I don’t have the inside scoop.
If I did, I would’ve put money on the Hoosiers’ historic 16-0 season, and I definitely would’ve picked the winning numbers for Powerball’s Christmas Eve jackpot.
I’d also be able to tell you what’s next for AI this year. Truth be told, no one knows for sure.
All I know is that technology moves fast. I felt it at GitLab as we built and expanded our DevSecOps platform. I felt it again at OpenSSF as a governing board member. And I feel it now at Anaconda as we help enterprises move AI securely from prototype to production.
But I will say this: 2026 feels different. AI isn’t suddenly more mature, but its utility is no longer in question. You can see this with projects like OpenClaw and capabilities like Claude Cowork. Now, customers want proof of its value, and investors want their returns. That means AI will get judged not on its potential, but on what it actually delivers.
Here’s what I expect to see this year:
Everyone Is Talking About AI ROI Wrong
This year may be the first time that AI’s ROI is measured well. Everyone in the industry is currently looking at “efficiency” as the top KPI, but it shouldn’t be the only metric by which success is determined. In fact, companies like Klarna have restaffed post major layoff decisions because their AI investments didn’t create the boost in talent efficiency they promised.
Instead, I think we’ll start to see a shift in the way that organizations evaluate ROI from a standard approach to a bespoke one, that takes a company and its developers’ unique needs into account. At GitLab, we measured AI success through DORA Metrics, including Lead Time for Changes as well as the number of bugs we mitigated getting to production. But this approach may not be as impactful for another organization. Leaders will need to decide on the metrics that make the most sense for them.
Companies That Innovate With AI to Solve Critical World Problems Will Survive the AI Bubble
2026 is the year AI startups are going to have to figure out how to solve real world problems, instead of just being a lightweight wrapper around models’ APIs. Getting ahead of a potential “AI bubble” burst means companies will invest more heavily in developing AI-native capabilities throughout their offerings, and in using AI to solve critical world problems that we face every day. Being just an AI wrapper will no longer cut it.
Building for a Future Where Python Is No Longer the Top Language for AI
Python is the premier language for AI, and while I don’t expect that to come to an end in 2026, I do predict that a new language will surpass it in the coming years. Organizations need tools that evolve and natively address underlying performance and scalability issues, enabling AI agents to successfully create enterprise-grade applications and services.
In 2026, I expect organizations will start investing in universal building blocks that accelerate AI value and adoption. That’s what will best position them to navigate the ever-evolving lexicon of coding languages that power AI’s infrastructure layer.
David DeSanto is CEO at Anaconda, where he leads the company’s mission to empower the world’s data science and AI communities through open-source innovation and secure enterprise solutions. A proven product and technology executive, David holds more than two decades of experience spanning cybersecurity, developer platforms, and enterprise software. Follow David on LinkedIn for more insights about AI and the future of open source.