The Algorithm of Life

Why AI Is Changing the Exploration-Exploitation Equation

Watch an ant colony searching for food and you will see a surprisingly sophisticated decision system at work. Some ants leave the established path and search in different directions. Most of those explorations lead nowhere, but occasionally a scout discovers a valuable food source and returns to the colony, laying down a chemical trail. Other ants follow, and if the source proves worthwhile, they reinforce the trail with more pheromones. The colony gradually shifts from searching to concentrating its effort on what works. When the food disappears, the pheromone fades and the search begins again.

Honey bees use a different mechanism, but the underlying logic is similar. Scout bees search independently for nectar and pollen. When a scout finds a promising source, she returns to the hive and performs the waggle dance, communicating information about its direction and distance. More profitable sources attract more recruitment, so the colony directs more of its energy toward the better opportunities. Exploration produces information. Exploitation turns that information into results.

I have come to believe that this tension between exploration and exploitation is one of the basic algorithms of life. We encounter it in nature, business, careers, investing, and many of the decisions that shape who we become. And now AI is changing the equation by reducing the cost of exploration.

Exploration Before Exploitation

Brian Christian and Tom Griffiths explore this idea in their book Algorithms to Live By. One of their best-known examples is the “secretary problem,” a classic problem in optimal stopping. Imagine that you have 100 candidates for a job. You interview them sequentially, and once you reject someone, you cannot go back. How long should you keep looking before making a decision?

Under the classic assumptions of the problem, mathematics gives us a surprisingly precise answer: about 37 percent. You use the first 37 percent of the process to explore and establish a baseline for what good looks like. After that, you select the first candidate who is better than everyone you have seen so far.

The 37 percent figure is best understood as a model rather than a literal formula for life. The principle is what matters. Commit too early and you may settle before discovering something better. Explore forever and you may never capture the value of what you have learned. Good decisions require both modes, and wisdom often lies in knowing when to shift from one to the other.

The Most Important Exploration of My Life

Looking back, some of the most consequential decisions in my life began as explorations. The most important happened when I was a young man in Brazil. I left my home country by myself with little more than a small suitcase, a modest amount of money, and an academic scholarship. I had no way of knowing where that decision would lead. I saw an opportunity to continue my education, experience another country, and discover what might be possible.

That exploration changed my life. It eventually led to building a family and a career in technology spanning Silicon Valley startups, Oracle, Zebra Technologies, Flexera, entrepreneurship, writing, teaching, and advisory work. None of that was visible when I packed the suitcase. The biggest returns from exploration are often impossible to calculate beforehand because they come from opportunities we have not yet encountered.

My corporate career became, in many ways, the exploitation side of the equation. It gave me the opportunity to deepen my expertise, build relationships, lead larger organizations, and create financial stability. At the same time, I kept exploring through business ideas, products, writing projects, investments, and educational ventures. Many never became large businesses, but each produced information that influenced what I did next.

When Exploration Gets Cheaper

For most of my career, exploration was expensive. Even a few years ago, testing a serious software business idea might have required a product manager, developers, designers, infrastructure, outside capital, and many months before you could put something meaningful in front of a customer. The size of the investment forced you to make relatively few bets and raised the cost of being wrong.

Nick Roseth makes a related point in an essay appropriately titled “Previously Unthinkable.” As AI reduces the time, specialized labor, and capital required to create something useful, the range of ideas worth attempting expands. Projects that once looked too expensive, too slow, or too difficult can become reasonable experiments. That changes more than productivity. It changes what we are willing to try.

I recently discussed this shift with Dmitry Shapiro, CEO of Remy, the platform I used to build Intelleus.ai. Software development is moving toward a model in which people spend more time defining the problem, describing the desired outcome, setting constraints, and reviewing results while AI agents handle more of the implementation. Coding, testing, documentation, and other technical work can increasingly be compressed into a much shorter cycle.

