Innovation is Thinking in Bets

The technologies that change our lives rarely begin with certainty. They begin with a decision to move forward, before the answer is known.

There is something slightly misleading about the way we talk about innovation. We usually talk about it after it works.

Tesla made the bet on electric cars. NVIDIA saw the future of accelerated computing. The iPhone changed how we interact with technology.

Looking backwards, innovation has a strange tendency to appear uncertain.

Tesla itself warned investors during its early Model S years that its limited operating history made its future difficult to evaluate and that production and cost improvements were hard to predict.

NVIDIA spent years investing in CUDA when the commercial return was far from obvious. Jensen Huang later described years of poor performance and skeptical shareholders before GPU computing became fundamental to modern AI. 

These companies weren’t guaranteed a seat at the winners table. They were making bets on themselves. And perhaps that is a more useful way to think about innovation.

Innovation begins where certainty ends.

I (Tomas Smagarys) recently finished Annie Duke’s bestseller Thinking in Bets, and I have to say, the book genuinely impressed me. Many of its ideas resonated with my own experiences in business and innovation development journey – especially the way we make decisions when the outcome is still uncertain. One idea in particular stayed with me:

A good decision can have a bad outcome. A bad decision can have a good outcome.

The outcome alone doesn’t tell us whether the original decision was intelligent. That sounds simple. In practice – it changes a lot.

Innovation means making decisions while important pieces of information are still missing.

Will the technology work at scale?

Will the people understand it?

Will the innovation exist five years from now?

Nobody really knows.

If we waited until every answer became certain, very little genuine innovation would ever happen. Innovation doesn’t mean pretending to know the future. It means having enough evidence to believe a future is possible, and enough conviction to start building before everybody agrees. NVIDIA didn’t have all the answers either and is one of the clearest modern examples.

Today its relationship with artificial intelligence can look almost inevitable. NVIDIA introduced CUDA in 2006, opening GPU parallel computing beyond graphics to science and research. Years later, those capabilities helped power the deep-learning breakthrough represented by AlexNet. But there were years between the bet and the win. Huang has spoken openly about the criticism surrounding the investment and about other NVIDIA decisions that simply failed. An early architecture developed for Sega put the company in serious difficulty. NVIDIA later entered mobile computing and then retreated when the technologies performed. 

Some bets worked. Some didn’t. Some took years before anybody could understand which category they belonged to. That’s the part of innovation we often forget.

A bad outcome isn’t always a bad bet.

We have experienced a version of this while developing BYOSOM. 

Our scientific work in genetics and genomics reached the point where BYOSOM GENYQ™ technology needed to become a physical skincare product. We selected a U.S. manufacturing partner and invested significant time and money into the development and it was a reasonable decision based on the information available that days. Almost a year later, it simply didn’t work. The samples weren’t close to what the scientific formulation required. More time and money could have been invested, and because we had already invested so much, continuing would have been the easiest decision to justify. But that was the moment when we changed the bet.

Scientific assessment from our professor indicated that the problem wasn’t something another adjustment could solve. The manufacturing team simply didn’t have the specialized biochemistry expertise required to produce the complex formulations our genetics-based approach demanded. The required capability simply wasn’t there. Even with detailed instructions, the chemists were not equipped for this level of formulation, and the necessary knowledge base was insufficient to translate BYOSOM’s genetics-based formulation science and GENYQ™ technology into the formulations we had developed.

So, we changed the bet in just one day. We saw the risk becoming too high and decided to “fold”. We stopped before burning through more money and kept enough capital for the next bet – even though, at that moment, we had no idea what the next bet would be.

The manufacturing team simply didn’t have the specialized biochemistry expertise required to produce the complex formulations our genetics-based approach demanded.

Fortunately, in a very short time and almost accidentally, new developments in chemistry and biochemistry provided the missing piece we needed. We found a super-talented PhD team in chemistry and biochemistry, and just like that – we were back on track. 

Was losing the year good? Of course not.

Was losing the money good? Definitely not.

But allowing yesterday’s loss to determine today’s decision would have been worse. And that distinction may be one of the most important lessons in innovation.

