Current News on Startups and Venture Investments as of February 21, 2026: Mega-Rounds in AI, Capital Concentration, Venture Market Trends, and Key Signals for Funds and Investors.
Venture Capital Market: Capital Concentration and Growing Competition for Deals
By mid-February 2026, the venture market increasingly follows the "winner takes almost all" model: the largest checks and the highest valuations are again flowing to a limited circle of AI companies and infrastructure players, while the broader array of early-stage investments is being chosen much more rigorously. Investors are more willing to pay a premium for proven revenue, access to data and computational power, and the ability to quickly scale products in the corporate segment. For funds, this translates into heightened competition for a limited number of “obvious” deals and the necessity to delve deeper into unit economics, training/inference costs, and demand sustainability.
Main Topic of the Day: OpenAI Round as an Indicator of a New "Supercycle" of Private Capital
A key marker of the week has been the preparation for the largest financing round in recent years surrounding OpenAI: discussions are underway to attract approximately $100 billion or more, with several strategic investors and major tech groups reportedly considering participation. The importance lies not only in the size but also in the rationale behind such financing: funds are essentially being converted into accelerated access to computing, chips, cloud infrastructure, and engineering talent. This reinforces the trend where “capital expenditure on intelligence” becomes the new norm, and the boundary between venture capital, private equity, and strategic investments blurs.
For the startup market, this creates a dual effect. On one hand, there is a displacement effect: part of the capital that could have been allocated across a wide spectrum of B2B/SaaS, biotech, or fintech is flowing into a few oversized stories. On the other — a powerful wave of secondary benefits arises: there is increasing demand for applied models, observability and security tools, inference optimization, specialized data, and vertical solutions for various industries.
Major Deals and Signals of the Week: AI Raises the Bar on Valuations Again
The focus is on mega-rounds in generative AI and everything related to “delivering intelligence” on an industrial scale. The market is discussing record-breaking deals that elevate reference valuations for late stages and deepen the gap between leaders and others.
- Generative AI: Ultra-large rounds among segment leaders are setting new benchmarks for valuations and the volume of capital needed to compete on the frontier.
- AI Infrastructure: The demand for alternatives and diversification of supply chains is intensifying interest in developers of accelerators, specialized computing platforms, and “AI-clouds.”
- Vertical AI Products: The best-funded companies are those demonstrating ROI through time/risk savings (compliance, financial control, cybersecurity, software development) and have a clear go-to-market strategy.
Infrastructure and “Hardware”: Betting on Computing as a Strategic Asset
The shift in market phases is evident in how investors evaluate infrastructure startups: “GPU access,” stack efficiency, optimization of computing costs, and the ability to deliver predictable performance have become as crucial as product differentiation. In late stages, this leads to deals where the economic logic resembles infrastructure projects: long payback horizons, substantial capital expenditures, but potentially high entry barriers.
For venture funds, this means that due diligence increasingly includes technical metrics (model training costs, latency, query costs, load profiles), as well as contractual details with cloud providers and chip suppliers. Teams that can turn computing into a predictable business process and safeguard their margins at scale are winning.
What’s Happening at Early Stages: The Market Has Become More Pragmatic
In seed and Series A rounds, there is a noticeable turn toward “applied efficiency.” Founders are less forgiven for unclear monetization, but support is given more willingly to those demonstrating specific ROI for clients, short implementation cycles, and understandable sales economics. In the AI segment, there has been increased filtering of “wrappers” without unique data, integrations, or industry advantages: investors are waiting for either proprietary data, deep process integration, or infrastructural competencies that are hard to replicate.
A practical checklist that is more frequently voiced in negotiations includes:
- Unit Economics: gross margin considering inference, support, and training costs.
- Proven Impact: measurable KPIs for the client (speed, accuracy, loss reduction, compliance risks).
- Protectability: data, distribution channels, partnerships, regulatory/process barriers.
- Scalability: sales repeatability and the ability to manage growth without explosive increases in COGS.
M&A and Exits: Strategists Return, But Choose Wisely
Against the backdrop of capital concentration in AI, the role of strategic buyers is strengthening, especially in sectors where AI has a direct impact on R&D, risk management, or operational efficiency. In biotech and pharma, there is a marked readiness to acquire technologies that accelerate drug development and clinical processes; in the enterprise sector, there is interest in development, security, and compliance tools. However, the overall exit market remains selective: either “must-have” assets or teams/technologies that can be quickly integrated into existing products are being acquired.
Venture Geography: The US and Major Hubs Strengthen Dominance, but Niche Ecosystems Persist
The majority of the largest deals continue to concentrate in the US and a few global tech centers where talent, capital, and corporate buyers are available. However, funds are also interested in “secondary markets” — where regional AI platforms are created, infrastructure for local languages and sectors is developed, and fintech and industrial solutions are linked to specific regulatory regimes. In 2026, regional differentiation increasingly occurs not based on “the presence of startups” but on access to data, infrastructure, and corporate demand.
Risks: Conversations About an “AI Bubble” Are Returning — and This Is a Useful Stress Test
Ultra-high valuations and rounds inevitably raise the issue of overheating. For investors, this is not so much a reason to “exit from AI” but a reason to distinguish more accurately:
- Frontier Models (expensive, capital-intensive, betting on scale and infrastructure);
- Infrastructure (high entry barriers, risk of cyclical capex among clients);
- Vertical Applications (dependency on data quality and sales, but the economics become visible faster).
The main practical risk in 2026 is the mismatch between revenue growth rates and computing cost growth rates. Therefore, the market requires a new standard of transparency: model efficiency metrics, service costs, retention rates, and real added value for the client.
What Investors Should Watch in the Coming Weeks
By the end of the quarter, the market is focused on three sets of signals: (1) the completion and conditions of the largest AI rounds, (2) the dynamics of corporate budgets for AI infrastructure and implementations, (3) strategic activity in M&A, especially in biotech, cybersecurity, and development tools. At a tactical level, venture funds should maintain focus on companies that deliver measurable efficiency and can scale without proportional increases in computing costs.
