2026年人工智能状况报告

Foreword research, product design, and enterpriseworkflows. Adoption is accelerating, systemsare improving, and entirely new categories—agentic software, autonomous science,physical-world automation—are emerging inreal time. Intelligence is becoming cheaper,more abundant, and increasingly embeddedinto how work gets done. This report matters because it looks beneaththe surface of that momentum. What appears chaotic—a surge of capital,rapid model iteration, exploding use cases—is in fact the messy formation of a new stack.At the base, compute, energy and data arebecoming the true constraints. In the middle,models are converging in capability even ascosts fall. At the top, applications are proliferating,where most real-world value will likely be created.At the same time, ecosystems like India showhow AI’s impact may be driven less by whobuilds the models, and more by who appliesthem at scale. AI has now crossed from experimentation toinfrastructure. It is becoming the default interfacefor knowledge, the operating layer for enterprises,and increasingly, a system that acts—not justassists. As it moves beyond software into science,defense, and physical systems, the implicationsextend far beyond business. We are in the midst of a multi-trillion-dollarexperiment—over$800B deployed last yearalone—not just in technology, but in belief. Across markets, capital is flooding into AI withthe expectation that it will deliver exponentialreturns and reshape the global economy. Techgiants, sovereign funds and private equity alikeare making all-in bets—building infrastructureat unprecedented scale, even taking on debtto do so. It has all the hallmarks of a gold rush:speed, intensity, and a overarching narrative. That is what makes this moment not justconsequential but monumental. The question is no longer whether AI willreshape the world—it already is. The real questionis where value will accrue, how power will shift,and what constraints—technical, economic,and societal—will define its trajectory. And yet, this moment is fundamentallydifferent—the technology is no longer apromise;it is already at work. AI is a fundamental redefinition of how intelligenceis produced and applied—and we are only at thebeginning of understanding what that means. AI is delivering real productivity gains todayacross software development, scientific Team SenseAI T H EA I G O L D R U S H 4I N D I A’S A I L A N D S C A P E 1 0E N T E R P R I S EA I:F R O M P I L O T S T O P R O D U C T I O N A T S C A L E 2 2A IM A K E S L A R G E S T R I D E S B E Y O N D B U S I N E S S 3 2A IT E C H N O L O G Y T R E N D S 4 2 1. Big dollars chase the AI revolution,AI Gold Rush as it rewrites rules for life&work "AI is probably themost important thinghumanity has everworked on. I think of itas something moreprofound thanelectricity or fire." -Sundar Pichai, Google CEO Record$800Btotal investment in AI Venture Capital fundingfor AI startupsnearly doubling to$225.8B 97%funding growth in Venture Capital Mega-rounds($100M+)accounted for 79%of totalfunding in 2025. Number of deals as well asthe average value per dealincreased from 2024. Mega-Rounds Are Replacing Growth Rounds:Instead of gradual Series A>B>C scaling,winners jump from early traction directly intomassive rounds. AI is still evolving rapidly. Investorsare funding exploration to pushthe frontier, not just expansion. Investment into AI shows no signs ofslowing down–in Feb 2026, OpenAI raiseda$110B mega round(4X the largest IPO ever). AI now has308 unicornsthe most of any technology sector The New Unicorn Frontier- AI Application layer has the highestnumber of unicorns. Most new unicorns were born here andevery large industry sector now has an AI-native entrant. Foundational AI Layer has the largest firms in terms of revenues, funding and valuations:OpenAI($730B), Anthropic($380B), xAI($250B). Compression of Time-to-ScaleAI startups are reaching unicornstatus significantly faster than non-AIpeers—averaging 4.7 years forAI vs 6–7 years for non-AI companies. The Lean Unicorn Model Scaling no longer requires massiveheadcounts. AI-native unicorns arescaling with a median of 203 employees,less than half the 414 employeestypical of non-AI unicorns. New AI UnicornsOther New Unicorns 2 IN 3 NEW UNICORNSIN 2026 ARE AI STARTUPS In 2020, just 13%of new unicorns&$1B+exits were core-AI startups. The AI Capital Stack:$100M+Rounds Are Landingin the Application Layer model capabilities into real productsand industry workflows. The Billion-Dollar Model Race Mega-capital continues to cluster aroundfoundation model builders, where trainingfrontier systems demand billions in compute,data, and talent. The Two-Speed AI Economy Capital concentrates at the model layer,while startup velocity concentrates at theapplication layer. This stratification reflectsthe emerging AI industry architecture—where a handful of extremely well-fundedinfrastructure companies and foundationlabs power an expanding