Bindu Reddy doesn’t just build AI companies—she reimagines how entire industries will operate in an agent-driven future. As CEO and Co-Founder of Abacus.AI, the San Francisco-based company behind ChatLLM Teams and DeepAgent, Reddy is on a mission to make AI accessible to every professional and enterprise. With a career spanning leadership roles at Google, where she headed product for Google Apps, and Amazon Web Services, where she established the AI verticals organization and launched pioneering services like Amazon Personalize and Amazon Forecast, Reddy brings a rare combination of product vision and technical depth to the AI race. Her previous venture, Post Intelligence, a deep-learning company focused on social media influencers, was acquired by Uber—a testament to her ability to identify market opportunities before they become obvious.
What sets Reddy apart in the crowded AI landscape is her willingness to take bold, contrarian positions. While many American AI labs remain locked in a closed-source arms race, she’s become one of the most vocal advocates for open-source AI, warning that the U.S. risks ceding technological leadership to China if it doesn’t embrace openness and democratization. Yet her views are refreshingly nuanced: she acknowledges that closed-source models like Anthropic’s Opus 4.5 still hold an edge for complex reasoning tasks, even as she predicts that gap will rapidly narrow. With backing from tech luminaries including Eric Schmidt, Ram Shriram, and Mike Volpi of Index Ventures, Abacus.AI has raised over $90 million to pursue Reddy’s vision of a future where small teams of 10-20 people can supervise AI agents that manage multi-billion-dollar operations.
Reddy, who holds a Master’s degree from Dartmouth College and a B.Tech from the Indian Institute of Technology, Mumbai, envisions a world where AI handles the majority of white-collar work by 2028, freeing humans to focus on creativity, strategy, and high-level decision-making. It’s an ambitious timeline, but for someone who’s already helped shape AI products used by millions, betting against her vision seems increasingly unwise.
In Conversation: Bindu Reddy on Open-Source AI, Model Preferences, and the Agentic Future with Interviewer Tom White
You’ve been incredibly vocal about what you call the “open-source AI tsunami.” Can you explain what’s driving this shift and why you believe it’s so critical for the industry?
Bindu Reddy: Absolutely! Look, we’re witnessing something remarkable—models that are significantly more powerful than GPT-3, which OpenAI once deemed “too dangerous” to release, are now freely available, and guess what? The sky hasn’t fallen. Chinese AI labs like DeepSeek, Kimi, and Qwen are releasing world-class open-source models at a blistering pace. Kimi K2.5, for instance, is arguably the best open-source model available right now. What’s happening is that the gap between open-source and top closed-source models is shrinking much faster than anyone predicted.
The critical piece here is sovereignty and decentralization. When you rely solely on closed-source models, you’re at the mercy of providers for pricing, terms of service, and strategic decisions. Open-source gives you control over your AI stack—you can fine-tune models, run them on your own infrastructure, and truly understand their workings and limitations. This isn’t just a technical preference; it’s about preventing dangerous monopolies. We’ve seen what happens with mobile app stores—do we really want to repeat that mistake with AI? The geopolitical stakes are enormous too. If American labs stay closed while China democratizes access, we’re essentially handing them the keys to global developer mindshare and ecosystem development. That’s a future where developers worldwide build on Chinese AI foundations, and the network effects are absolutely seismic.
That said, you’ve also acknowledged that closed-source models still have advantages. Which closed-source models do you prefer, and for what use cases?
Bindu Reddy: You’re right—I’m not dogmatic about this. Right now, Anthropic’s Opus 4.5 is my go-to for tasks requiring the highest level of reasoning and reliability. When you need bulletproof logical reasoning, complex multi-step problem solving, or mission-critical decision-making, Opus 4.5 is tough to beat. It’s expensive, yes, but the world would have 1000x more agents deployed if it were cheaper! That price-to-performance ratio is actually the most important benchmark in 2026—not just token prices, but the cost to get a particular task done.
I also respect what OpenAI and Google are doing with their latest models. GPT-5 and Gemini Pro have made impressive strides. But here’s the thing: I believe this advantage is temporary. The gap is closing every month. In six months, we’ll likely see open-source models matching today’s Opus 4.5 performance. That’s why businesses need to diversify their AI stack right now—experiment with models like Kimi for agentic coding tasks, DeepSeek for everyday use cases, and Qwen as fine-tuning bases. Don’t put all your eggs in one basket, whether that basket is closed-source or open-source.
