Artificial intelligence is experiencing unprecedented capital liquidity. In 2024 alone, AI startups attracted over $110 billion in investment. This surge was the primary driver behind a broader global VC rebound, which reached approximately $314 billion in 2024.
However, massive liquidity brings massive noise. The market is saturated with "wrapper" solutions, and product definitions are shifting rapidly. For capital allocators, the challenge is no longer identifying innovation, but distinguishing between fleeting trends and sustainable business models.
Synthesizing global market research, investment forecasts, and insights from the Web Summit Lisbon panel "AI as a Game-Changer" (featuring leaders from Handy.ai, V7.VC, Brave1, and Reply.io), we outline four strategic pillars defining the investment landscape through 2026.
From Software Tools to "Hiring" Autonomous Agents
The value proposition of B2B technology is evolving from "assistance" to "autonomy." We are moving away from buying software that helps employees work faster, to "hiring" autonomous agents that perform the work themselves.
Oleg Bilozor (CEO, Reply.io) highlights this transition in the sales domain:
Businesses... don't want to hire sales people. We want AI agents... Right now it's a first process till the booking meeting, but in future, I believe sales calls or demos will be held as well by AI.
This shift is driven by a demand for tangible financial returns. According to EY, 97% of business leaders investing in AI are already reporting positive ROI, fueling the appetite for systems that deliver outcomes rather than just efficiency.
Consequently, startups solving narrow problems face the existential threat of being replaced by foundation model updates. The safety zone lies in complexity. Because AI currently struggles to connect disparate tools, platforms that integrate multiple systems (e.g., complex DevOps or Admin workflows) remain defensible.
For Founders: Stop building "copilots." Build end-to-end agents that replace entire workflows, not just individual tasks.
For Investors: Divest from simple productivity tools. Allocate capital to platforms with deep integration capabilities that LLMs cannot easily replicate.
The End of the "AI" Label
The label "AI" has lost its power as a differentiator. Sergii Potapov (Founder & Investor, V7.VC) emphasizes that for private capital, a "real AI" startup is defined by a strong technical team and a "data moat". Without proprietary data, a product is viewed merely as a temporary layer.
This urgency is backed by macro forecasts: Goldman Sachs estimates AI investment could reach $200 billion globally by 2025. This capital flood lowers the barrier to entry, creating a sea of generic competitors.
Investors are becoming ruthless about substance. Iryna Zabolotna (USF & Brave1) is blunt about the lack of substance in many pitches:
80% of all startups using 'We are blah blah AI solution' isn't important. I'm looking for solving the issue.
The market is shifting toward "Value-Add Wrappers" – solutions that may rely on external models but provide superior utility through unique UI/UX, better prompting, or specific fine-tuning.
For Founders: Do not pitch "AI" as your product. Pitch the unique proprietary data you own that makes your AI smarter than generic models.
For Investors: Ignore pitch deck jargon. Audit the backend: Does the startup own the data loop, or are they renting intelligence?
Commercial Execution: Distribution Is the New Product
In the current cycle, technical superiority without visibility is a path to failure. The consensus is clear: distribution is more important than product.
Oleg Bilozor states plainly:
Mediocrity products getting everything because you just built distribution... my suggestion would be building not a product, but building a distribution machine.
Regulatory pressure is intensifying. As the EU AI Act and US executive orders reshape the landscape, market access is becoming complex. The winners will not be the best coders, but the companies capable of navigating these frameworks to secure market share in the US, EU, and Asia.
Founders, particularly those with engineering backgrounds, often prioritize code over sales – a weakness identified by Sergii Potapov. However, building a "distribution machine" must happen before perfecting the product.
For Founders: If you are a technical founder, your first key hire should be a Head of Sales or Growth, not another engineer.
For Investors: Assess the "Go-To-Market" strategy with the same rigor as the technical due diligence.
Global Context and Strategic Funding
Innovation is geography-dependent, and local perceptions often lag behind global realities. To survive, founders must physically align with global tech hubs. Oleg Bilozor illustrates this with Cursor, an AI coding tool initially dismissed by local developers but embraced by Silicon Valley as a game-changer. His advice is simple: "Travel to learn".
The funding landscape is diversifying beyond traditional VC. Public sector funding is becoming a massive catalyst, with the EU and India ramping up programs to compete with the US and China.
Iryna Zabolotna highlights Ukraine’s state investor model (Brave1) as a prime example. Unlike traditional VCs, they offer equity-free capital with no IP claims.
- Defense Tech: Allocates 95% of the state budget, writing checks over $2 million for autonomous systems.
- Civil Innovation: Programs like "Startup Edge" target EdTech, DeepTech, and GreenTech to prepare companies for private capital.
For Founders: Do not rely solely on VCs. Target non-dilutive state grants and government accelerators to extend your runway without sacrificing equity.
For Investors: Scout "state-graduated" startups. Companies emerging from rigorous defense or government accelerators often possess vetted technology and reduced early-stage risk.
Conclusion
The 2026 landscape will be defined by specificity and execution. The "AI" label is dead; the era of the autonomous agent and the data moat has arrived.
For founders, the mandate is to move beyond simple tools to build integrated workflows and distribution engines.
For investors, the focus must remain on fundamentals: back teams with deep domain expertise, verified data strategies, and the grit to compete globally. In a market flooded with liquidity but competitive on substance, these distinct qualities will separate the temporary wrappers from the enduring industry giants.
Editor: Iryna Andriushchenko
