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Home Blog AI on Your Own Terms: Anaconda Acquires Kilo Code

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AI on Your Own Terms: Anaconda Acquires Kilo Code

By David DeSanto and Scott Breitenother

July 15, 2026

There’s an old public service announcement from a generation ago: It’s 10 p.m. Do you know where your children are?<br>The question was designed to make people stop and reckon with something they’d been assuming was fine.<br>Every CIO, CTO, and CISO in the world should be asking a version of that question about AI right now: Do you know where your data is?<br>Not according to your company’s internal AI policies. Actually, in practice. Builders are running AI agents today on personal accounts, with their own API keys, across five different tools, routing an organization’s most sensitive context through external services no one is monitoring.<br>For most enterprises, the honest answer is: no.<br>Today, there is a solution. Anaconda is acquiring Kilo Code.<br>The Tokenpocalypse Is Here<br>The global AI economy now processes hundreds of trillions of tokens every month, according to OpenRouter. Kilo alone orchestrates almost 10 trillion tokens per month across more than 3 million developers. These AI-native development workflows are operating at a scale that would have been unimaginable 12 months ago.<br>Meanwhile, enterprise AI spend is growing faster than anyone’s ability to account for the spend. AI builders are being told to move fast, and the token consumption accumulates invisibly, spread across dozens of tools, work accounts, and personal accounts. It leaves no single view of where it all goes.<br>There’s been no real answer to token-maxxing. Organizations are spending enormous sums, with very little visibility into where the risk is and if they are getting an ROI.<br>The harder question underneath the spend is one of dependency. Would any organization accept a single source dependency on anything else this critical to its business? If a cloud provider had downtime, there would be a failover plan that included an alternate cloud provider. If there is a chance your company’s law firm could have conflicts on the business you need from them, you would have another law firm on retainer or ready to go.<br>Yet many enterprise development workflows today are entirely dependent on a single model provider. If that provider has an outage, changes their pricing, including removing capabilities from your plan, or decides to retire a model suddenly, work stops.<br>Developers aren’t going to stop using AI. The question is whether enterprises are going to own that experience end to end or just hope for the best.<br>"There’s been no real answer to token-maxxing. Organizations are spending enormous sums, with very little visibility into where the risk is and if they are getting an ROI." —David DeSanto, CEO, Anaconda

The False Trade-Off<br>In conversations with enterprise leaders, the pattern is consistent. CIOs and CTOs are not afraid of AI. They are afraid they’ve lost visibility into what it’s costing, whether their data is being handled securely, and whether they have any real alternative. They feel trapped by what feels like their only option. They have no consolidated view of what their teams are running, and they cannot assess whether the speed they are gaining is commensurate with the risk accumulating underneath it.<br>The instinct is to frame this as a forced choice: velocity or trust. Move fast or move safely. Give AI builders the tools they love, or maintain the governance posture the board requires. That is a false trade-off. And the fact that so many enterprise leaders believe they must make it is precisely the problem this acquisition is designed to solve.<br>Right now, most enterprises are living with one of two realities. Either they lock everything down to one tool and one model provider, and the CTO becomes the person holding back the team. Or, they look the other way while developers use whatever they want, with zero visibility. Neither of those is a real strategy.<br>What enterprises actually need is a path between those two options: any IDE, any model, any provider, with a single layer to see all of it, enforce policy across it, and trust that AI is operating on the organization’s terms.<br>"Developers aren’t going to stop using AI. The question is whether enterprises are going to own that experience end to end or just hope for the best." —Scott Breitenother, CEO and Co-Founder, Kilo Code

What We’re Building Together<br>Anaconda and Kilo have been working on the same underlying problem from different sides.<br>Anaconda has spent over a decade earning trust at the foundation layer: the packages, the environments, the models, the orchestration layer, and the governance that over...