Summary
Similar to how AlphaGo used reinforcement learning to reach superhuman levels of Go, Generative AI is at a point where pure reinforcement learning is leading to superhuman levels of capabilities.
Once a model reaches a certain intelligence level, it can bootstrap itself to perform increasingly complex tasks due to pure reinforcement learning and high-quality synthetic data generation.
Palantir is perfectly placed at the center of this AI revolution, given its growing dominance in the operational AI application layer.
Unlike companies involved in foundation model training and research, Palantir gains all the upside of rapidly improving AI without the downsides of pouring billions of dollars into foundation model development.
Palantir still faces many competitive and reputational risks given its deployment of powerful AI systems in highly lucrative and security-sensitive sectors.
A New AI Era
There is something magical happening in AI right now. For readers to understand why, I will give a very brief history lesson on AlphaGo and how it relates to the current moment in AI. Then, I will talk about why Palantir (NASDAQ:PLTR) is uniquely well-positioned to benefit from this current AI environment.
In the famous Go match between AI AlphaGo and Go legend Lee Sedol, AlphaGo famously made a very strange move on "move 37". In fact, the move looked outright alien and nonsensical at the time but proved to be brilliant in hindsight. Such a move was only possible as a result of AlphaGo reinforcement learning (RL) through self-play. Basically, AlphaGo's neural net continually rewired itself based on rewards or punishments from win/loss outcomes of self-play during its training. AlphaGo was able to come up with never-before-seen strategies using this approach and eventually beat Sedol.
Here is the most astonishing part. A later version of AlphaGo called AlphaGo Zero was actually able to crush the older version of AlphaGo that beat Sedol. You see, the older version of AlphaGo that beat Sedol also used imitation learning from human play as a scaffold in combination with reinforcement learning from self-play. However, the later version, AlphaGo Zero used pure reinforcement learning from self-play without augmenting itself with human knowledge.
Why was this pure reinforcement learning approach superior? It is because humans actually proved to be a hindrance when it came to achieving Go superintelligence. This makes sense as humans have their own biases and limitations. It turns out, achieving optimal Go play works better when humans are taken out of the loop. Top AI researchers like Andrej Karpathy have consistently stated that reinforcement learning without human feedback is key to the type of recursive self-improvement seen in AlphaGo Zero. After all, humans themselves are limited in intelligence and very expensive.
Now you might be asking, how does this relate to the current moment in AI? It is simple. Through innovations like chain-of-thought and the availability of generative AI models that are now "intelligent" enough to bootstrap the process of reinforcement learning, we have finally started to crack the code on how to implement pure reinforcement learning for generative AI models. This explains the sudden explosion of superior "reasoning" models we see from OpenAI's "o" series and DeepSeek-R1. In fact, one of the reasons why DeekSeep-R1 was able to catch up to o1 so quickly was its innovative use of pure reinforcement learning in the domain of generative AI.
Google DeepMind
Generative AI Equivalent of AlphaGo's Self-Play is Emerging
A key driver of the recent spike in AI capabilities is the “chain-of-thought” revolution. Basically, a reasoning model works through intermediate "reasoning" steps before arriving at a final conclusion rather than the single forward pass method of traditional models. Early versions relied on the simple trick of prompting models to “think step by step”, but new models go further. OpenAI’s o1 and o3 Series, along with the open-source DeepSeek-R1, are trained to improve reasoning through reinforcement learning.
This is where the analogy between AlphaGo and reasoning models really comes into focus. Generative AI models have started to find their own equivalent of "self-play" using reinforcement learning. Let me explain. By using reinforcement learning on functionally verifiable tasks, i.e. where results are easily checked against a correct answer, these RL-trained AI systems can outmatch earlier large language models. A coding problem either passes test cases or fails. A math problem has a definite solution. Whereas AlphaGo played itself during the reinforcement learning process, these "reasoning" models play against verifiable tasks. Both have the same outcome of giving the model useful information to improve upon itself. For AlphaGo, it is the win/loss data from self-play, and for reasoning models, it is the true/false data from functionally verifiable tasks. For instance, non-subjective questions, pass/fail on coding tasks, did the model get the correct answer for the complex math problem, etc.
There is an additional aspect where the process of improving models gets accelerated even further. Reasoning models can also use their own "good" chain-of-thought outputs to create “synthetic” training data to input as the next model iteration's pre-training data. OpenAI is likely already compiling "o3" good chain-of-thought data output as pretraining data for "o4". This blows past the AI data wall as these models can output a huge amount of chain-of-thought reasoning data.
