Artificial intelligence has entered the "real world AI" stage

Author: Wang Jie

During the Summer Davos held in Dalian, China at the end of June, global AI (including robotics) industry leaders gathered to discuss the current development of the AI industry and the upcoming major trends. Among them, Wang Jie, a pioneer AI investor in China and co-director of the AI Economic Research Center at the Shenzhen Digital Economy Research Institute, proposed that after going through the three stages of "generating content," "reasoning ability," and "action ability," the AI industry is about to enter the "Real-World AI" stage. All sectors of the industry also need to make corresponding preparations to welcome the arrival of this stage.

The following is the full text of "AI Arrives at the 'Real-World AI' Stage," first published by Tencent Tech.

We are at AI’s reality moment.

In the past few years, AI has learned to generate, reason, and act. The next stage is not just whether AI can give a better answer on the screen, but whether AI can learn from real-world feedback and deliver acceptable, sustainable work results in the real world. Today, we are at the "real-world moment" of AI development.

Observation: AI is continuously leaving the benchmark world

Over the past few years, the main narrative of the AI industry has been organized by benchmarks. Each model release is accompanied by a set of scores: language understanding, professional exams, mathematical reasoning, code generation, software engineering, web operations, multimodal question answering, and agent tasks. Scores rise, the industry gets excited; scores saturate, new benchmarks are created. Benchmarks have become milestones on the long road of AI development.

However, an increasingly clear fact is emerging: AI is continuously leaving the benchmark world. Many tests once considered difficult enough to represent intelligence are being approached, matched, and surpassed by models one after another. Researchers continue to define new tasks, new leaderboards, and new evaluation sets, and models continue to catch up and pull down new flags. This is certainly part of scientific progress, but it also shows that pure benchmarks are increasingly unable to carry the full meaning of AI development.

The benchmark world is essentially a "theoretical world": problems are predefined, answers have clear boundaries, evaluation criteria can be formalized, and the cost of failure is usually just a score. It is suitable for proving that a model has a certain capability, but it does not mean it can deliver the results we expect in real workflows. A model that answers correctly in a test bank does not mean it can stably complete tasks in a company's procurement process, hospital diagnosis and treatment collaboration, factory scheduling system, legal document risk review, or urban governance emergency response.

Therefore, when we say AI is leaving the benchmark world, we are not saying benchmarks are no longer important. On the contrary, benchmarks are still necessary dashboards for technological progress. But a dashboard is not a road, scores are not results, and demos are not delivery. Where is AI going after leaving the benchmark world? The answer is: the real world. The entire industry is entering the "Real-World AI" stage.

The transition from the "theoretical world" to the "real world"

The three old stages of the "theoretical world"

**This round of AI development has already gone through three clear old stages. The first is the "generating content" stage, typically in the form of chatbots. AI for the first time used natural language as an interface, capable of writing, summarizing, translating, conversing, and explaining, becoming a general text tool for human cognitive labor. The second is the "reasoning ability" stage, typically in the form of reasoners, represented by GPT o1 and DeepSeek R1 reasoning models. AI began to show stronger abilities in decomposition, search, planning, proof, and self-checking, handling longer chains and higher complexity problems. The third is the "action ability" stage, typically in the form of agents. AI no longer just answers questions but calls tools, browses the web, writes code, operates software, and executes multi-step tasks.

These three stages are very important. Generation gave AI language, reasoning gave AI thought, and agents gave AI preliminary hands. After being able to generate, reason, and act, the next thing is not to perform more actions in demos, but to bear results in real environments. The real world will be the environment for AI's long-term actions in the future.

Why are the above three considered old stages? Because they mostly remain in the "theoretical world" or "quasi-real world." The models face abstracted problems, not complete economic-social systems; they optimize computable feedback, not multi-agent, multi-constraint, long-cycle real results; they demonstrate capability possibilities, not work results accepted by users, organizations, institutions, and markets.