I experienced this firsthand with Intelleus.ai. I wanted to test an idea that had been occupying my thinking for some time: what if we could capture the knowledge scattered across our documents, notes, bookmarks, videos, ideas, and experiences, organize it into a trusted body of knowledge, and make that context available to AI? I was able to turn that idea into a fully working application in roughly two to three months with a relatively small personal investment. A few years ago, I believe the same experiment would have required 12 to 18 months, a team of developers, and significant capital.

Had the exploration required the old level of investment, I might never have attempted it. AI changed the threshold at which the project became worth exploring.

The Equation Is Tilting

If the cost of exploration falls, the rational amount of exploration should increase. Individuals can test more ideas before committing. Entrepreneurs can build prototypes before raising large amounts of capital. Companies can experiment with more workflows and business models. Researchers can investigate more hypotheses. Creative people can attempt work that once required capabilities they did not possess.

Exploitation still matters. Businesses have to scale what works. Expertise has to deepen. Relationships take time. Compounding requires consistency. But the penalty for trying something new is falling, and that gives us room to run more small experiments before making large commitments.

I find this particularly encouraging for people who have accumulated decades of experience. AI gives us a way to combine what we already know with experiments we might previously have considered impractical. You increasingly can explore first, learn something, and then decide how much time and capital an idea deserves.

Exploration Still Needs a Compass

Cheaper exploration creates another challenge. If AI is helping us generate ideas, evaluate alternatives, build products, and make decisions, what is guiding the AI? Generic models begin with knowledge derived largely from the same public information available to everyone else. Without enough context from us, they naturally tend toward conventional answers.

I recently discussed this with futurist Kevin Benedict. As AI becomes a more active collaborator, I believe it needs access to more than our files. It needs context about our knowledge, beliefs, interests, experiences, priorities, and judgment. Those things form the compass that helps determine which possibilities are worth pursuing and which ones are inconsistent with who we are or what we are trying to accomplish.

That is where governance becomes important. We need to decide what information AI should trust, which sources are authoritative, what values should shape its recommendations, when human review is required, and who remains accountable for the decisions that follow. The easier it becomes to act, the more important it becomes to be deliberate about the context behind the action.

Nature has been balancing exploration and exploitation for millions of years. We are now introducing a new variable into that ancient equation: the cost of exploring is falling rapidly. That gives us the ability to test more possibilities, learn faster, and discover opportunities that might otherwise remain invisible. Some of the most consequential choices in my own life began as uncertain experiments. AI gives more of us the capacity to make those experiments smaller, faster, and more affordable, while keeping human judgment at the center of deciding where they should lead.

Sources and Further Reading

1. Lanan, Michele. “Spatiotemporal resource distribution and foraging strategies of ants (Hymenoptera: Formicidae).” Myrmecological News 20 (2014): 53–70. https://pmc.ncbi.nlm.nih.gov/articles/PMC4267257/

2. Palmer, Joseph, et al. “Foraging distance distributions reveal how honeybee waggle dance recruitment varies with landscape.” Communications Biology 7 (2024): 1306. https://www.nature.com/articles/s42003-024-06987-9

3. Riley, J. R., et al. “The flight paths of honeybees recruited by the waggle dance.” Nature 435 (2005): 205–207. https://www.nature.com/articles/nature03526

4. Christian, Brian, and Tom Griffiths. Algorithms to Live By: The Computer Science of Human Decisions. Henry Holt and Company, 2016. https://algorithmstoliveby.com/

5. Roseth, Nick. “Previously Unthinkable.” Adaptive Intelligence, Substack. https://nickroseth.substack.com/p/previously-unthinkable

6. Espindola, David. Conversation with Dmitry Shapiro, CEO of Remy, on AI-assisted software development and building Intelleus.ai. YouTube. https://www.youtube.com/watch?v=2yfu_VGkEJ8

7. Espindola, David. Conversation with Kevin Benedict on personal AI, context, values, and governance. YouTube. https://www.youtube.com/watch?v=-vzw7pRlKLw