Instinct vs. Adaptability: The Future of AI Learning

Overview of AI Learning Challenges

The blog compares a parrot's instinct-driven survival behavior to current AI training. It highlights the limitations of relying solely on historical data. Large Language Models (LLMs) consume massive energy because they start without innate "instinct," unlike humans. The future lies in adaptive learning algorithms that, like biological brains, continuously integrate new information without full retraining. This approach reduces energy use, mitigates bias, and keeps AI up-to-date. Challenges include catastrophic forgetting, overfitting, and scalability, but early examples like Perplexity and Grok show progress. The goal is a balanced "synthetic instinct" that enables AI to learn, adapt, and perform effectively in dynamic environments.

The Limits of Instinct

Our pet parrot, Mitthoo, has never lived in the wild; he would most likely die. Mitthoo doesn't know how to look for food, fly great distances without getting lost, or protect himself from potential predators. That last point is interesting because he still instinctually identifies potential predators. If he sees a bird bigger than him, he starts screaming even though the bird is far away and Mitthoo is indoors. However, when he sees a dog or a cat, he isn't scared at all. This is because he instinctually knows he can just fly higher and farther away. But a bigger bird can also do the same, so he's more scared of them.

In his protected suburban reality, however, he should be doing the opposite. Mitthoo lives inside a home and is surrounded by human neighbors who have dogs and cats that can run by or even into our house. Therefore, he's more likely to run into dogs and cats than big birds, but his instinct tells him otherwise. This is because his instinct, ingrained in his biological neural network, was forged over millions of years of his ancestors being hunted and surviving in the jungles of South America. This instinct is trained on volumes of past data that, though useful, wasn't trained for his current environment. This demonstrates the limitations of only training on existing historical data, as it lacks immediate adaptability in new contexts. This has major implications for how we train LLMs today and in the future.

Humans Use Less Energy to Learn than LLMs

LLMs are currently trained on large amounts of existing human data in massive training centers. Every day, AI companies announce larger1 and larger2 AI training centers with an ever-increasing number of GPUs and power consumption3. In the theme of this blog, we asked an LLM how much energy a human consumes from birth to about age 25 (when the prefrontal cortex is fully developed). It replied that humans consume roughly 70 gigajoules (GJ), or 19,400 kilowatt-hours (kWh). OpenAI used roughly 1,300 MWh4 of energy to train GPT-3, and it is only higher for frontier models in 2025. Why did OpenAI need a massive training center that consumed 67 times as much energy as a human does in 25 years to create an LLM that is arguably worse than the average 25-year-old?

This is because a human and an LLM are starting from fundamentally different points. A human baby is born preloaded with a neural network full of information and knowledge; we call this instinct. LLMs start with randomized weights (i.e., a complete scratch)—tabula rasa as John Locke once said. The energy consumption behind LLM training is a result of humans trying to expedite in a few months what took nature and evolution billions of years to create: preloaded, foundational knowledge, or "Synthetic Instinct".

Once an LLM has "instinct," we then fine-tune and adapt it to the current world, the same way we teach and educate humans. Fine-tuning still takes around 100 MWh, an order of magnitude less than training but still significantly more than a human. This is because the brain is the most energy-efficient learning machine we know of today. We are curious if the energy consumed to train a frontier AI model totals the same amount of energy it took nature to build instinct into human brains over hundreds of millions of years of evolution.

AI, Learning from Nature

Learning to build advanced LLMs and multimodal models has been a necessary step in our understanding of how to build artificial intelligence. This experience has taught humans how to construct the bottom threshold for a basic synthetic instinct needed to make a generalizable neural network, surpassing the specialized neural networks from the 2010s. However, if we are to continue to take inspiration from nature, it will become painfully obvious that our current methods to produce frontier LLMs and multimodal models are not the future, as they lack one important trait: Adaptation.

Instinct isn't enough; animals must also adapt. Adaptation works by continuously learning new information5 and integrating it with existing knowledge. That last word is very important: Continuously. Currently, when new information is embedded into an LLM, you must perform the complete fine-tuning process again. If you want to embed significant amounts of information, you must start from scratch and repeat the entire pre-training and training process again, which we've seen is an extremely energy- and resource-inefficient task. If biological creatures had to repeat that same process every time they learned new information, life would never exist. The future of artificial intelligence is continually learning algorithms (or adaptive learning algorithms), algorithms that allow neural networks to learn new information while preserving and building on top of previously learned information, dynamically and continuously. This models the neural plasticity biological brains have that allows humans and animals to learn in dynamic and ever-changing contexts and environments.

