What the Experts See Coming in 2026

Artificial intelligence closed 2025 as the largest recipient of global venture capital, accounting for close to 50% of all funding, up from 34% a year earlier. Crunchbase data shows that total investment in the sector reached about $202.3 billion during the year, covering the full AI stack, including infrastructure, foundation models, and enterprise and consumer applications. 

However, questions remain about whether this surge in investment will translate into sustainable returns. As attention shifts from funding volumes to outcomes, expectations for 2026 are increasingly focused on productivity gains, adoption at scale, and measurable business impact.

Here is what leading voices expect next, drawing on reporting and analysis from Google Cloud, Stanford University, Microsoft, PwC and Sequoia Capital.

1. AI Agents Move From Novelty to Everyday Work Tools

Enterprise use of AI agents is expected to expand sharply. In its 2026 outlook, Google Cloud argues that employees will increasingly delegate tasks to multiple agents that work together across entire workflows. Instead of focusing on execution, people will spend more time setting direction and making judgment calls. Early deployments already suggest material productivity gains, reinforcing the idea that agents will become a standard layer in knowledge work rather than a niche capability.

2. AI Hits a Reality Check on Ambition

While adoption accelerates, expectations around artificial general intelligence (AGI) are cooling. Researchers at Stanford Human-Centered AI Institute predict that 2026 will be a year when AI’s limits become clearer. 

Stanford professor James Landay has argued that AGI is unlikely to arrive in the near term, shifting attention toward practical impact rather than sweeping claims. At the same time, governments are expected to push harder on AI sovereignty, seeking greater control over where models run and where data resides.

3. AI Becomes Embedded in Scientific Discovery

AI’s role in research is expected to deepen beyond literature review and data analysis. According to Peter Lee of Microsoft Research, AI systems will increasingly generate hypotheses, design experiments and operate lab tools alongside human scientists. This evolution mirrors how AI has already reshaped software development through pair programming and automation, and it could significantly shorten the cycle from idea to discovery in fields such as chemistry, biology and materials science.

4. Quantum Computing Edges Closer to Practical Use

Quantum computing remains early, but experts believe progress is accelerating. Microsoft executives point to hybrid computing models, where quantum systems complement AI and classical supercomputers, as a path toward meaningful advantage. Improvements in error correction and logical qubits suggest that quantum tools may begin addressing problems that classical machines cannot, particularly in materials science and medicine, sooner than many expect.

5. The Rise of the AI Generalist

Workforce structures are likely to change as agents take over many specialized tasks. PwC predicts growing demand for generalists who can oversee AI systems and connect their outputs to business goals. In functions such as IT and finance, deep specialization may matter less than the ability to manage and interpret automated work, reshaping career paths and organizational hierarchies.

6. Responsible AI Shifts from Principle to Practice

Executives increasingly agree that responsible AI improves trust and returns, but operationalizing it has lagged. PwC expects 2026 to be a turning point, driven by the spread of agentic systems that require new governance approaches. Automated testing, continuous monitoring and clearer documentation are likely to replace slower, manual oversight models as companies try to keep pace with adoption.

7. A tale of Two AIs in Investment and Adoption

David Cahn, a partner at Sequoia Capital, believes 2026 will be defined by two opposing realities in AI.

On one side, he expects delays. Rapidly rising demand for AI infrastructure is likely to collide with supply constraints across chips, data center construction, industrial equipment, and skilled labor. Semiconductor capacity cannot scale quickly, and many large data center projects may fall behind schedule. Expectations around artificial general intelligence are also being reset, with timelines now shifting toward the 2030s. That raises the risk that some of today’s massive capital spending could arrive before the ecosystem is fully ready to absorb it.

On the other side, Cahn sees no slowdown in AI adoption. The strongest AI startups continue to scale rapidly, moving beyond the “zero to $100 million” phase toward billion-dollar revenue trajectories.

The core message is that 2026 will not deliver a sudden AI breakthrough or a dramatic AGI moment. Instead, progress will come through sustained execution.