(New York) — The US technology investment market is entering a period increasingly defined by artificial intelligence, computing infrastructure, energy, robotics, cybersecurity and strategic technologies. US venture capital firms deployed about $320 billion across 15,352 deals in 2025, a 51% increase in deal value from the previous year, with artificial intelligence accounting for 65.4% of total venture deal value. The scale of capital flowing into technology suggests that the next phase of the market will be shaped less by conventional software and more by technologies requiring massive computing capacity, physical infrastructure and strategic capital.
The acceleration continued into 2026. US startups raised more than $400 billion during the first half of 2026, surpassing every previous full-year investment total on record, although the overwhelming majority of capital went into AI companies and financings of $100 million or more. That concentration provides an important starting point for assessing the next three years, as technology investment is increasingly likely to flow toward sectors with large capital requirements, strategic importance and identifiable paths to monetization.
First, AI agents will become one of the largest areas of technology investment. The next phase of artificial intelligence is moving beyond chatbots and generative models toward autonomous agents capable of performing sequences of tasks across software systems, from coding and customer service to procurement, finance and enterprise operations. For investors, this expands the AI opportunity from model development into software that can directly execute business processes, making recurring revenue, workflow ownership and measurable productivity gains increasingly important investment criteria.
Second, AI infrastructure and data centers will attract enormous amounts of capital. The rapid expansion of AI models has created demand for computing capacity, data centers, power, cooling systems, networking equipment and specialized infrastructure. The investment opportunity therefore extends well beyond companies developing AI models, as investors seek exposure to the physical infrastructure required to operate the next generation of AI services.
Third, semiconductors, memory, networking and optical technology will become increasingly strategic investments. The expansion of AI computing has created bottlenecks across the semiconductor supply chain, including advanced processors, high-bandwidth memory, networking equipment and optical connectivity. As AI infrastructure spending rises, venture capital and strategic investors are likely to allocate more capital to companies positioned between chip manufacturers, hyperscalers and data-center operators.
Fourth, physical AI and robotics will move closer to commercial-scale investment. Humanoid robots, autonomous machines, warehouse robotics and industrial systems combining computer vision with AI are attracting capital as artificial intelligence increasingly moves from digital environments into the physical economy. The investment thesis is shifting from robotics as a specialized hardware sector toward robotics as an extension of AI, with applications ranging from manufacturing and logistics to healthcare, agriculture and defense.
Fifth, defense technology will become a major destination for US technology capital. The combination of geopolitical competition, autonomous systems and growing demand for advanced military capabilities is expanding the market for defense startups. AI-enabled surveillance, drones, autonomous vehicles, electronic warfare, sensing systems and battlefield software are increasingly attracting private capital alongside government procurement, creating a business model that differs from conventional enterprise software.
Sixth, cybersecurity will increasingly become an investment in protecting AI from AI. As companies deploy autonomous agents and AI systems across critical operations, the attack surface is expanding beyond conventional endpoints, networks and cloud infrastructure. Investors are therefore likely to focus increasingly on AI-native cybersecurity companies capable of identifying vulnerabilities, detecting threats, simulating attacks and responding automatically to increasingly sophisticated attacks.
Seventh, quantum computing will move deeper into strategic technology portfolios. The US government is putting substantial resources behind quantum computing and quantum manufacturing, providing an additional catalyst for private investment. Over the next three years, investors are likely to distinguish more carefully between companies developing fundamental quantum hardware, companies building quantum software and businesses developing applications that could benefit from quantum computing once the technology reaches commercial scale.
Eighth, AI-enabled biotechnology and life sciences will become a major intersection between technology and healthcare investment. AI is increasingly being applied to drug discovery, protein design, molecular simulation and biological research, creating opportunities for companies that combine software platforms with proprietary scientific capabilities. The investment case is particularly significant because AI could reduce the time and cost required to identify promising drug candidates, although clinical, regulatory and commercialization risks remain substantially different from those of conventional software companies.
