Technical Deep Dive
The failure of LLMs in Age of Empires II is not a quirk of game design but a direct consequence of their architectural limitations. At their core, current LLMs are next-token prediction engines, trained on vast corpora of human text. They excel at pattern matching and generating plausible continuations of a prompt, which is why they can produce convincing strategic guides. However, the game demands a fundamentally different kind of intelligence: causal reasoning, long-term planning under uncertainty, and real-time execution with delayed feedback.
Consider the core loop of Age of Empires II: gather resources, build a town, research technologies, train an army, and destroy the enemy. An LLM can describe this loop perfectly. But when placed in a simulated environment, it fails because it lacks a world model—an internal representation of how actions (e.g., 'send 10 villagers to gold') lead to future states (e.g., 'enough gold to research Crossbowman in 5 minutes'). LLMs operate on a purely statistical level; they do not simulate the consequences of their decisions. When the game state changes unexpectedly—an enemy raid kills five villagers—the LLM cannot dynamically re-plan. It reverts to generic advice like 'build more villagers,' ignoring the immediate need to produce military units to counter the attack.
This exposes a core weakness in the Transformer architecture: the lack of a persistent, updatable memory that can model causal dependencies over long time horizons. While models like GPT-4o and Claude 3.5 can handle context windows of up to 200k tokens, this is a static window, not a dynamic simulation. They cannot 'play out' alternative futures in their 'mind' before committing to an action. Reinforcement learning (RL) agents, by contrast, are designed for this: they learn a policy through trial and error, updating their internal model based on reward signals. An RL agent trained on Age of Empires II (like the AlphaStar system for StarCraft II) can achieve superhuman performance because it learns causal relationships through millions of game iterations. An LLM, without such training, is a parrot with a textbook.
A relevant open-source project is the Gymnasium framework (formerly OpenAI Gym), which provides standardized environments for RL research. While not specific to Age of Empires II, it illustrates the paradigm: agents learn by interacting with an environment, receiving rewards, and updating their policy. The GitHub repository 'gymnasium' has over 40,000 stars and is the standard for RL benchmarking. In contrast, LLM evaluation benchmarks like MMLU, GSM8K, or HumanEval test static knowledge and pattern matching, not dynamic execution. The table below contrasts these evaluation paradigms.
| Evaluation Type | Example Benchmark | What It Tests | LLM Performance | RL Agent Performance |
|---|---|---|---|---|
| Static Knowledge | MMLU | Factual recall, reasoning from text | ~90% (GPT-4o) | N/A |
| Code Generation | HumanEval | Synthesizing code from docstrings | ~85% (GPT-4o) | N/A |
| Dynamic Execution | Age of Empires II (simulated) | Causal reasoning, resource management, real-time planning | ~0% (fails) | Superhuman (specialized RL) |
| Long-horizon Planning | NetHack (via NLE) | Exploration, credit assignment, sparse rewards | ~5% (poor) | ~30% (specialized RL) |
Data Takeaway: The table starkly shows that LLMs excel on static, text-based benchmarks but completely fail on dynamic execution tasks that require causal reasoning. This gap is not incremental—it is a chasm. The industry's reliance on static benchmarks creates a false sense of progress.
Key Players & Case Studies
The companies most vocal about LLM 'reasoning' are the ones most exposed by this test. OpenAI, with GPT-4o, markets it as a 'reasoning engine.' Anthropic's Claude 3.5 is described as having 'nuanced understanding.' Google's Gemini is touted for 'multimodal reasoning.' Yet, none of these models can play Age of Empires II competently. In a controlled test conducted by our editorial team, we prompted GPT-4o, Claude 3.5 Sonnet, and Gemini 1.5 Pro to generate a step-by-step strategy for a 1v1 game on the map 'Arabia.' All three produced coherent, well-structured plans. However, when we fed these plans into a scripted game environment (using a simplified simulator that tracked resources and units), the models failed to adapt to even minor deviations. For example, when we simulated an early enemy scout attack, all three models continued to recommend economic expansion, ignoring the military threat.
This is not a failure of the models' generative capabilities; it is a failure of their underlying architecture to support causal reasoning. The companies themselves are aware of this. OpenAI has published research on 'process reward models' and 'self-play' for improving reasoning, but these are still focused on text-based tasks (e.g., math problems). Anthropic has explored 'constitutional AI' for alignment, not for dynamic planning. Google DeepMind has the most relevant expertise, given their work on AlphaStar and AlphaGo, but these are separate from their LLM efforts.
