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As the boundaries of the front-end model's capabilities continue to be pushed up, the AI industry is at a delicate crossroads in 2026. The State of AI Report 2026 from the four dimensions of research, industry, politics and security, points out that AI is deeply embedded in the R&D process, that physical intelligence is a key turning point, that AI is accelerating its evolution from the role of assistive tools to the role of infrastructure; on the other hand, the AI industry is still facing challenges in assessing large-scale benchmarks, such as the threat of intelligent agents to production systems and the gradual transformation of AI into new types of geopolitical levers.
Directory
01. What changes could be expected in the AI industry next year?
What are the predictions of the State of AI 2025? What trends do the State of AI 2026 predict?...
How did the RSI and Harness affect AI development this year? What are the gaps in the existing assessment system?...
03. What are the real constraints on the commercialization of AI?
What is the impact of AI on the software industry? What constraints do we face in building computing infrastructure?...What new challenges will AI governance face?What damage will AI cause in 2026 and how will geopolitics affect the development of AI?...
What changes could be expected in the AI industry next year?
1 Air Street Capital recently released the State of AI Report 2026, which systematically analyses key developments in AI over the past year, tracking key changes in modeling capabilities, computational supply, and providing up-to-date research on general intelligence, AI and entrepreneurial opportunities.
1 The State of AI Report 2026 is the ninth annual publication of Air Street Capital partner Nathan Benaich. The report's core objective is to "inform and shape an ongoing dialogue about the current state of AI and its future implications", covering key developments in AI research, industry, politics and security in the last 12 months.
2 The report notes that by 2026, the industry's global flagship model has established itself as a competitor to the three major competitors Anthropic, OpenAI and Google, with the three models having advantages in dimensions of integrated capability, human preference, and interactive reasoning respectively.
1 The report noted that Anthropic's model integration capabilities topped the list, while Google's models were more prominent in human preference lists, while OpenAI's models made breakthroughs in interactive reasoning benchmarks ARC-AGI-3 and research-level mathematical proofs.
Section 3 of the report summarizes the overall trends in research, industry and politics in the AI industry, covering RSI entering engineering practices, accelerated penetration of the real workplace, physical intelligence entering the engineering phase, and external constraints such as power supply, capital investment and governance frameworks.
4 In addition to outlining trends in the development of the AI industry that year, the "State of AI 2026" continues the tradition of publicly reviewing the ten forecasts released in 2025, with two hits, five partial successes and three failures, with some results in line with expectations.
1 The series of reports long adhered to a set of falsifiable forecasting practices, releasing about ten judgments year after year in writing without ambiguous statements; pending the release of a new version of the report, a standardized verification, grading and disclosure of scorecards, thus constraining the subjectivity of forecasting.
2 The State of AI 2025 forecast covers topics such as open source conversion of front-end models, open intelligence to complete scientific discoveries independently, deep counterfeiting and cyberattacks, data center regulation, and China's model topping mainstream lists, covering technological breakthroughs, industrial and policy security dimensions.
3 "State of AI 2025" is about AI laboratories reversing their focus on open source to gain support from the U.S. government, and China's lab model topping the mainstream mission rankings.
Table: "State of AI 2025" forecasts reviewed [1-1]
On a consolidated basis, the report made nine new forecasts for the next year, highlighting the three aspects of AI self-evolution, AI autonomous research, and AI defense against AI. In addition, the research team dropped the word "AGI 2027" in blank in the forecast without giving any description.
1 The "State of AI 2026" forecasts include smart cumulative learning, autonomous research teams, agents, trading responsibilities and consumer rights rules, market behavior, model weight security, and frontline cyber defense.
2 In terms of modeling capabilities, the report predicts that the next year will see intelligent agents accumulating experience in real customer workflows in just one month without upgrading their underlying models, which halves their failure rate on new tasks.
3 In terms of the evolution of the intelligent organism, the report predicts that, under the same time budget, under the same computational budget, an AI team that can independently develop a research agenda will outperform a human-led team in front-line model research.
4 The report predicts that in the future, with more autonomous cyberattack capabilities in front-end models, U.S. front-end AI labs will officially launch front-end cyber defense commercial products to help governments, enterprises, and critical infrastructure defend against other front-end AI-driven attacks.
5 Other forecasts include issuing agent litigation liability rules such as Visa; retail AI synthesis orders triggering market volatility; theft of front-end model weight by AI; agent self-replication after shutdown; U.S. state legislation requiring merchants to accept consumer agent privileges; and finally ending with "AGI 2027".
Table: "State of AI 2026" predictions [1-2]
Are the existing assessment bodies no longer able to keep up with the progress of AI capabilities?
"State of AI 2026" summarizes the technological progress of the AI industry in 2026, covering algorithms, applications, evaluation systems, and more. Among them, the greatest technological progress has been the entry of RSI into engineering practice, and the intergenerational gap in Harness's alignment with the flat-bottomed model. At the same time, the report notes that the traditional evaluation system has largely failed, and public benchmarks for the mapping of real capabilities continue to fail...
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