Wstęp
If you’re leading an AI startup today, you’re not just competing with other founders—you’re navigating one of the most capital-saturated and strategically contested markets in tech history, marked by the rapid growth and intense competition within the AI industry. With major companies building proprietary large language models, infrastructure layers racing to become standardized, and generative AI reshaping industries from media to enterprise SaaS, the stakes are high. The current AI boom, driven by advancements in artificial intelligence such as transformer architectures and large language models, has led to unprecedented investment and accelerated innovation across sectors.
Artificial intelligence has evolved from early research and symbolic reasoning to today’s transformative applications, profoundly impacting industries worldwide.
We’ve developed a practical five-step framework to help AI founders evaluate this decision. Each step includes key questions every founder should ask themselves when weighing the pros and cons of raising more capital versus selling the business. To bring this to life, we draw on real-world examples—like Lovable’s recent funding round and Scale AI’s partial sale to Meta—highlighting how founders tackled these decisions and what trade-offs came with each path.
1. Know What Machine Learning Solution You’re Really Building for AI Startups
Not all AI startups are created equal. Some resemble AI research labs, others offer tools with real-world practical applications, some are building an AI program, and many are still proving whether they’re building a product—or just a feature.
Take Lovable, for example: their user-centric generative AI tools enhance creative workflows with a clear product-market fit. They weren’t building a tech demo—they built a sticky, viral product. Their strong usage metrics and ARR allowed them to raise at a $1.5B valuation in a competitive round led by Accel.
Ask yourself:
- Are you solving problems grounded in customer needs—or building cool demos using GPT-4?
- Are you dependent on external APIs, or do you own core infrastructure (e.g., unique machine learning models, proprietary datasets, or even a proprietary programming language)?
- Is your solution embedded into workflows like customer support, content creation, or financial decision-making? For example, an expert system is a type of AI solution that has delivered real-world impact in specialized domains.
Differentiation matters. Defensibility matters more. Historically, expert systems provided defensible AI solutions by encoding domain expertise, which led to widespread adoption and industry growth.
2. Recognize the Type of Attention You’re Getting
Investor interest is not the same as market validation. In AI, interest can come from several different directions:
- VCs are looking for platforms that can scale across verticals with winner-take-all potential.
- Strategic buyers often want to acquire core tech, teams, or early market presence to strengthen their existing offerings.
- Private equity firms may provide funding or seek acquisitions, typically focusing on established startups with proven business models.
Scale AI exemplifies this dual attention. They sold 49% of the company to Meta at a $29B valuation—effectively cashing out part of the business without giving up control. The draw wasn’t just their revenue—it was the strategic value of their data infrastructure to Meta’s AI roadmap.
Red flag: If you’re getting inbound M&A interest but struggling to raise a round, it may mean you’ve built something valuable—but not venture-scale.
That’s OK—but you need to act accordingly.
3. Assess Market Conditions and AI Competitive Pressure
We’re not in an AI winter, but the heat is shifting. Capital is flowing toward clear winners, not experiments. Models are becoming commoditized. Being “an LLM wrapper” is not enough.
Look at Glean: they raised at a $7.2B valuation after crossing $100M ARR. Why? Because they’re solving a deep enterprise need—internal search across enterprise tools—and have strong signals of durability. Their AI applications aren’t flashy; they’re essential.
Contrast that with founders chasing short-term virality. If you don’t have distribution, a data moat, or a differentiated use of neural network technology, now might be the best time to partner up—before incumbents catch up. Recent advances in deep learning and deep learning techniques have been breakthrough technologies, driving the success of many leading companies. Solutions gaining traction today are often robust AI systems, including the first ai system milestones and modern applications like chatbots and virtual assistants. To succeed, companies need access to big data and massive datasets, which are critical for training advanced models. What sets successful companies apart is their ability to leverage breakthrough technology and stay ahead of the curve in a rapidly evolving landscape.
Ask:
- Are we still ahead of the pack—or getting caught by commoditization?
- Can we justify raising $30M+ to go after enterprise buyers—or would a strategic exit better realize the value we’ve created?
Ongoing competition and progress in the field are being driven by rapid AI development, making it essential to continuously innovate and adapt.
4. Map Your Team’s Appetite for Risk and Ownership
Some founders want to swing for a $10B IPO. Others want to build, win, and sell.
Neither is wrong—but being unclear leads to wasted years.
- Raising means more dilution, more execution risk, and more time.
- Selling means giving up some control—but can unlock broader reach or a faster path to impact.
Don’t just look at valuation. Ask what kind of journey you and your co-founders want. Do you want to stay independent for another 5–7 years? Or would being part of a larger platform let you do your best work?
You don’t get bonus points for burning out with a high cap table and no exit.