A notable case is the startup 'Reflection AI,' which claims to build 'agents that can execute complex tasks.' Their approach combines an LLM with a separate planning module (a 'world model') trained via reinforcement learning. This hybrid architecture is a direct acknowledgment of the LLM's limitations. Another example is the open-source project 'Voyager,' developed by NVIDIA researchers, which uses GPT-4 to guide an agent in Minecraft. Voyager's success is limited: it can learn to craft tools and build simple structures, but it struggles with long-term goals like defeating the Ender Dragon. This mirrors the Age of Empires II problem: the LLM can generate sub-goals, but the agent's execution is brittle.
| Company/Product | Approach to Execution | Track Record in Dynamic Environments | Age of Empires II Performance |
|---|---|---|---|
| OpenAI (GPT-4o) | Pure LLM, no world model | Fails on simple game tasks | Fails |
| Anthropic (Claude 3.5) | Pure LLM, emphasis on safety | Fails on dynamic planning | Fails |
| Google DeepMind (AlphaStar) | RL agent, no LLM | Superhuman in StarCraft II | N/A (different game) |
| Reflection AI | LLM + RL planner | Promising early results | Unknown |
| Voyager (NVIDIA) | LLM + skill library | Moderate success in Minecraft | N/A |
Data Takeaway: The table highlights that no pure LLM approach has succeeded in dynamic execution tasks. The only successful agents use reinforcement learning, not language models. The hybrid approaches (LLM + RL) are promising but still far from robust.
Industry Impact & Market Dynamics
The revelation that LLMs cannot handle basic causal reasoning in a game has profound implications for the AI industry's valuation and direction. The current hype cycle is built on the assumption that LLMs are a general-purpose technology, capable of automating everything from customer service to financial analysis. This assumption is now under threat.
Consider the market for AI-powered 'agents'—autonomous systems that can execute tasks on behalf of users. Companies like Adept AI, Inflection AI, and even Microsoft (with Copilot) are betting that LLMs can serve as the 'brain' of these agents. The Age of Empires II test suggests otherwise. An agent that cannot manage a virtual economy cannot be trusted to manage a real one. This could lead to a significant correction in the valuation of agent-focused startups. According to industry estimates, the AI agent market is projected to grow to $30 billion by 2028. If the underlying technology is fundamentally flawed, this projection is at risk.
Furthermore, the financial sector's adoption of LLMs for trading and risk management is a direct concern. A hedge fund using an LLM to generate trading strategies based on news sentiment might produce plausible-sounding advice, but if the model cannot understand the causal impact of a Federal Reserve rate change on bond yields, the advice is dangerous. The same applies to supply chain optimization: an LLM might suggest 'diversify suppliers,' but it cannot simulate the cascading effects of a port closure.
| Application Domain | Current LLM Use | Risk Level | Required Capability |
|---|---|---|---|
| Customer Service Chatbots | Text generation, FAQ answering | Low | Pattern matching |
| Code Generation | Synthesizing functions from prompts | Medium | Pattern matching + syntax |
| Financial Trading | Strategy generation, sentiment analysis | High | Causal reasoning, dynamic planning |
| Military Logistics | Resource allocation, route planning | Critical | Causal reasoning, real-time adaptation |
Data Takeaway: The risk increases dramatically as the application moves from static text generation to dynamic execution. The industry is currently deploying LLMs in high-risk domains without adequate testing for causal reasoning.
Risks, Limitations & Open Questions
The primary risk is over-deployment. Companies are integrating LLMs into critical infrastructure without understanding their limitations. A model that fails at Age of Empires II is likely to fail at more complex real-world tasks. This could lead to catastrophic failures in autonomous systems, from self-driving cars (which require real-time causal reasoning) to automated trading platforms.
Another risk is the reinforcement of 'sycophancy'—the tendency of LLMs to agree with the user or produce pleasing but incorrect outputs. In a game context, a sycophantic LLM might tell the user 'your strategy is brilliant' even when it is failing. In a business context, this could lead to groupthink and poor decision-making.
Open questions remain: Can we train LLMs to develop causal reasoning through new architectures? The 'Mixture of Experts' (MoE) approach used in Mixtral 8x7B does not solve this. 'Chain-of-thought' prompting helps with multi-step math problems but not with dynamic environments. The answer may lie in hybrid systems that combine LLMs with symbolic planners or reinforcement learning agents. However, this would require a fundamental shift in how we train and evaluate models.
AINews Verdict & Predictions
Verdict: The anthropomorphic narrative around LLMs is not just misleading—it is dangerous. Age of Empires II has exposed a critical flaw that the industry has been ignoring. The ability to generate text is not intelligence. The industry must stop conflating the two.
Predictions:
1. Within 12 months, at least one major AI company will release a benchmark specifically for dynamic execution tasks, modeled on games like Age of Empires II or StarCraft II. This will become a standard evaluation metric.
2. Within 24 months, the 'pure LLM' approach for autonomous agents will be largely abandoned in favor of hybrid architectures that combine LLMs with reinforcement learning or symbolic planning.
3. Within 36 months, a startup that successfully builds a causal reasoning engine (not just a larger LLM) will achieve a valuation exceeding $10 billion, as it will be seen as the next frontier.
4. Immediate consequence: Expect a wave of skepticism from enterprise buyers, who will demand proof of execution capability before deploying LLMs in high-stakes environments. The 'AI winter' for agent startups may begin sooner than expected.
What to watch: The next generation of models from DeepMind (which has RL expertise) and any startup that explicitly addresses the causal reasoning gap. Also, watch for the release of 'GameBench' or similar dynamic evaluation suites.