The Role of Effective Reasoning Engines

A significant nuance for building the next generation of real-world adaptive AIs is the development of effective reasoning engines. This trend, exemplified by models like DeepSeek-R1, has moved towards utilizing reinforcement learning to improve reasoning without relying solely on massive, labeled datasets. This shift has, in turn, led to a greater focus on smaller language models (SLMs) that can achieve strong reasoning capabilities, challenging the assumption that only large models can reason. By using methods like reinforcement learning, SLMs can be trained to reason through complex problems, making advanced AI more accessible and efficient.

The Benefits of Adaptive Learning

Continually learning algorithms will, by nature, be able to overcome challenges that even frontier LLMs suffer from today: high energy consumption, a lack of real-time information, and bias in training data. As stated earlier, training—and even fine-tuning—requires significant amounts of energy, thus making it costly and only accessible to big companies with lots of resources. However, adaptive learning models will be able to learn new information and embed it directly into their weights without needing to undergo complete retraining cycles. This will allow any company and organization to create new models without demanding massive amounts of energy consumption and computational resources, reducing upfront financial investment and making AI model creation more accessible.

LLMs also suffer from knowledge cutoff dates, handicapping their ability to answer questions accurately in ever-changing contexts (e.g., news, finance, research). As training currently involves batches of data, the company or organization must commit to a specific date range of high-quality data before its next LLM training cycle. By the time the training is complete, the LLM, no matter how frontier, will always be inherently out of date. Adaptive learning models, however, will thrive in ever-changing environments as they will be able to continuously adapt and integrate information, always being able to provide the most up-to-date information.

Finally, adaptive learning will help automate an issue that has plagued machine learning since the beginning: bias.

Bias is inherent due to many factors, including the availability of data, which data is included in the training set, and subconscious human biases being programmed into machine learning source code itself. Once this bias is discovered, the designers must "fix" the training data and redo the training cycle over again to hopefully reduce it. Finding quality data that is relatively unbiased is such an extremely difficult and time/resource-consuming problem that entire billion-dollar startups8 have been created just to solve this. However, with adaptive learning algorithms, once the bias is discovered, it will be much easier to provide new data to it and course-correct early before the bias spirals and propagates.

The Future is Almost Here

Adaptive learning algorithms aren't easy to develop. Current problems faced with developing these algorithms include catastrophic forgetting9 (where the AI forgets old information when it learns new information), overfitting10 to either old data or new data, scalability issues11 (as computational resources increase as the model learns more information), and resource constraints12 (as continual training requires efficient computing and memory management). That being said, we are already starting to see early implementations of adaptive learning models. Though not completely adaptive learning models, both Perplexity and Grok (XAI) have already been able to simulate similar adaptive behaviors in very distinct ways.

Perplexity implements13 a Retrieval Augmented Generation14 architecture by crawling the web and retrieving real-time information, and then providing that information to an LLM to generate a response, leveraging both the crawler's ability to find up-to-date data and the LLM's ability to generate a cohesive, human-understandable response.

Grok takes this one step further and performs15 reinforcement-based learning in production, using a combination of human feedback, self-play, synthetic data generation, and automated reasoning routines with human oversight. This allows Grok to be "smarter than Grok a few days ago" (Elon Musk).

Conclusion

Though our parrot cannot immediately model predator threat levels accurately between dogs, cats, and birds stuck outside, give him enough real-life experience, and he too will learn to adapt and build on top of his existing intuition. This is the future of AI.

Next Step or Call to Action

For businesses and technical teams looking to leverage AI, the shift toward continually learning algorithms presents a significant opportunity. Consider investing in research and development that focuses on adaptive AI systems capable of continuous learning and integrating new data without extensive retraining. Explore partnerships with educational institutions like the Applied AI Institute and startups specializing in neural plasticity and dynamic learning models. Understanding and implementing this paradigm shift will be crucial for building AI systems that can truly thrive and provide lasting value in our rapidly changing world.