The new stage of the "real world"

We propose "Real-World AI" to reflect the new stage AI is about to enter. The definition of Real-World AI is: AI that can learn from real-world feedback, complete real-world tasks, and produce real results. The "real world" here **includes two meanings: First, training feedback comes from real-world results, users, institutions, costs, and risks, not just from standard answers; second, tasks come from real workflows, not just from test banks, sandboxes, or demos. It is not a vague label but a stage name for AI moving from capability demonstration to production delivery, from theoretical intelligence to work intelligence.

The core of Real-World AI is not to add more buttons to AI, but to put AI into a closed loop: understand real tasks, receive real feedback, execute real actions, adjust its own strategy, and finally deliver acceptable real results. It requires model capabilities to break through the current focus on "computer academic" areas such as code, software engineering, mathematics, and cybersecurity, and move towards broader human work scenarios: marketing, sales, supply chain, manufacturing, finance, law, healthcare, education, research, public governance, and robots and automation systems in the physical world.

The following is a key comparison between the real world and the theoretical world:

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In this sense, the "theoretical world" new stage"

In this sense, Real-World AI is not a single model, product, or algorithm route, but a new direction for the entire industry. It will connect post-training, reinforcement learning, tool use, memory systems, workflow integration, organizational feedback, human supervision, safety mechanisms, and economic value measurement. The real world will become AI's new training ground.

Real-World AI will output real-world intelligence. Real-world intelligence is the model capability formed by AI after receiving real-world feedback, and also the ability to turn goals into results under real constraints. It measures not the instantaneous performance of a model on static problems, but the continuous availability, reliability, and value creation ability of an AI system in real tasks. If the core of benchmark intelligence is "whether it can get the correct answer on a given problem," then the core of real-world intelligence is "whether it can complete accepted results in real tasks."

Why is the transition from the "theoretical world" to the "real world" inevitable?

This transition has technical inevitability and economic inevitability. Technically, large language models have given AI language ability, reasoning models have given AI stronger thinking ability, and agents have given AI preliminary action ability. Considering human behavior, after possessing language, thought, and action ability, a person will inevitably enter a stage of interacting with the real world. Intelligence is not an ability that stays in the mind, but the ability to achieve goals in an environment. Therefore, AI's next step is also very clear: enter the real world.

Economically, the greatest value of the AI revolution cannot forever remain in question answering, writing, and code snippets. True productivity release comes from unlocking real tasks: a customer service process is completed end-to-end autonomously, a legal due diligence is delivered stably, a supply chain is dynamically optimized, a research hypothesis is quickly verified, a robot works reliably in a warehouse or at home. Only when AI enters real workflows will enterprises count it as organizational capability, will society count it as productivity, and will humans truly feel the scale of this technological revolution.

This is also why "Real-World AI" is more operational than simply discussing AGI. AGI asks whether AI is close to human intelligence; Real-World AI asks whether AI can complete real tasks; AGI easily leads discussions toward infinite capabilities; Real-World AI pulls discussions back to feedback, results, costs, and value. It does not lower AI's goals, but puts AI's goals where it must ultimately face: reality.

Roadmap and Terminology

Roadmap

In terms of a roadmap, OpenAI's five-stage roadmap proposed in 2024 generally captured the evolution direction from chatbot to reasoner to agent, but it did not fully describe the transition from the theoretical world to the real world. Moreover, the latter two stages, innovator and organizer, are more biased towards capability characteristics an agent might possess, rather than technical forms parallel to chatbot, reasoner, and agent; the standards are inconsistent. More importantly, when this roadmap was proposed, the industry had not yet truly entered the agent stage, so judgments about what comes after the agent were naturally uncertain.

At the node where the industry is moving from the theoretical world to the real world, we need a roadmap that can better guide long-term work. We **propose the following five-stage framework: First, Foundation AI, the foundation model stage, where AI gains general representation and knowledge compression ability; Second, Generative AI, the generative AI stage, where AI gains natural language and multimodal generation ability; Third, Reasoning AI, the reasoning AI stage, where AI gains stronger search, planning, proof, and reflection ability; Fourth, Agentic AI, the agent AI stage, where AI gains the action ability to call tools, operate software, and execute steps; Fifth, Real-World AI, the real-world AI stage, where AI enters real workflows, learns from real feedback, and delivers real results accepted by humans, organizations, and institutions.