The result is a new generation of AI that not only answers questions more accurately, but can debug and self-correct on the fly. Essentially, we have started to figure out ways to get past the two major limitations to superhuman artificial intelligence. The first being how to effectively use pure RL to train the models, and the second being able to blow past the data wall by allowing the models to generate data themselves via high-quality synthetic chain-of-thought reasoning data that could be fed into even larger models.
arc-agi benchmark
A flood of new reasoning models like o3 and DeepSeek-R1 have absolute shattered benchmarks. It turns out the much-awaited AI wall that many suspected GPT-4o was pushing against never really existed. (arcprize)
Infinite Tasks for Reinforcement Learning and an Intelligence Explosion
Functionally verifiable tasks, i.e. non-subjective tasks, extend far beyond solving math or coding challenges. Dylan Patel of SemiAnalysis recently brought up the fascinating point about how the internet effectively holds an infinite array of “functionally verifiable” tasks in which a reasoning model can use RL to make itself "smarter". For instance, an AI reasoning agent can sign up for a social media site and have a verifiable goal of hitting a certain number of followers.
Each time the model succeeds in whatever task it gives to itself, the RL framework can give the model a positive reward, or inversely a negative reward if it fails. At a very high level, the model will basically change its weights so that it is more likely to do a successful action in the future, or less likely to do an unsuccessful action in the future. Human's essentially do reinforcement learning all the time.
We can analogize a model's "weights" as the connection strength between neurons. If a toddler were to put its hand on a hot stove, there is an "RL" algorithm working so that the synaptic connections of neurons (weights) in the toddler's brain strengthen to avoid such actions in the future. Maybe the neural connections between "fear" neurons, "bright color detection" neurons, and "physical stimuli" neurons are strengthened, thus causing the baby to be fearful of touching glowing objects in the future. I am probably butchering the neuroscience part of this explanation, but I believe most will understand my point. In fact, neural nets have largely been inspired by the human brain architecture since that is the only real intelligent "model" that we currently know of.
These models' capabilities could improve exponentially as they improve themselves through RL on the near infinite open-ended verifiable tasks on the internet. Some researchers call this scenario a “hard takeoff,” where an AI’s capacity to invent and test solutions feeds back into the next wave of improvements. We have seen a small sampling of this in DeepSeek-R1, which soared from near-random performance on advanced math tests to matching state-of-the-art models. This was basically all through repeated trial and error via pure RL. It takes little imagination to see how such methods, scaled to the entire internet, could produce astonishing leaps in capability.
We are already witnessing the beginning of how agentic models like OpenAI's recently released "Deep Research" will be able to perform a near infinite array of "functionally verifiable" tasks in the near-limitless playground of the internet. When they can move out of sandboxed environments into real-world and complex environments like the internet, the real magic of RL can truly begin. Once these models get smart enough, they could even start to improve themselves, which is the point where an intelligence explosion could go into overdrive. In fact, we are already seeing signs of such self-improvement. DeepSeek-R1 recently got a 2x speed improvement using code that was 99% written by DeepSeek-R1 itself. While humans were still in the loop in this process, the fact that this DeepSeek-R1 improvement came from mostly its own generated code is a step in the direction towards recursive self-improvement.
This could also be taken to the world of robotics and simulation, where robotic agents can use RL to improve from the near-infinite tasks in simulated worlds. The knowledge gained from simulated robotic agents can then be transferred to real physical robots. In fact, Nvidia is already starting to play with this idea in what is being called "sim2real". When RL can be automated to this degree for foundation models, they could experience an AlphaGo move 37 moment far sooner than most anticipate. That is, it becomes clear that these models have moved far beyond general human intelligence into almost an alien general super intelligence.
OpenAI "Deep Research"
OpenAI's recently released "Deep Research" agent can now autonomously access the web and do research. This is just the first step of truly capable reasoning agents entering the near-limitless internet playground. (OpenAI)
How Palantir Sits at the Intersection
Palantir Technologies is positioned as a prime beneficiary of these recent AI innovations. Originally known for its platforms in national security and enterprise data integration, Palantir is now embedding chain-of-thought AI throughout its product suite. The company’s core mission of turning raw data into effective decisions fits perfectly with advanced reasoning models. Palantir can provide “copilot” AIs that break down a problem in multiple stages, consult an organization’s data, test possible outcomes, and present a final recommendation that is transparent.
