini, Claude, Perplexity, Grok and Meta AI are becoming new front doors to the internet. For many users, the LLM is where they start a task: searching, comparing, writing, planning, shopping, learning, coding or creating.
That has major implications for founders.
First, LLMs are becoming distribution layers. If consumers begin product discovery, travel planning, content creation or shopping inside an assistant, the assistant becomes a gatekeeper. Founders used to optimise for SEO, app stores, social feeds and paid acquisition. Now they also need to think about how AI assistants discover, recommend and execute.
Second, the model labs are moving up the stack. OpenAI is building shopping, memory, images, voice, video and agents. Google can connect Gemini to Search, Android, YouTube, Maps and Workspace. Meta can distribute AI through WhatsApp, Instagram, Messenger and Facebook. These companies are not neutral suppliers. They are increasingly competitors.
That kills a lot of thin wrappers.
If your product is just a generic chat interface on top of a model, you are exposed. If your feature can be copied into ChatGPT, Gemini or Claude with better distribution and lower cost, it probably will be.
But LLMs also create the opening. They teach consumers new behaviours. They normalise AI interfaces. They expand what people are willing to delegate to software. And they make vertical, opinionated products more valuable where the horizontal assistant is too generic.
The startup opportunity is not to build a worse ChatGPT. It is to own a specific context, workflow, community, content layer, transaction, data asset or habit that compounds over time.
The consumer AI categories that matter
AI is touching every consumer category, but not every category will produce venture-scale outcomes. I would focus on the places where AI changes either frequency, creation, personalisation, transaction or identity.
Gaming: creation gets cheaper, discovery gets harder
Gaming is one of the most interesting categories because AI changes both the supply side and the operating layer.
It is reducing the cost of prototyping, concept art, dialogue, localisation, QA, asset generation, analytics and live operations. Smaller teams can move faster. Studios can test more ideas. UGC can become more powerful. AI NPCs and adaptive gameplay can create more personalised experiences.
But this also creates a problem. If AI makes it easier to create games, it increases the number of games. The bottleneck moves from production to distribution.
That is why I am more interested in AI that improves iteration, retention and monetisation than in generic “prompt-to-game” demos. In gaming, the hard questions are familiar: are players coming back, are sessions getting longer, are cohorts improving, is LTV expanding, does the game have a content loop, and can paid acquisition work?
Israeli examples are relevant here because Israel has deep gaming DNA. Playo.ai is working on prompt-to-playable gaming. Keewano is building AI intelligence for gaming and consumer app teams. The real opportunity is not AI as a gimmick, but AI as an operating advantage: faster testing, better live ops, smarter segmentation and improved retention.
Entertainment and creators: output is abundant, taste becomes scarce
AI video, voice, music, avatars and editing tools are turning more people into creators. That is exciting, but it also creates a new problem: if everyone can produce content, content itself becomes less scarce.
Taste, workflow and distribution become more important.
The best creator AI tools do not just generate output. They help users make something good, make it faster, adapt it to a platform, learn from performance and repeat the workflow. A creator tool with weak retention is usually just a toy. A creator tool with repeat usage, paid conversion and organic sharing can become infrastructure.
This is where companies like Lightricks, D-ID, Hour One, Munch and Decart are relevant from Israel. They each touch a different part of the generative media stack: editing, avatars, synthetic video, short-form repurposing and real-time generative media. Dazl is adjacent in AI-assisted application creation, moving from prompt to usable product.
This category is tough, but continues to attract investment. Just this week, Suno announced a $400M series D, in February Runway raised $315M series E and ElevenLabs raised $500M series D at $11 billion valuation. All three offer both B2B and B2C products that have become part of the infrastructure layer for companies and consumers alike. Whether you’re creating a gaming studio or Youtube videos, you’ll need music, video editing and voice over.
The investor question in this category is not “is the output impressive?” It is “does the product become part of the creator’s workflow?”
Shopping and commerce: from search to delegation
Shopping is moving from search to delegation.