The brain learns to bet.

There is another interesting thing that happens after betting on innovation for long enough. Decisions come faster. Not necessarily because we become smarter. And certainly not because we become capable of predicting the future.

The brain begins recognizing patterns.

A situation appears completely new on the surface, yet something underneath it feels familiar. A conversation, business proposition or technical problem triggers information accumulated from dozens of apparently unrelated experiences.

Sometimes the answer arrives before we can fully explain why.

We call it intuition.

Good intuition often comes from experience. That doesn’t mean acting impulsively. There is an enormous difference between speed built from experience and speed built from confidence. The important loop is:

Decide. Act. Observe. Learn. Update. Repeat.

The feedback matters as much as the decision. Make enough decisions without learning from them and we simply become experienced at being wrong. But make decisions, reality test them, examine the result and update honestly and decision-making itself starts becoming a professional skill.

Speed has value.

This creates an uncomfortable conclusion. Sometimes we need to think less and decide sooner. Not because analysis is bad. Good decisions need information. But there is a point where another week of thinking produces less value than another week in the field.

Innovation moves through feedback:

A scientific experiment provides information.

A failed formulation provides information.

A customer provides information.

Even a failure provides information.

Until we act, much of that information remains inaccessible. So speed isn’t valuable simply because things happen faster. Speed increases the number of opportunities reality has to teach us something. And over time, those learning cycles compound.

Then came AI

AI makes this idea even more interesting. Never before individuals had access to so much intelligence so quickly. We can interrogate assumptions, explore scientific literature, compare scenarios, challenge strategies and examine a decision from perspectives that previously might have required large teams and enormous amounts of time.

That should make our bets better. But they are still bets, not claims.

AI doesn’t know whether a technology being developed today will define an industry in 2035. Neither do we. It can increase the quality of information surrounding the decision. It can help us see what we missed. It can challenge our confidence. It can make us faster. But eventually someone still has to say:

Go!

And accept that the outcome remains uncertain.

That’s why I increasingly believe:

Better intelligence doesn’t eliminate the bet. It improves the quality of the bets we are able to make. Genetic science may be one of the biggest innovations of our generation.

This is where the idea becomes particularly relevant to BYOSOM.

We are building around a belief that beauty will increasingly move from generalized assumptions toward deeper biological understanding.

Do we know exactly how quickly that transformation will happen? – No.

Do we know exactly how personalized genetic skincare will look like ten years from now? – No.

Nobody does.

We have science. We have genetics and genomics. We have biotechnology. We have increasingly powerful AI. We have far more biological information than previous generations could access. And we have a hypothesis about what happens when these technologies begin working together.

That is our bet.

BYOSOM’s technology exists today. But the category around it is still developing. And I think it’s important to say that openly.

Innovation doesn’t mean pretending to know the future. It means having enough evidence to believe a future is possible and enough conviction to start building before everybody agrees.

Biotechnology gives us the opportunity to understand things about ourselves that previously weren’t easily accessible. Genetics cannot tell us our destiny, but it does provide very detailed insights into our strengths and weaknesses.

At BYOSOM, this is why we believe personalized skincare should begin with understanding rather than another product recommendation. Learn more about biology first. Then decide what is and isn’t best for the individual.

There is a difference between making a decision with the information vs without. Understanding improves our chances of seeing the desired result.

Innovation is not a straight line from idea to success. It is a sequence of bets.

Technology can improve the process. AI can improve speed. Experience can improve intuition. Science can improve the evidence. But none of this guarantees an innovation will succeed. 

At BYOSOM, we’re betting on a future where beauty is no longer one-size-fits-all, but built around a deeper understanding of what truly aligns with each individual.

About BYOSOM

BYOSOM is a biotechnology-driven cosmetic system built around genetic onboarding and biologically aligned formulation. Operating strictly within cosmetic regulatory frameworks, the platform emphasizes clarity, restraint, scientific discipline, and long-term structural understanding over trend-based claims.

Invisible code. Visible beauty.

Author Bio

The author is the founder of BYOSOM.