Palantir’s strengths shine when high-stakes environments demand clear accountability. A purely generative AI might produce compelling text, but might also be prone to errors or lack detail on how it arrived at a conclusion. By contrast, a chain-of-thought AI can lay out a step-by-step analysis, which is especially important in defense, healthcare, or manufacturing. Clients want to see the logic behind decisions and a reliable means to catch mistakes. A reasoning AI fits neatly into Palantir’s framework of ingesting/integrating data from multiple sources and giving decision-makers tools they can trust.
Palantir AIP
Palantir is one of the few companies operating at the application layer to have wholly embraced AI. It has the luxury of picking and choosing between the best models to anchor its products on. (Palantir)
Financial Traction and Growth Outlook
Recent quarterly results is further evidence of Palantir’s strong momentum. The company reported Q4 revenue of $828 million, up 36% year-over-year, far surpassing analysts’ estimates. Its U.S. commercial and government revenue grew by 54% and 45% respectively. What's even more impressive is the fact that Palantir grew this fast while achieving an adjusted operating margin of 45%.
Palantir is expecting roughly 31% annual revenue growth in 2025, predicting a total near $3.75 billion. Government contracts will remain a major component as the company is still regularly securing deals in excess of $100 million to deploy AI-driven analytics. On the commercial side, deals with industries like telecom, banking, and logistics are growing faster than ever. This can partly be attributed to how easily state-of-the-art AI fits into Palantir’s existing data pipelines.
Investors have taken notice. Palantir’s stock price soared over 300% in 2024. Some analysts now compare it favorably to premier “AI platform” companies, pointing to its operational results rather than purely theoretical or consumer-level AI. While the share price could see volatility given its high multiples, Palantir’s business resonates with a market hungry for real-world AI deployments.
Risks and Competition
Palantir's high valuation rests on consistent above-market growth, which could face a sharp decline should commercial or government demand slow. Competition is also fierce. Tech giants like Microsoft (MSFT)(MSFT:CA) and Google (GOOG)(GOOG:CA)(GOOGL) and well-funded startups all want to embed advanced AI into businesses.
Another concern relates to the advanced reasoning of agents themselves. When an AI agent can revise its reasoning, it becomes far harder to supervise. Palantir’s stance is that transparent logic chains and strict human oversight remain key. However, a reasoning agent that is generating a huge amount of output in chain-of-thought would be incredibly hard to monitor.
Why Palantir is a Big Winner in this AI Moment
If chain-of-thought and reinforcement learning continue to accelerate AI’s capabilities, software platforms that can deploy these models securely and at scale will have a huge competitive advantage. Palantir has spent years building precisely that kind of infrastructure. The company is able to deploy AI behind firewalls, manage sensitive data, and continually update models. Its government business provides large, stable contracts that fund ongoing R&D, while the commercial side gives it a path to massive growth.
Palantir’s emphasis on delivering decisions and not just analytics aligns perfectly with the strengths of next-generation AI. Companies and agencies often need to see exactly how or why an AI recommends a specific plan. Chain-of-thought reasoning can make that logic traceable, especially as Palantir offers a powerful data pipeline for the AI to consult, interpret, and refine. This relationship suggests Palantir will remain at the forefront of enterprise-level AI.
One final point to note is that a competitive environment where AI models are exploding in capabilities is only a positive for Palantir given its uniquely strong place on the AI application layer. The same could not be said about the companies actually building the AI given how quickly these models commoditize and technological obsolescence occurs given the sheer speed at which newer models are improving.
Palantir's AIP in action
Palantir's business of operational AI makes it perfectly suited for the rise of highly capable reasoning models. (Palantir)
A Forward-Looking Bet on Operational AI
The rise of chain-of-thought AI and large-scale reinforcement learning is about more than incremental gains in model accuracy. It signals a shift toward AI that can decompose problems, search for evidence, correct its missteps, and converge on solutions with minimal human input. Powered by the near-infinite tasks on the internet, we may see an unprecedented leap in what many leading AI figures are viewing as a “hard takeoff” in AI capability.
Palantir represents a compelling investment in this AI environment. Palantir’s end-to-end platform could become an industry backbone, allowing algorithms to have real-world impact. While Palantir looks incredibly pricey at its current valuation of $236 billion and P/E ratio of 525, the company is still a buy due to its incredible positioning in a game-changing industry.
