Why Investors Invest in AI Applications and What it Means for Businesses Creating Digital Products

According to Rothschild & Co, by April 2025, 65% of all VC deals in the US will have gone to AI startups, three times more than a year ago. The funds are particularly active in consumer applications: from dating and social apps to personal assistants. Here, we analyze which projects receive funding, what investors look for, and what this means for businesses planning to launch a mobile product.

Investment Boom: Key Indicators of 2025

• In the first half of 2025, over half of all venture capital was directed toward AI projects worldwide.
• In the US, this share reached a record 64% — the highest ever.
• Generative AI remains the main attractor: according to HAI, $33.9 billion was invested in it over six months, up 19% from the previous year.
• The consumer applications segment grew to $12 billion, but monetization lags: only about 3% of users pay.
• Funds see this engagement-to-revenue gap as an opportunity and actively seek teams capable of closing it.
• Startup valuation continues to rise: median Series A investment is $13 million, Series C is $50 million, and valuations of leaders reach up to $588 million.

What It Means for Business

The audience is already there, but they only pay for real value — saving time, money, or emotional effort. 2025 remains a favorable moment for market entry: investor interest in consumer AI products is high, and projects with clear monetization models obtain funding faster and on better terms.

From ChatGPT to Apple Intelligence: How AI Became an Essential Part of User Services

The trend started with the massive success of ChatGPT: reaching 100 million users in its first 2 months. Following that, AI was embedded into Google, Microsoft, Meta, and Apple products. Today, almost every major service uses generative AI models for support, search, recommendations, and content creation.

The consumer segment has become a battleground: major players set standards, startups find niches. People are accustomed to AI in everyday tasks, changing expectations for any digital product. Today, users demand not just functions but "smart" assistance, personalization, and dialogue.

Investors understand: if a product solves a clear task faster or more conveniently with AI, it becomes a candidate for funding.

Which AI Products Receive Funding: The Most Attractive Niches

• Personal Assistants and Chatbots: AI companions, mental health support apps, self-development, and productivity tools. By 2026, this segment is expected to attract $37.7 billion. Examples: Replika, Kindroid, Pi (Inflection AI), Yutori, and others.
• Voice and Multimodal Interfaces: Funds are actively investing in products where AI understands speech, text, and images simultaneously. This forms a new generation of voice assistants, auto assistants, and smart home AI. For example, OpenAI’s GPT-4o: a multimodal AI capable of real-time dialogue, seeing, listening, and responding with voice without delays. A record $40 billion funding round involving Microsoft and others was closed in 2025 in this area.
• Social and Content Platforms with Generative Core: Projects integrating AI into everyday communication, self-expression, and entertainment. Examples include Character.AI, allowing users to interact with virtual characters and create their own, and visual social networks like Lensa AI and BeFake, transforming photos into stylized images using generative graphics.
• Gaming Solutions: Platforms like Inworld AI and AI Dungeon, where AI creates behavior, dialogues, and plot in real time, showing organic user growth. Funds are heavily investing in areas with clear engagement and retention metrics.

Step-by-Step: What to Do When Considering Creating an AI Product

1. Define the Use Case: AI is not an end by itself. Start with the question: "What problem should it solve for the user?" It could be saving time (auto-completion, quick responses), reducing effort (recommendations, personalization), or providing emotional support (chatbot, AI companion). Without a clear purpose, AI will just seem like an unnecessary addition.
2. Select the Appropriate Model: There are dozens of solutions on the market: GPT, Claude, Gemini, Mistral, Perplexity, and others. In most cases, integration via existing APIs suffices. A custom model is necessary only for narrow specialization or high privacy requirements. Evaluate costs, response quality, and legal constraints, such as data storage policies.
3. Design the Interface: Even powerful AI can’t succeed if users don’t understand how to interact with it. Provide prompts, request examples, and give context-aware responses. AI should be embedded into the familiar workflow, not break it. Simplicity and clarity in user interaction increase engagement.
4. Launch a Minimum Viable Product (MVP) Quickly: Launching a simple, functional version allows you to test hypotheses and see if users need AI in this scenario. It’s better to start with a minimal set of features, gather data, and iterate than to spend resources on a full release that may not meet actual demand.
5. Prepare for Scaling: If the AI helps solve a problem, users will come back. At this stage, implement analytics, develop monetization strategies, and start testing additional scenarios. Investors and the market focus on retention and paying traffic metrics—showing that AI provides real value.

If you are at the idea stage or planning a launch, the DigiNeat team will help you go from concept to working MVP. We develop user-friendly apps, set up integrations with the right models, and ensure a fast go-to-market process.