The AI Trends 2025 report is here—fully updated through 12/30/2025. It’s 100+ pages covering everything from the macro picture down to scaling laws, with proprietary analysis throughout. There’s also a market map of over 1,800 AI startups, highlighting the top 10% that matter most.
This article is a summary of the report’s key findings, with screenshots and the AI agent market map. For the full report & database:
Multimedia and PDF versions of the report: link
AI agent market map: link or head to the bottom of the article
Key Themes from the 2025 AI Trends report
From Experiment to Economic Engine
This is the fourth edition of my annual AI Trends report. I started writing these in 2022, before ChatGPT launched, and watching this technology evolve from academic curiosity to economic force has been something else.
This year’s report is titled “Crossing the Chasm” because that’s what the data shows: generative AI has moved from early adopters into mainstream production use. It’s no longer about potential—it’s generating real revenue, displacing real labor, and running into real physical constraints.
What follows are the findings I think matter most.
1. AI Agents Are Matching Human Professionals
The shift from AI-as-assistant to AI-as-worker is showing up in benchmarks now. The GDPVal benchmark tests whether AI can produce professional-grade deliverables—things like 3D engineering models and financial analyses. The latest results: GPT-5.2 matches or beats human experts (with an average of 14 years of experience) 70.9% of the time.
The economics are stark. For a range of knowledge work, AI agents are now over 20x cheaper for basic tasks and more than 3x cheaper for advanced work like programming and creative writing.
This is already affecting hiring:
Employment for workers ages 22-25 in highly exposed roles (software development, customer service) has fallen sharply
Job postings for AI-exposed roles are down 12% relative to less exposed roles
When surveyed, companies report AI-driven headcount reductions most often in Engineering (42%), CS & Support (27%), and Marketing (26%)
2. Energy Is the Bottleneck
For all the focus on algorithms and chips, power is emerging as the binding constraint.
Epoch AI found that while chip manufacturing could theoretically support an 80,000x increase in training compute by 2030, power constraints cap that at around 10,000x. Energy is the tightest limit by a wide margin.
This isn’t just theoretical. 92% of senior data center professionals now cite utility power availability as a major barrier. 44% face wait times of four years or more for grid connections. Some are building their own on-site generation—gas turbines, even small modular nuclear reactors.
3. Adoption Is Faster Than Any Technology We’ve Measured
By mid-2025, 55% of US adults had used a generative AI product. That’s a faster adoption curve than the internet or the smartphone.
The products driving this:
ChatGPT: 900 million weekly active users, >$12B ARR. It’s become the default brand for AI the way Google is for search.
Claude Code: $1B revenue run rate six months after launch. This is the clearest commercial proof that autonomous agents can replace high-value professional work at scale.
Sora 2 & Nano Banana: High-fidelity video and image generation moved from novelty to genuine creative tool for millions of users.
4. Acquisitions Are the Exit Path, Not IPOs
AI exit activity increased 44% from 2023 to 2025, but the biggest value is going to Big Tech acquirers, not new public companies.
The interesting dynamic: incumbents are using licensing and acqui-hire structures to sidestep antitrust scrutiny. Microsoft’s deal with Inflection AI became the template—they hired the key employees and licensed the technology without a formal acquisition. It effectively transfers the most important assets while avoiding regulatory review.
5. Is This a Bubble? It’s Complicated.
Valuations are high—95th percentile on the Goldman Sachs bubble framework. But other indicators that typically precede crashes (profit decline, credit risk, leverage) remain healthy. High valuations alone don’t cause crashes; deteriorating fundamentals do.
The new risk factor is asset lifespan. The telecom bubble left behind fiber optic cable with a 25-30 year useful life. The AI boom is built on GPUs with 2-4 year lifespans that depreciate fast as new chips release. That makes the capital stack more fragile and more dependent on continuous external financing.
I’d call the fundamentals healthy but the financing structure worth watching closely.
6. Where the Next Wave of Startups Will Come From
The framework I use: map job tasks by automation feasibility against total wages paid for that role. High feasibility + large wage pool = attractive target for agentic AI companies.
This worked well in practice. The high-potential roles I flagged in last year’s report (software developers, financial managers, legal professionals) attracted the most VC and produced the fastest-growing startups—Cursor, Harvey, etc.
Closing
The data on adoption, labor displacement, and infrastructure strain all point the same direction. The open questions are about pace and constraints—how fast energy infrastructure can scale, how financing structures hold up, and which verticals see the most disruption next.
There’s a lot more in the full report: deeper dives on scaling laws, sector-by-sector breakdowns, and a market map of 1,800+ AI startups. What I’ve covered here are just the findings I thought deserved the most attention.
Full Report
The energy constraint is the most under-discussed part of this whole story. Compute, models, and capital scale fast — grids and power plants don’t. Whoever controls energy and inference efficiency will quietly control the real pace of AI deployment.
Well written. Thanks for sharing.