Why Studying Monkeys Could Revolutionize the Future of AI

Date published: August 21, 2025

TL;DR

The next major leap in artificial intelligence (AI) won’t come from more microchips or bigger data centers—it will come from better understanding how brains work, especially those of our closest evolutionary relatives: monkeys. While today’s AI systems are energy-hungry and fundamentally limited, studying primate neuroscience may unlock the blueprints for truly smart, highly efficient thinking machines.

The Real Limits of Modern AI

If you listen to the headlines, it seems we’re in the midst of an AI gold rush—with companies racing to build ever-bigger models, invest billions, and promise world-changing results. But beneath the hype, today’s artificial intelligence is running into bottlenecks that put the future of this technology in question.

• Enormous computational requirements: The biggest AI models need supercomputers, special chips (GPUs/TPUs), huge warehouses of humming servers, and constant streams of data.
• Unsustainable energy use: Training a single large language model can consume as much electricity as a small town. Keeping it running requires power flows that rival major cities.
• Lack of real understanding: Even the most sophisticated neural networks can’t form true abstract concepts, adapt flexibly to new contexts, or generalize from sparse information the way a human—or even a toddler—effortlessly can.

In short: Modern AI is large, but it’s not clever.

Machine Intelligence vs. Biological Intelligence: The Efficiency Gap

Let’s put things in perspective. The human brain operates on roughly 20 watts—less than most household lightbulbs. In comparison, the latest AI infrastructure can drain entire power plants just to simulate a fraction of human-like reasoning.

• To reach the computational power of one human brain, an AI system could need as much energy as the city of Dallas (1.3 million people).
• Scaling this to millions—or billions—of intelligent agents is simply unsustainable.

Meanwhile, nature’s solution (the brain) is:

• Adaptable—solves problems it’s never seen before.
• Social—understands minds and emotions, not just data.
• Creative—imagines, predicts, and invents beyond patterns in data.
• Efficient—learns swiftly and with minimal energy.

Toddlers vs. Transformers: The Learning Challenge

Consider a child learning language and logic. With a handful of examples and social cues, a child rapidly develops abstract ideas, recognizes patterns, and adapts to a constantly changing environment.

Current AI, in contrast, stumbles with unseen inputs and needs mountains of data. Why the discrepancy? The answer lies not in the code, but in biology: The brain’s architecture and the way it processes and encodes information are fundamentally different from today’s AI models.

Nature’s Blueprint: Why Neuroscience—and Monkeys—Matter

The breakthrough in next-generation AI won’t be the “next bigger chip,” but a deep understanding of how real brains orchestrate intelligence. And for this, monkeys and apes provide a unique window.

• Shared evolutionary lineage: Primates share much of our brain structure and function, including the circuits that process vision, learning, and social behavior.
• Experimental access: Scientists can study monkey brains with a precision and depth simply not possible in human subjects.
• Proven foundation: The very first artificial neural networks were inspired by primate brain research, especially vision studies.

By investing in primate neuroscience, we can begin to decode the precise circuit architectures and neural mechanisms that give rise to flexible, creative, and robust intelligence—the ultimate goal of AI.

Learning from Monkeys: From Research to Real AI

One lesson from the past: the U.S. Brain Initiative (since 2013) yielded technologies that revolutionized how scientists observe and manipulate brain activity. Yet, much of this foundational knowledge remains untapped by the mainstream AI field.

Meanwhile, countries like China are rapidly expanding national support for primate neuroscience and “brain-inspired AI,” building breeding centers and research infrastructure at unprecedented scale.

• China now has more than 40 primate centers—nearly triple the number in the U.S.
• Its state-backed initiative aims to merge brain research with AI and push for strategic leadership in smart technologies.

The U.S. Is Falling Behind in Brain-Inspired AI

Despite leadership in foundational research, monkey studies in the U.S. are increasingly underfunded, politically pressured, and hampered by regulatory uncertainty. There’s a risk that critical expertise, infrastructure, and talent could shift overseas, ceding the future of neuroscience-guided AI to others.

The stakes are high—not just for scientific discovery but for national security, technological independence, and economic leadership. Whoever unlocks the secrets of biological intelligence could define the arc of the coming century, much like the Silicon Revolution or the Manhattan Project shaped the last.

Current AI Hype: The Coming Plateau

Political and corporate leaders frequently tout AI superiority as a core national priority. But without a shift in research priorities, today’s data-hungry, energy-burning algorithms are unlikely to achieve the kind of general, human-level intelligence promised by visionaries.

• The only scalable, efficient form of general intelligence we know comes from biological brains.
• Ignoring the lessons of nature risks wasting trillions in capital and coming up short in the race to machine intelligence.

The Road Forward: Towards “Brain-Inspired” Thinking Machines

Where should the next generation of funding and innovation go?

• Step 1: Renew federal and private investment in primate neuroscience, experimental brain mapping, and integrative brain–AI research.
• Step 2: Foster interdisciplinary “neuro-AI” programs that bridge computational neuroscience, machine learning, biology, and engineering.
• Step 3: Translate insights from monkey brain structure and function into new AI architectures—not just “bigger networks,” but networks built on biological principles (e.g., learning from sparse data, self-organization, social reasoning, energetic frugality).
• Step 4: Promote ethical, responsible frameworks for animal research—ensuring high scientific value, humane treatment, and societal benefit.

Bottom line: To create real AI, we must first understand the only genuinely intelligent “machines” we know—our own brains and those of our primate cousins.

The True Path to General Intelligence

It’s time to acknowledge that software and hardware alone won’t close the “cognitive gap.” By integrating neuroscience—especially from monkeys and apes—into AI development, we may finally build thinking machines that match or even surpass our own brains but with a tiny fraction of the energy and resources.

For researchers, investors, and policymakers, the question isn’t whether to embrace the brain’s lessons, but how quickly we can do it before the next AI revolution passes us by.

Key Takeaways

• AI’s biggest challenge is not data or chips, but matching the efficiency and adaptability of brains—especially primate brains.
• Studying monkeys and neuroscience offers the most promising blueprint for real machine intelligence.
• The U.S. risks falling behind as other countries pour resources into merging neuroscience and AI development.
• The next big leap will be “brain-inspired” machines—not bigger data centers, but smarter design.

Frequently Asked Questions

1. Why focus on monkeys instead of just studying the human brain?

Answer:
Monkeys share much of our brain architecture and cognitive abilities, allowing scientists to conduct detailed, ethically governed experiments that are not possible with humans. These studies reveal the fundamental neural mechanisms of intelligence—the “source code” for cognition—that can guide next-gen AI.

2. Isn’t today’s AI already inspired by brains?

Answer:
Early neural networks took inspiration from biological brains, but most current AI prioritizes scale, data, and engineering shortcuts over biological plausibility. As a result, today’s AI doesn’t learn, adapt, or generalize as efficiently as real brains, especially in novel or sparse-data situations.

3. How will primate neuroscience actually make AI better?

Answer:
By uncovering how primate brains process information, learn, reason, and interact socially—all with minimal energy and data—scientists can design AI systems that are more robust, flexible, and efficient. This could unlock true general intelligence and enable machines to “think” more like us.

Conclusion: Unlocking AI’s True Potential Will Take Brains—Not Just Bytes

The future of AI will not be won in the next chip factory or server farm, but in the minds of neuroscientists, biologists, and machine learning engineers working together. As our computational systems reach their limits, it’s time to return to nature’s most extraordinary achievement—the brain—for inspiration. In this pursuit, monkeys may prove to be the guides that lead us to machines that truly learn, reason, and understand.

About the author: Cory Miller is a professor of psychology at the University of California, San Diego.

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