This roadmap places "Real-World AI" after the agent. The agent solves the problem of "whether AI can act," and Real-World AI solves the problem of "whether AI's actions produce acceptable consequences." The agent is the interface, the real world is the closed loop; the agent is the hand, Real-World AI is the organized work capability; the agent lets AI enter the process, Real-World AI lets AI be accepted by the process, trusted by the organization, and measured by the economy.

Further ahead, the industry may enter a larger stage: AI becomes the operational layer of the economy and society, the "digital layer" we have mentioned multiple times before. At that time, AI will not just complete individual tasks but will participate in decision support, organizational coordination, resource allocation, scientific discovery, city operations, and physical world operations. But whether this future arrives depends on whether we can cross the hurdle of Real-World AI today. Without real feedback, there is no real intelligence; without real results, there is no real productivity.

Terminology

In the past, we have already had many terms describing this round of AI development: AGI, ASI, Generative AI, Agentic AI, Embodied AI, Physical AI, etc. (World Model does not describe the characteristics of AI development but describes a model route). Overall, most of these terms are from the perspective of algorithms, capabilities, or carriers, which can be called "descriptions from an algorithm perspective." They are very important, but they also easily lead industry discussions into abstract debates of "whether the model is smart enough," "whether intelligence is infinite," and "when it surpasses humans."

A good name should have a sense of direction: it not only describes what the technology is, but also reminds us where we are ultimately going, and where we are at the moment. "Real-World AI" has this sense of direction. It does not deny AGI, Physical AI, or Embodied AI, but changes the way of asking: no longer just ask what AI is technologically, but ask what AI can do in the economy and society; no longer just ask whether AI is close to human intelligence, but ask whether AI can stably complete real tasks, create real value, and bear real consequences.

"Real-World AI" also unifies the digital world and the physical world. In the digital world, Real-World AI means AI enters enterprise software, knowledge work, transaction processes, R&D processes, and governance processes; in the physical world, Real-World AI means robots, autonomous driving, smart manufacturing, home services, and urban infrastructure learn from real environments. Regardless of whether the carrier is a browser, API, office software, robotic arm, vehicle, or humanoid robot, the core question is the same: can AI form a closed loop in a real environment, complete tasks, and be accepted by reality?

Therefore, we introduce to the entire industry "Real-World AI" as this expression. It can bring researchers, entrepreneurs, investors, enterprise users, and policymakers onto the same map: from benchmark intelligence to real-world intelligence; from the capability demonstration period to the task unlocking period; from the model competition to the productivity competition; from "AI looks like it can do it" to "AI really can do it."

Real-World AI is not the end, but the entrance. It reminds us that the most important AI work in the coming years is not just creating larger models, longer contexts, or more beautiful demos, but turning reality into a training loop, turning feedback into capability, turning tasks into value, and turning AI into a truly usable productive force for human society.

For this stage to truly arrive, the industry needs to form a new consensus. Model training needs to use real workflow feedback as a core resource for post-training, not just chase existing leaderboards; AI applications need to push products from assistant form to task delivery form, not just embed AI chat windows into software; enterprise users need to advance AI evaluation from "is it easy to use" to "can it stably complete key tasks"; investors need to re-evaluate task unlocking speed, feedback loop depth, and unit cost output beyond model parameters and demo effects; policymakers need to establish data, responsibility, safety, and audit frameworks so that real-world adoption can expand in trust.

This is the meaning of "Real-World AI" as a term. It condenses a scattered industry focus into a common direction: let AI leave the demo stage and enter the production site; leave the test bank and enter the organization; leave one-time answers and enter continuous feedback; leave abstract intelligence and enter real value. We are at AI’s reality moment. AI’s next frontier is not another benchmark; the next frontier is the real world.

The real world will become AI's new training ground.

Reality is becoming the next training loop for AI.