Don’t wait for funding — start earning now: how AI rewrote the rules for B2C — сolumn by Tanya Ladanova, CEO of TRMNL4
Any article can be bookmarked on the site to read later.
Reading mode enlarges the text, removes all unnecessary things from the page and allows you to concentrate on the material. Here you can turn it off at any time.
Venture deals have dropped to a historic low, and for B2C products, that’s changing the growth playbook. Instead of chasing funding rounds, more companies are building their businesses around direct audience monetization. AI has made launching and personalizing services so simple that you can get started in just a few days with access to ready-made APIs. This opens the door to faster profitability, but also raises the risk of copycats and losing users.
In a column for Vector, Tania Ladanova, CEO of TRMNL4, shares her insights on how AI is reshaping growth strategies for B2C startups and driving changes in their monetization.
Last year, B2C startups captured only 6% of global investments — the lowest share in history. Yet, despite declining interest from funds, another major shift is underway. Users are spending more: in 2024, people spent $150 billion on mobile apps (+13%), while AI apps brought in $3.3 billion in revenue (+51%) from 3.3 billion downloads (+26%).
The point is, AI allows B2C products to deliver personalized value from day one and start generating revenue without relying on external funding.
In the past, personalization took years: a team of engineers, in-house infrastructure, and complex models. Now, it takes just a few days, a few thousand dollars, and APIs from OpenAI, Midjourney, or ElevenLabs. But that speed comes with a risk: AI products are easy to copy if they lack a strong brand or a loyal community.
Case studies: Duolingo, Cal AI, and TRMNL4 startups
I see proof of AI-driven B2C monetization every day in my work with startups. Here are just a few examples.
Duolingo is a great example of AI-driven monetization. In 2023, it launched the Max plan with GPT-4 at $29.99 a month, priced higher than the usual Super plan, which helped raise the average revenue per user. The next step was cutting content costs. Previously, exercises were written manually; now, AI generates the tasks, and the team simply reviews them. In just one year, Duolingo rolled out 148 courses — a process that used to take years in its early days. As a result, by 2024 the company reported a 41% revenue increase, a 51% rise in daily active users, and 9.5 million paying subscribers.
Another example is Cal AI, built by 18-year-old Zak Yadegari. Traditional calorie counters often failed because of the tedious manual input — searching for every ingredient, weighing it, and entering it into the database. Most users quit by day three. Cal AI solves this with computer vision that recognizes food instantly with 90% accuracy. This speed keeps users engaged, with many logging up to four meals a day. But here’s the catch: an early «wow» effect doesn’t guarantee retention. Without a daily value, users will leave. That’s why Cal AI focuses on building habits and showing health progress. Thanks to this approach, the app has already hit over 5 million downloads and $30 million in annual revenue.
In my daily work with startups globally, I see many similar cases.
- Alter is a philosophical self-awareness app where users pick an AI companion, like Freud or Burns, and engage in conversations that help them better understand themselves and the world. This «humanized» experience works: 20% of users remain active even on day 30, which is twice the average retention in the mental wellness niche (typically 3 – 7%). On top of that, Alter has been doubling its revenue every month since launching in early 2025, making the startup increasingly visible to investors.
- Zoog is a platform where grandparents record fairy tales in video format with AR, turning long-distance communication with their grandchildren into warm evening rituals. Each recording feels like a magical cartoon, creating a genuine emotional connection between generations.
In just a year, the Insight Partners–backed startup grew 4x to $1.3M ARR, reached 13,000 paying users, and boosted retention by 28% — that’s 5 – 10 times higher than the average for AR or social platforms. During its acceleration program, the team began personalizing content feeds with AI, partnering with Paramount, DreamWorks, and Netflix to create tailored playlists for each family, increasing watch time and the shared-experience effect. Zoog shows that deep emotional connections don’t just engage users; they also drive strong monetization.
- Bloom is an investment app built for Gen Z. Instead of dull Excel sheets, it delivers AI-powered advice through Stories and chat. With smart use of TikTok and Instagram, Bloom hit 1.8M downloads, earned 36,000+ reviews (4.7 rating), and landed on the list of the top 36 financial apps of 2024. After joining TRMNL4’s program, the team learned to acquire users 2.5 times cheaper, while paid ads started performing twice as well. Revenue grew to $3.1M — a 55% jump from last year. Bloom shows how AI, paired with the right strategy, can fuel rapid growth.
Why AI isn’t that simple for startups
Despite the AI boom, new challenges are emerging. One of the biggest is defensibility: almost any AI feature can be copied today. We’ve seen how quickly an advantage disappears when a product lacks a clear identity. That’s why, in our work with teams, the focus is shifting from building features to building a recognizable brand and a community that truly keeps users engaged.
Another challenge is low retention. AI products often deliver a strong «wow» effect early on but lose users if they don’t provide consistent, ongoing value. Teams that tap into real user motivations, better sleep, improved health, and learning progress tend to achieve much stronger retention. It’s a trend I’ve been observing over the past year and a half working with startups.
Another interesting trend is zero tolerance for mistakes. If an AI response feels off or «not about me», users leave immediately. People now expect AI to act like a true assistant — precise, reliable, and tailored to their needs.
Steps to take now
From working with thousands of teams, I see that AI-native B2C products have a fresh opportunity. It feels like the early days of social media in the 2010s — users are excited again, and they’re willing to pay.
But the biggest risks are fast feature copying and poor retention.
Here are my principles for fast growth:
Usefulness matters more than technology
With tools like Lovable, you can piece together the tech in just a few days. That’s no longer the hard part. The real challenge is deeply understanding your users. Build a quick MVP with minimal features, gather as much feedback as possible, and remember: retention is the metric that matters most.
Go-to-market = content × speed
Don’t over-plan campaigns, but launch them. Focus on creators, niche communities, AI-driven search, and live user interactions. And when you see traction, double down and scale that channel fast.
Revenue retention matters more than DAU
A hundred thousand downloads isn’t success. Ten thousand users who pay monthly, use the product consistently, and recommend it to friends — that’s a real business. Focus on the depth of value, not just the vanity metrics.
Monetize from day one
Don’t wait for the «perfect moment». If you’re delivering value, there should be revenue. Build upsells, subscriptions, and personalized features into the user experience from the start.
It’s not about funds coming back — it’s about B2C products finally becoming profitable. Today, investment isn’t the starting point; it’s a tool for scaling. Founders who can launch quickly, test, adapt, and monetize are the ones driving the new wave of consumer tech.