Let’s talk about Abacus.AI specifically. How are you helping companies create, deploy, and orchestrate AI agents at scale?
Bindu Reddy: This is where I get really excited! 🚀 At Abacus.AI, we’ve built what I believe is the most comprehensive platform for agent orchestration. Our DeepAgent technology can handle everything from app creation to automated workflows, research, data analysis, and multimedia generation—all from natural language prompts. But the real magic is in the orchestration layer.
We’ve developed a sophisticated multi-agent architecture that intelligently switches between state-of-the-art models—OpenAI, Anthropic, Google, and our own fine-tuned models like Dracarys and Smaug—to maximize accuracy and speed for different subtasks. It’s like having a team of specialized AI experts working together. DeepAgent employs a “plan-execute-review” loop, breaking down complex requests into manageable steps, executing them with the right tools, and iterating until the goal is achieved. We’ve integrated dozens of external tools and services—Gmail, Slack, Jira, Salesforce, you name it—so these agents can actually take actions across your entire business ecosystem.
What’s revolutionary is how accessible we’ve made this. You don’t need a team of ML engineers to deploy a production-ready AI agent. A marketing manager can create an automated social media workflow, a finance analyst can build a data analysis agent, or a developer can spin up a full-stack application—all through conversational interfaces. We’re seeing companies use Abacus.AI to automate contract understanding, claims management, customer support, sales outreach, and even complex testing workflows. The vision is simple: small teams supervising powerful AI agents that handle the heavy lifting while humans focus on strategy and creativity.
You’ve painted a picture of a future where 10-20 person teams manage multi-billion-dollar operations through AI agents. How far away is that reality?
Bindu Reddy: Closer than people think! My timeline is that AI will automate the majority of white-collar work between 2026 and 2028, with robots handling blue-collar work by 2027-2030. We’re already seeing Amazon and other tech giants reduce middle management layers because they understand AI can bridge senior leadership with hundreds of individual contributors. The companies winning in this new era will be largely employee-owned, with each person acting as a supervisor for multiple specialized agents.
At Abacus.AI, we’re building the infrastructure to make this transition smooth. Our platform handles the complexity of agent orchestration, model selection, safety guardrails, memory management, and long-term context—all the hard problems that prevent agents from being truly autonomous. We’re SOC-2 Type-2 and HIPAA compliant, with sandboxed execution environments, so enterprises can trust these agents with sensitive operations. The technology is ready. The question isn’t “if” but “how fast can organizations adapt?” And honestly, the ones moving fastest on AI adoption will have an insurmountable advantage by 2028.
Final question: In the broader context of decentralized AI, what do you see as the biggest risk if we don’t embrace openness?
Bindu Reddy: The biggest risk is that we’re paralyzed by hypothetical scenarios while our competitors are shipping, iterating, and open-sourcing. Look, I was initially sympathetic to AI safety concerns, but the predicted disasters simply haven’t materialized despite vastly more powerful models being freely available. The real existential risk isn’t AI becoming too powerful—it’s falling so far behind in the AI race that we lose economic and technological leadership.
If the U.S. falls behind China in AI, the consequences cascade: China becomes the global talent magnet, the dollar weakens as a reserve currency, our VC and stock markets suffer, and we’re no longer the primary superpower driving innovation. That’s not fear-mongering—it’s geopolitics 101. Open-source isn’t just a philosophical position; it’s a strategic imperative. We need developers worldwide building on American AI foundations, creating those network effects that make ecosystems unstoppable. Chinese labs understand this, which is why they’re flooding the market with open-source alternatives. American labs need to wake up before the window closes.
The beautiful irony is that decentralized, open-source AI is also the path to a more equitable future. When AI tools are accessible to everyone—not just companies that can afford $1,000/month API bills—we unlock innovation from places we’d never expect. That’s the future I’m fighting for with Abacus.AI: powerful AI agents in the hands of every professional, every entrepreneur, every student. That’s how we reach the age of abundance. 🚀
Abacus.AI is backed by Index Ventures, Coatue, Tiger Global, and technology leaders including Eric Schmidt, Ram Shriram, and Martin Chavez.
Disclaimer: GeekWire newsroom and editorial staff were not involved in the creation of this content..
