Kalshi Co-Founder Luana Lopes Lara on AI, Prediction Markets, and Building a Company That Moves Faster

The most interesting thing about prediction markets isn't that they can tell us who might win an election, whether the Knicks will win the NBA Finals, or what the odds of a recession might be.

It's that they reveal what people are becoming worried about.

For Kalshi co-founder Luana Lopes Lara, one shift has become increasingly visible: people are no longer primarily asking what artificial intelligence can do.

They're asking what AI is going to do to them.

In a wide-ranging interview about Kalshi, AI, jobs, hiring, and entrepreneurship, Lara described how market demand has evolved alongside the AI boom. A year or two ago, users wanted markets forecasting AI capabilities—which model would win, which problems AI could solve, and how quickly the technology would advance.

Now, requests increasingly revolve around consequences: layoffs, unemployment, elections, and the economic impact of AI.

For entrepreneurs, that's more than an interesting data point. It's a window into where the next opportunities—and anxieties—may be emerging.

From Asking “What Can AI Do?” to “What Will AI Do to My Job?”

Lara believes work will be one of the areas AI changes most dramatically in the near term.

But her view is more nuanced than the familiar “AI is coming for everyone's jobs” narrative.

Inside Kalshi, she said, the company hasn't simply eliminated entire roles because AI can perform them. Instead, roles are being augmented. Engineers can operate with coding agents. Information can be gathered and synthesized automatically. Managers can get context faster.

That distinction matters.

The first wave of AI adoption was largely about individual productivity: write this email, summarize this document, generate this code.

The next wave may be about redesigning how companies operate.

Kalshi offers a glimpse of that transition.

At the time of the interview, Lara said the company had roughly 170 employees, while using AI across engineering, market operations, metrics, communication, planning, and other workflows.

The objective isn't simply to accomplish the same work with fewer humans.

It's to accomplish more work with the humans you already have.

That's an important mindset shift for early-stage founders.

Instead of asking, How many employees can AI replace?, a more useful question may be:

What could my company attempt if AI dramatically increased the leverage of every person on the team?

AI Gave This Founder Something More Valuable Than Automation: Context

One of Lara's most revealing examples isn't a flashy AI product.

It's her Sunday planning.

As a founder overseeing a rapidly growing company, she needs to understand what's happening across teams, what was promised the previous week, what actually happened, where metrics are moving, and what deserves attention next.

Historically, assembling that context could consume an enormous amount of time.

Kalshi has been building systems that connect information across tools such as email, documents, Slack, and internal company data. Lara described AI helping aggregate updates, compare progress against previous commitments, surface metrics, and flag areas where execution isn't matching expectations.

The result?

She can spend less time collecting information and more time thinking about it.

She even joked that she can have brunch on Sundays again.

For founders, this points toward one of AI's most valuable applications.

Don't just automate tasks. Automate context gathering.

A founder could ask:

• What commitments did we make last week that haven't been completed?
• What customer complaints keep appearing?
• Which projects have repeatedly slipped?
• What changed in our most important metrics?
• Where are team members blocked?
• Which customer requests deserve prioritization?

The breakthrough isn't necessarily having an agent make every decision.

It's having an agent make sure the founder enters the decision with the right information.

The Solo-Founder AI Narrative Misses Something Important

There's a popular vision of AI entrepreneurship in which one founder commands an army of agents and builds a massive company alone.

Lara's experience suggests another possibility.

AI can make companies more capable—and simultaneously increase the value of exceptional people.

Kalshi has invested in engineers specifically tasked with bringing AI into different functions. Instead of expecting every department to independently figure out how to reinvent itself, technical talent can work alongside teams such as design or legal to discover new workflows.

Lara's reasoning is simple: if something is strategically important, giving it 5% of someone's attention probably won't produce the best result.

That lesson extends far beyond AI.

Founders love wearing multiple hats because early-stage entrepreneurship demands it. But there's a difference between temporarily owning many responsibilities and pretending that everything important can remain a side project forever.

