AI Trends 2025 Lookback: Agents Cross the Chasm and What Comes Next
Published on 15.01.2026
AI Trends: 2025 Lookback and 2026 Outlook
TLDR: 2025 marked the year AI agents crossed from early adopters to mainstream adoption, with reasoning models now matching professionals with 10+ years of experience. The rapid growth is creating both massive value and significant infrastructure constraints.
The numbers tell a compelling story: by mid-2025, 55% of US adults had used a generative AI product. That adoption curve is faster than both the internet and the smartphone. We're not just witnessing technological change—we're living through one of the fastest technology adoptions in human history.
What defined 2025 wasn't just more capable models, but a fundamental shift in how they work. The models that defined the year—OpenAI's GPT-5, Anthropic's Claude 4.5, Google's Gemini 3.0 Pro, and DeepSeek's R1—all implemented reasoning approaches. Instead of responding immediately, these models think before answering. This seemingly simple architectural change markedly improved capability across knowledge work.
The evidence is in benchmarks like GDPVal, which tests whether AI can produce deliverables companies would actually pay for: 3D engineering models, financial analyses, customer service responses. The benchmark encompasses 44 jobs representing $3 trillion in US wages. As of December 2025, the best models matched or beat human experts averaging 14 years of experience over 70% of the time. "Matched or beat" means evaluators preferred or couldn't distinguish AI output from work produced by credentialed professionals. That's a staggering capability threshold.
This capability created a chain reaction: measurable economic value drove infrastructure investment, which ran into energy constraints, which created leveraged financial risk. The irony here is notable—the very success of AI is creating bottlenecks that could slow its expansion. Employment for workers ages 22-25 in highly exposed roles like software development and customer service has already fallen sharply.
For architects and teams, there are important considerations here. First, the consolidation pattern: AI exit activity increased 44% from 2023 to 2025, but the biggest value is flowing to Big Tech acquirers, not new public companies. Acquisitions are the exit path, not IPOs. This means the landscape is consolidating around a few major players, which has implications for vendor strategy and platform decisions.
The report also raises a question that should concern everyone building with AI: when AI writes almost all code, what happens to software engineering? This isn't theoretical—it's happening now. Teams need to think about how to structure work, maintain institutional knowledge, and develop talent in an environment where AI handles increasingly complex tasks autonomously.
The energy bottleneck deserves attention too. AI infrastructure continues to ramp up, but the US hasn't solved its energy constraints. This could create regional disparities in AI capability and cost, affecting where organizations can economically run AI workloads.
Key takeaways:
- 55% of US adults used generative AI by mid-2025—faster adoption than internet or smartphones
- Reasoning models now match professionals with 14+ years experience in 70%+ of evaluated tasks
- AI exit activity up 44%, but value flows to Big Tech acquirers rather than IPOs
- Energy constraints are becoming a limiting factor for AI infrastructure expansion
- Employment in exposed roles (software, customer service) for ages 22-25 is declining sharply
Tradeoffs:
- Reasoning models gain significantly improved accuracy but sacrifice response latency
- Rapid AI adoption creates business value but concentrates market power in fewer companies
- Infrastructure investment enables scale but runs into energy constraints that create financial risk