The old consumer journey was keyword search, tabs, reviews, price comparison and checkout. The AI-native journey is more conversational: “Find me the best carry-on bag for a three-day work trip, under £250, that fits European cabin restrictions and does not look cheap.” The assistant asks questions, narrows the options and may eventually transact.
OpenAI reversed course when it comes to agentic shopping. It shut down its Checkout product shortly after it launched. But it’s not the end of agentic commerce. Consumer habits are changing fast and this changes who owns demand. Retailers and brands used to optimise for search, marketplaces and social platforms. Increasingly, they will need to be visible to agents and recommendation systems.
ZyG is an interesting Israeli example, not because it is a classic consumer app, but because it sits inside the consumer commerce value chain. Founded by ironSource alumni, ZyG is building an AI-powered commerce engine for product growth and DTC scale. That reflects a broader point: some of the best consumer AI companies may be B2B2C. They may power the brands, stores, creators and platforms that reach the end consumer.
In commerce, I would look hard at ROAS, contribution margin, payback periods and whether AI improves conversion enough to justify the cost. “AI shopping assistant” is not enough. The question is whether it changes purchase behaviour.
Dating and social: AI can help, but trust is the product
Dating is a good example of where AI is both promising and dangerous.
Swipe fatigue is real. Users want better matches, less wasted time and more authentic connection. AI can help with profile quality, safety, intent, matching, coaching and event-based discovery.
But dating is also one of the most trust-sensitive consumer categories. Too much automation can make the experience feel fake. AI-written messages, AI-enhanced profiles and synthetic personas can easily make the problem worse.
The winners will not be the apps that automate dating. They will be the products that use AI to improve confidence, trust and connection.
Social is hard to penetrate and scale. Instagram, Snap, X are nearly two decades in the making. Newer social networks are either too niche (and therefore less VC-backed) or fade quickly (remember Clubhouse, the buzzy audio social network? Or BAYC?)
To succeed in social and dating, the metrics here are not just matches or messages sent. They are repeat engagement, successful conversations, offline conversion, safety outcomes and whether users feel less exhausted by the process.
In consumer AI, automation is not always the product. Sometimes the product is trust.
Personal assistants: horizontal interfaces, vertical habits
The horizontal assistants are already huge. That is obvious.
But the existence of ChatGPT or Gemini does not mean every vertical assistant dies. It means the vertical assistant has to be much more specific. A good example is Town, which just raised $55M series A. Town built a personal AI assistant that works across the tools you already use: email, calendar, Slack, docs, WhatsApp, desktop, web. It learns how you work and starts proactively pitching in. Not because you configured it just right, but because it actually understands you and how you work.
A great AI travel product should know your family constraints, budget, food preferences and tolerance for chaos. A great parenting assistant should understand age, safety and context. A great finance assistant should know your actual spending and goals. A great health or wellness companion needs trust and continuity. A great consumer productivity tool should reduce friction in a recurring workflow.
The question is not whether a horizontal model can technically answer the same prompt. It probably can. The question is whether the vertical product has context, habit, data, UX and trust that make it meaningfully better.
That is the narrow path for consumer AI assistants. The goal in this category, is to build a Jarvis-AI type of assistant we all know from Iron-Man.
Consumer hardware: the surprising return of atoms
One emerging area that has become more investable as software moats get eroded by AI is consumer hardware.
For the past decade, many VCs were allergic to consumer hardware. It was capital intensive, operationally complex, lower margin and hard to scale. Software looked cleaner. But AI is changing that equation. If software features are easier to copy, the physical object can become part of the moat.
I am seeing more founders building around smart wearables, specialised headphones, desktop games, connected collectibles, toys, music instruments and other physical products that combine AI, sensors, community and software. Some of these products are gaining real traction and attracting venture funding because they offer something a pure app often struggles to create: presence, ritual, identity and daily usage. Case in point, digital table top startup Board, just raised $20M series A led by USV.
The bar is still high. Hardware does not get a free pass because it has AI inside it. The investor questions are different but equally unforgiving: are people pre-ordering, paying, using it repeatedly, sharing it, upgrading it, buying accessories, joining the community, and telling others about it? What are the gross margins? What is the supply chain risk? Is the AI layer essential or cosmetic? Does the product become better with data, context and software over time?
