AI scaling
AI scaling at Beyond Market Intelligence is a file of 3 stories. The newest of them: “Beyond Scaling: Cerebras on the Future of AI's Compute Demands”, “Four strategies to scale multi-agent AI without rising token costs”, and “From Chatbots to Operational Intelligence: Building Agents That Scale”. Andrew Feldman, CEO of Cerebras Systems, is set to challenge the AI industry's assumptions at TechCrunch Disrupt 2026. Scaling a multi-agent architecture doesn't have to mean watching your token bill climb. Acme AI is the next-generation, AI-powered spreadsheet platform built to replace Excel and redefine how analysts, data scientists, and enterprise teams work… The list below is every AI scaling story on Beyond Market Intelligence, newest first.
Beyond Scaling: Cerebras on the Future of AI's Compute Demands
Andrew Feldman, CEO of Cerebras Systems, is set to challenge the AI industry's assumptions at TechCrunch Disrupt 2026. He'll confront a simple question: what happens when today's hardware can't keep up? Cerebras is building differently, not just faster chips, but a fundamentally different approach to compute, energy, and infrastructure. This isn't about incremental upgrades. It's about rethinking the ceiling. For context on why some builders are stepping back from consumer AI, see our related piece on frontier labs.
Four strategies to scale multi-agent AI without rising token costs
Scaling a multi-agent architecture doesn't have to mean watching your token bill climb. This guide lays out four practical strategies to keep costs in check while you grow, and it's a refreshingly grounded take on a problem many teams quietly wrestle with. The focus here is on smart implementation, not hype. For more on the broader infrastructure behind such systems, our piece on distributed training algorithms offers a useful foundation. If you're ready to streamline without the sting, this is a worthwhile read.
From Chatbots to Operational Intelligence: Building Agents That Scale
Enterprises don't fail to scale AI because the models aren't smart enough; they stumble on the messy reality of their own systems. Arun Joseph brings that truth to life with real-world lessons from scaling Deutsche Telekom's LMOS. He tackles the organizational fault lines head-on, advocates for replacing tool sprawl with core platform abstractions, and pushes beyond basic chatbots toward operational intelligence. His work with ephemeral agents and an Agent Definition Language makes this a practical, human-centered look at what's actually required.