AI doesn't eliminate the need for ownership.

In many cases, it makes clear ownership even more valuable because the potential leverage is so much greater.

Hiring Is Changing Too

AI is also changing what Kalshi looks for in candidates.

Lara explained that candidates can use AI during engineering interviews. The company is increasingly interested in how prospective employees have worked with AI—not just in engineering, but in areas such as design and legal.

Yet technical proficiency isn't the entire test.

The deeper characteristic is adaptability.

Can someone accept that the process they mastered two years ago might no longer be the best process?

Can they experiment with new tools without becoming defensive about the old ones?

Can they learn quickly?

That leads to an increasingly important hiring principle for founders:

Don't only hire people who are excellent at today's workflow. Hire people willing to reinvent tomorrow's workflow.

In an environment where tools change every few months, expertise remains valuable. But expertise combined with low ego and curiosity becomes far more powerful.

Prediction Markets Could Become More Than Betting

Kalshi's larger ambition also offers entrepreneurs a lesson in reframing products.

Prediction markets can easily be viewed as places where people wager on future events.

Lara described a broader use case: information.

She said roughly 70% of Kalshi's users don't trade; they visit to look at forecasts, treating the platform almost like a new way to consume news.

Instead of reading ten opinions about whether something might happen, users can see a probability generated by a market in which participants have money behind their predictions.

There's also another emerging use case: hedging.

Lara shared an example of a New York bar that planned to cover customers' tabs if the Knicks won. Because that promotion could create a substantial financial loss, the business could take an offsetting position tied to the outcome.

She also described interest from people in hurricane-prone areas who wanted markets that could help offset specific financial risks.

For entrepreneurs, there's a product lesson hiding here:

Your customers may eventually discover a more valuable use case than the one your category is famous for.

The question is whether you're paying attention when they do.

“I Want to Make Sure Always That I Did Everything That I Could”

Technology may be changing entrepreneurship, but Lara's most powerful founder lesson has little to do with AI.

It comes from Kalshi's regulatory battle.

The company spent years navigating regulation around its markets. Eventually, Lara and her co-founder reached a point where they believed another path remained: challenging their regulator in court.

For a small company—and for Lara personally as an immigrant—the decision was intimidating.

But it aligned with a principle she says has guided her life:

“I want to make sure always that I did everything that I could.”

That philosophy doesn't mean working endlessly for the sake of appearing busy.

Lara connected it to hiring great people, taking care of employees, making difficult decisions, and pursuing every reasonable path before accepting that something cannot be done.

It's a powerful distinction for wantrepreneurs.

Entrepreneurship rarely gives you certainty.

You won't know whether the product will work. You won't know whether customers will come. You won't know whether the market will change underneath you. And, as Kalshi itself demonstrates, even sophisticated prediction markets deal in probabilities rather than guarantees.

You can control something else.

You can keep learning.

You can hire better.

You can experiment.

You can adapt when technology changes the rules.

And when the obvious options disappear, you can ask whether there's still one move you haven't made.

The Founder Advantage AI Can't Automate

Lara is optimistic about AI precisely because she believes it expands what companies can attempt.

That may be the most useful takeaway for entrepreneurs.

The winners of the AI era won't necessarily be the founders who automate the most jobs or deploy the most agents.

They may be the founders who recognize that leverage creates more possibilities.

A five-person company can attempt things that once required 20 people. An employee can manage information that once overwhelmed them. A founder can understand an organization faster. Teams can launch experiments without consuming the resources those experiments once required.

But none of that answers the fundamental entrepreneurial question:

What should we build with all that leverage?

AI can help gather information, write code, organize plans, analyze feedback, and accelerate execution.

The founder still has to decide what matters.

And Lara's journey offers a useful philosophy for making that decision: focus on what you can control, surround yourself with people willing to learn, move faster than everyone expects—and make sure that, whatever happens, you did everything you could.