5. Understand the Strategic Landscape—And Who Benefits Most from What You’ve Built
Each of these recent moves—Scale AI’s deal with Meta, Lovable’s rapid growth and funding, and Glean’s late-stage mega-round—reflects a different answer to the same question: who gets the most value from this company?
- Scale AI had leverage with multiple suitors and monetized strategically, integrating AI into their products and services to stay ahead.
- Lovable chose to raise and keep growing, betting on their user base and velocity.
- Glean doubled down with a long-term vision in enterprise search and AI-powered knowledge tools, focusing on improving customer experience for end users.
The best founders—often counted among the greatest innovators in the field—don’t just respond to inbound interest. They proactively shape the narrative—and decide based on both personal goals and company potential.
Practical AI applications, such as virtual assistants, are delivering real value across industries and demonstrate the impact of these strategic decisions.
Final Thoughts: There’s No “Right” Answer—Just the Right Fit
The entire industry is undergoing a transformation, driven by advances in information technology and foundational breakthroughs in information theory. This transformation reflects a paradigm shift that began with the cognitive revolution of 1956, fundamentally changing how we understand the mind and intelligence. Artificial intelligence (AI) is no longer a backroom experiment in computer science departments or AI laboratories—it’s now powering real business world outcomes, from customer interactions to logistics to creative workflows, generating and saving millions of dollars for organizations.
The origins of artificial intelligence can be traced back to pioneering computer scientists like Alan Turing, whose foundational work led to the development of the Turing test as a method to evaluate machine intelligence. The Dartmouth Conference, organized by key figures such as John McCarthy and Nathaniel Rochester, marked a pivotal event where artificial intelligence AI was officially framed as a scientific discipline. Early AI research by Allen Newell and Herbert A. Simon introduced symbolic reasoning, laying the groundwork for intelligent systems capable of tasks like playing chess and natural language processing. These milestones were achieved in AI laboratories, which became centers for innovation and collaboration among leading computer scientists.
From the early conceptualization of the artificial brain and artificial humans in the 20th century, inspired by science fiction and the influential science fiction play Rossum’s Universal Robots, to the pursuit of artificial general intelligence and machine intelligence that can rival human intelligence, the field has evolved rapidly. Whether you’re building artificial people who can play chess—matching or surpassing human players—and hold a conversation using advanced human language processing, or developing more targeted AI applications in healthcare or finance, the impact of artificial neural networks has been transformative. These innovations have enabled a broad range of AI capabilities, from game-playing to natural language understanding, and have expanded the reach of intelligent systems.
Today, AI agents are becoming increasingly autonomous, with advanced decision making capabilities and the ability to interact in human-like ways, reducing the need for human intervention. The future promises even more, with opportunities for early access to cutting-edge AI technologies and tools that will continue to push the boundaries of what is possible. However, it is important to recognize the unique qualities of human reasoning, which involve unconscious, embodied knowledge and intentionality—traits that distinguish human intelligence from machine processes.
So: are you better off doubling down or cashing in?
The answer depends on what you’re building, who values it most, and where you want to go as a team. If you’re weighing this decision, we at Aventis would be happy to discuss your situation and explore whether we’re the right partner to help you navigate this complex process.
Dlaczego potrzebujesz doradcy ds. fuzji i przejęć dla swojej firmy AI?
Każda firma AI jest wyjątkowa, tak jak wyjątkowa jest podróż każdego założyciela. Dlatego ważne jest, aby szukać wskazówek u ekspertów w dziedzinie fuzji i przejęć AI, w szczególności doradców ds. fuzji i przejęć, którzy specjalizują się w sektorze technologicznym i mogą zrozumieć twoją konkretną sytuację.
Doradcy ds. fuzji i przejęć w branży technologicznej posiadają dogłębną wiedzę na temat dynamiki rynku, metodologii wyceny i zawiłości procesu fuzji i przejęć. Podczas gdy Ty koncentrujesz się na zarządzaniu swoją firmą, doradcy pilnie dbają o każdy szczegół i opowiadają się za jak najlepszą transakcją w Twoim imieniu. Sukces doradców ds. fuzji i przejęć w branży technologicznej jest powiązany z Twoim sukcesem, a ich wiedza może często znacząco wpłynąć na ostateczną cenę sprzedaży.
O Aventis Advisors
Aventis Advisors jest doradcy M&A koncentrując się na spółkach technologicznych i wzrostowych. Wierzymy, że świat byłby lepszy z mniejszą liczbą (ale lepszej jakości) transakcji fuzji i przejęć przeprowadzanych w odpowiednim momencie dla firmy i jej właścicieli. Naszym celem jest zapewnienie uczciwego, opartego na wiedzy doradztwa poprzez jasne przedstawienie wszystkich opcji dla naszych klientów - w tym opcji utrzymania status quo.
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