Current Startups and Venture Capital News as of 21 February 2026: Mega-Rounds in AI, Capital Concentration, Venture Market Trends, and Key Signals for Funds and Investors
Venture Capital Market: Capital Concentration and Increasing Competition for Deals
By mid-February 2026, the venture market is increasingly operating under a "winner takes almost all" model: the largest checks and highest valuations are once again going to a limited circle of AI companies and infrastructure players, while a broad segment of early-stage investments is being scrutinised much more rigorously. Investors are more willing to pay a premium for confirmed revenue, access to data and computing power, as well as the ability to rapidly scale products in the corporate sector. For funds, this means growing competition for a limited number of "obvious" deals and the necessity to delve deeper into unit economics, training/inference costs, and demand sustainability.
Top Story of the Day: The OpenAI Round as an Indicator of a New 'Supercycle' in Private Capital
A key marker of the week has been the preparation for the largest funding round in recent years surrounding OpenAI: raising an amount in the region of $100 billion or more, with several strategic investors and major tech groups reportedly considering participation, according to business media sources. More important than the size is the rationale behind such financing: the money is essentially being converted into accelerated access to computing resources, chips, cloud infrastructure, and engineering talent. This cements a trend where "capital expenses for intelligence" become the new norm, and the boundaries between venture capital, private equity, and strategic investments are becoming blurred.
For the startup market, this creates a dual effect. On one hand, there is a displacement effect: part of the capital that could have gone into a wide range of B2B/SaaS, biotech, or fintech is being diverted into a few super-large stories. On the other hand, there is a powerful wave of secondary benefits: demand is rising for applied models, observability tools, security, optimised inference, specialised data, and vertical solutions for various industries.
Major Deals and Signals of the Week: AI Sets the Bar for Valuations Once Again
The focus is on mega-rounds in generative AI and all things related to "delivering intelligence" at an industrial scale. The market is discussing record-volume deals that raise reference valuations for late-stage companies and widen the gap between the leaders and the rest.
- Generative AI: super-large rounds for segment leaders are setting a new benchmark for valuations and the volume of capital required to compete at the frontier.
- AI Infrastructure: the demand for alternatives and supply chain diversification is increasing interest in accelerator developers, specialised computing platforms, and "AI-cloud" solutions.
- Vertical AI Products: companies that can prove ROI through time/risk savings (compliance, financial control, cybersecurity, software development) and have a clear go-to-market strategy are receiving the best funding.
Infrastructure and 'Hardware': Betting on Computing as a Strategic Asset
The phase shift in the market is evident in the way investors are assessing infrastructure startups: "GPU access", stack efficiency, cost optimisation for computing, and the ability to provide predictable performance are now on par with product differentiation. In late-stage deals, this leads to transactions where the economic logic resembles that of infrastructure projects: long payback periods, substantial capital investments, but potentially high barriers to entry.
For venture funds, this means that due diligence increasingly includes technical metrics (model training costs, latency, request costs, load profiles), as well as contractual details with clouds and chip suppliers. Teams that can convert computing into a predictable business process and protect margins at scale will prevail.
What is Happening in Early Stages: The Market has Become More Pragmatic
In the seed and Series A stages, there is a noticeable turn towards "applied efficiency". Founders are less forgiven for unclear monetisation, yet more readily supported those who demonstrate concrete ROI for the client, a short implementation cycle, and a clear sales economy. In the AI segment, there has been an increase in filtering out "wrappers" without unique data, integrations, or industry advantages: investors are looking for either proprietary data, deep integration in processes, or an infrastructure competence that is difficult to replicate.
A practical checklist that frequently comes up in negotiations:
- Unit economics: gross margin considering inference, support and training costs.
- Proven effect: measurable KPI for the client (speed, accuracy, loss reduction, compliance risks).
- Defensibility: data, distribution channels, partnerships, regulatory/process barriers.
- Scaling speed: repeatability of sales and the ability to handle growth without a proportional increase in COGS.
M&A and Exits: Strategics are Returning but Choose Selectively
In light of capital concentration in AI, the role of strategic buyers is becoming more pronounced — especially in sectors where AI has a direct impact on R&D, risk management, or operational efficiency. In biotech and pharma, there is a willingness to acquire technologies that accelerate drug development and clinical processes; in enterprise, there is interest in development, security, and compliance tools. Nonetheless, the overall exit market remains selective: only "must-have" assets or teams/technologies that can be integrated quickly into existing products are being purchased.
Venture Geography: The US and Major Hubs Strengthen Their Dominance, but Niche Ecosystems Persist
The majority of the largest deals continue to be concentrated in the US and a few global technology centres where skills, capital, and corporate buyers are readily available. However, funds are also interested in "second markets" — where regional AI platforms, infrastructure for local languages and industries, as well as fintech and industrial solutions tied to specific regulatory regimes are being developed. In 2026, the differentiation of regions increasingly occurs not by "availability of startups" but by access to data, infrastructure, and corporate demand.
Risks: The Discourse Around an 'AI Bubble' Returns — a Useful Stress Test
Sky-high valuations and rounds inevitably raise the topic of overheating. For investors, this serves not as a reason to "exit from AI", but as a prompt to differentiate more precisely:
- Frontier models (expensive, capital-intensive, betting on scale and infrastructure);
- Infrastructure (high barriers to entry, cyclicality risks in client capex);
- Vertical applications (dependent on data quality and sales, but the economics can be seen quicker).
The main practical risk for 2026 is the mismatch between the growth rate of revenue and the rate of growth in computing costs. Hence, the market requires a new standard of transparency: metrics for model efficiency, service costs, retention, and actual added value for the client.
What Investors Should Watch in the Coming Weeks
By the end of the quarter, the market will be looking for three key sets of signals: (1) the completion and terms of the largest AI rounds, (2) trends in corporate budgets for AI infrastructure and implementations, and (3) the activity of strategics in M&A, particularly in biotech, cybersecurity, and development tools. On a tactical level, venture funds should maintain their focus on companies that deliver measurable efficiency and can scale without proportional growth in computing costs.