The most interesting consumer hardware companies are not gadgets. They are software-enabled consumer products where the physical object creates habit, defensibility and emotional attachment. In a world where many apps can be cloned quickly, that may become more valuable again.
What the best founders are getting right
The strongest AI consumer founders I see are not selling “AI”. They are selling a better consumer outcome.
They start with a real behaviour. They know the natural frequency of use. They understand the wedge. They can explain why the user comes back. They care about onboarding, activation and cohort retention. They know their CAC, even if early. They know whether users are willing to pay, and what the gross margin looks like after inference costs.
AI is a means to an end. It can be helpful in accelerating time to market, fine tuning GTM and making more with less. But it doesn’t replace the need for product-market-fit. Creativity and originality are still at the heart of successful consumer products, even if they rely on AI heavily.
They are also honest about distribution. In consumer, “we will go viral” is not a strategy. The best teams understand whether growth will come from creator-led distribution, UGC, referrals, SEO/GEO, app stores, paid acquisition, influencers, communities, partnerships or embedded workflows.
Most importantly, they understand that AI should improve the loop. Better recommendations, better outputs, better personalisation, better timing, better conversion, better retention. If AI only improves the demo, it is not enough.
What I would avoid
I would be cautious with five types of consumer AI startups.
- Thin wrappers with no retention. If the product has no workflow, no context and no reason to return, the model labs will absorb it.
- Novelty products. Fun once is not enough. I’ve seen ‘personal robots’ that could just be an app on your phone.
- Products with expensive usage and weak monetisation. AI can create bad gross margins very quickly if users generate a lot and pay little.
- Apps that rely entirely on paid acquisition before proving retention. You can buy downloads. You cannot buy love.
- Products where automation damages the core experience. In dating, social, kids, wellness and companionship, the human element is often the product.
The Israeli and investor lens
Israel is usually discussed in AI through cybersecurity, chips, infrastructure and enterprise software. That misses part of the picture.
There is a real Israeli opportunity in consumer AI, especially around gaming, entertainment, commerce, creative tools and AI-native product creation. These are areas where Israel already has talent, global distribution experience and founder muscle memory from gaming, adtech, mobile apps and commerce.
Some will be AI-native studios. Some will be creator tools. Some will power gaming teams. Some will sit inside commerce infrastructure. Some will sell to consumers directly. Others will sell to the companies that reach consumers.
There is also a renewed opening for Israeli founders building AI-enabled consumer hardware, from gaming peripherals and smart wearables to connected toys, music devices and collectibles, especially where the hardware creates a defensible daily ritual and the software layer compounds over time. A recent example, Israeli startup Musical Beings has raised 20x their target in a recent Kickstarter campaign.
Another example, that also happens to be in music, is Heavys – the first headphones designed for Heavy Metal. With over 20,000 units sold, the company found a real niche and is able to compete with giants.
For investors, the bar should be simple but unforgiving:
- Retention
- Engagement.
- Cohort analysis
- Willingness to pay
- Organic distribution
- ROAS if you are using paid acquisition
- Why this gets better with scale
- Why this does not become a feature for another product
Consumer AI is back, but the playbook is not “AI everywhere”. The playbook is still consumer investing: behaviour, frequency, emotion, distribution and monetisation.
The difference is that AI changes what is possible inside the product.
The companies that win will not be the ones with the most impressive demo. They will be the ones that turn AI into a habit.
Israeli founders: If you’re building in consumer AI, reach out to us at Remagine Ventures. We’re always happy to chat and provide constructive feedback.
He is a former General Partner at Google Ventures (GV) in Europe, former head of Google for Entrepreneurs in Europe, and founding head of Campus London, Google's first startup hub. Eze writes on Israeli tech, venture capital, artificial intelligence, and founder strategy.
He is also the founder of Techbikers, a nonprofit that brings together the startup ecosystem on cycling challenges in support of Room to Read.
