Bindu Reddy approaches artificial general intelligence with what she terms “pragmatic optimism.” Her perspective is grounded in data: through her work on LiveBench, she and her collaborators (including Yann LeCun's team at Meta) created what she describes as the first LLM benchmark that can't be gamed. Unlike static evaluations that models can memorize, LiveBench presents dynamic challenges — providing a more rigorous assessment of true capability versus benchmark optimization.

On the technical challenges facing LLMs, Reddy has highlighted hallucination as the central unsolved problem. Commenting on OpenAI's research into why language models hallucinate, she explains that hallucinations are fundamentally guessing errors — and that current benchmarks inadvertently reward this behavior because guessing yields more correct answers. The solution, she suggests, is training models to say “I don't know” more often—accepting lower coverage for higher accuracy.

Beyond evaluation, Reddy offers practical insights on deployment architecture. She highlighted Apple Intelligence's approach as a model for the industry: LLM routing to the device — where applications handling millions of requests route simple queries to smaller, efficient models while reserving expensive frontier models for complex tasks. This technique, which Abacus.AI implements at scale by offering RouteLLM, enables cost-effective deployment without sacrificing capability where it matters. RouteLLM is Abacus AI’s proprietary technology that routes to the best LLM based on your prompt. Millions of customers use Abacus AI to route to all the top AI models.

Reddy's vision crystallizes in Abacus.AI, which she positions as the “AGI control center” for professionals and companies. The thesis is straightforward: as AI capabilities mature, organizations shouldn't need dozens of specialized SaaS subscriptions—they need a unified platform where intelligent agents understand their data, systems, and workflows holistically. Abacus.AI is building that platform. The infrastructure reflects this ambition. Abacus.AI has developed agents with persistent, infinite memory that maintain context across sessions and can execute on schedules. Unlike stateless chatbots that forget everything between conversations, these agents accumulate organizational knowledge over time—learning company-specific terminology, understanding internal processes, and building institutional memory that compounds with use.

A year ago, Reddy declared that 2025 will be the year of AI agents. She predicts organizations will deploy 50 to 500 agents that autonomously interact with enterprise systems, automate workflows, and execute tasks with full context of organizational data. This isn't speculative—it's the product roadmap. This vision is beginning to crystallize in 2026 and 2027.

The endgame Reddy envisions is a paradigm shift. Rather than humans adapting to rigid software interfaces, software adapts to humans. Rather than switching between applications, employees interact with a single intelligent layer that orchestrates everything underneath. The AGI control center is about giving every employee an infinitely patient, infinitely knowledgeable assistant that understands the full context of their organization and automates all their day-to-day tasks.

For Reddy, this is the path to AGI that matters: not a singular superintelligence emerging from a research lab, but distributed intelligence woven into the fabric of how organizations operate. Abacus.AI is her bet that the company building this infrastructure—the control center where agents are deployed, monitored, and orchestrated—will be essential to whatever AGI ultimately looks like.