Ninth, energy technology will become increasingly integrated with the AI investment thesis. The rapid construction of data centers is turning electricity availability into a strategic constraint for technology companies, making power generation, grid infrastructure, battery storage, nuclear technology, geothermal energy and power-management systems increasingly relevant to technology investors. As AI computing becomes more energy intensive, companies capable of delivering reliable and scalable power could capture a growing share of the value created by the AI economy.
Tenth, strategic capital will play a larger role alongside traditional venture capital. In 2025, nontraditional investors including hedge funds, sovereign wealth funds, corporate investors and endowments participated in roughly 30% of US venture transactions but accounted for 83% of investment value, according to NVCA. Such investors are increasingly important because leading AI companies require financing on a scale that exceeds the traditional venture model, while strategic investors may also seek access to intellectual property, computing capacity, supply chains and emerging technology platforms.
The growing concentration of capital is likely to shape the technology market through 2029. In 2025, the five largest companies receiving venture financing, including OpenAI, CoreWeave, xAI, Anthropic and Databricks, collectively raised nearly $60 billion, while overall deal count increased by less than 1%. The result is a market where headline investment figures can rise sharply even when the number of companies receiving meaningful capital remains relatively stable.
That concentration is also creating opportunities further down the technology supply chain. If AI model developers require enormous amounts of computing capacity, then semiconductor suppliers, memory companies, networking providers, cooling specialists, data-center developers, power companies and cybersecurity vendors can capture part of the same investment cycle without having to build a foundation model. This could make the next technology investment cycle broader than the current AI leaders suggest.
The private-market exit environment will also influence investment behavior. US venture-backed exits reached about $217 billion in 2025, more than twice the previous year’s level, although the figure remained well below the 2021 peak, while secondary transactions became an increasingly important source of liquidity for private companies and investors. If IPO and M&A markets continue to improve through 2027–2029, investors could gain more opportunities to recycle capital into new technology companies rather than keeping capital locked in mature private businesses.
Traditional software companies, meanwhile, face a different investment environment. Rapid advances in AI agents are forcing investors to reconsider whether conventional software products can maintain pricing power and customer loyalty when similar functionality can increasingly be delivered through AI-native systems. Startups with mission-critical workflows, proprietary data, strong customer relationships and credible AI strategies are likely to receive greater investor attention than companies whose products can be easily replicated by increasingly capable AI models.
The shift will also raise the standards for technology due diligence. Investors will increasingly examine computing costs, dependence on hyperscalers, data-center contracts, electricity availability, semiconductor supply, intellectual property, customer concentration and gross margins rather than relying primarily on user growth or revenue expansion. For capital-intensive technology businesses, infrastructure contracts and financing structures could become as important as the underlying technology.
The distinction between technology investment and infrastructure investment is therefore likely to become increasingly blurred. Data centers, power generation, semiconductor manufacturing, fiber networks, cooling systems and robotics can all form part of the same AI value chain, creating opportunities for private equity, infrastructure funds, sovereign wealth funds, corporate investors and private-credit providers. Joint ventures, strategic investments and project financing could consequently become more important alongside conventional venture capital.
The largest risk is that AI investment growth may not translate immediately into equivalent growth in end-user demand or corporate earnings. The enormous amount of capital being committed to AI infrastructure means investors will increasingly differentiate between projects supported by contracted customers and predictable cash flows and projects based primarily on long-term assumptions about future AI demand. Capital discipline could therefore become more important as the market moves beyond the initial AI investment surge.
By 2027, the market will increasingly test whether extraordinary AI capital expenditure can generate measurable productivity gains and sustainable profits. Hyperscalers and technology companies are committing unprecedented amounts of capital to computing infrastructure, increasing pressure on AI businesses to demonstrate that revenue and margins can eventually justify those investments. The transition from narrative-driven investment toward return-driven investment could determine which companies remain capable of attracting capital through 2029.
The US technology investment market is therefore entering a period in which the opportunity extends far beyond finding the next AI software company. Capital is increasingly moving across the entire technology stack, from semiconductors, networking and data centers to energy, robotics, cybersecurity, defense technology, quantum computing and biotechnology. Through 2029, investors are likely to place increasing emphasis on companies that combine technological differentiation with recurring revenue, strategic infrastructure access and a defensible position within critical technology supply chains.
