What happened: IBM Think rounded up a grab-bag of expert predictions for 2026: more agentic capabilities, more “do it with less” hardware obsession, and a lot of people politely admitting that just making the model bigger is getting old.

Why it matters: The through-line is that AI leadership shifts from raw model scale to the systems around it—deployment, tooling, security, and the efficiency tricks that make scarce compute feel less scarce (or at least make the invoice easier to explain).

Wider context: If 2025 was “throw GPUs at it,” 2026 is “optimize like your budget depends on it,” with edge AI, specialized accelerators, and hardware-aware models positioned as the next scaling strategy.

Background: IBM also points to quantum computing progress (and the growing overlap with AI tooling) as a looming milestone—because apparently we needed *another* compute paradigm to argue about in meetings.

The trends that will shape AI and tech in 2026 — IBM

Singularity Soup Take: The industry is quietly swapping “scale everything” for “engineer everything,” which is great news for systems people and terrible news for anyone whose entire moat was a bigger parameter count and a louder keynote.

Key Takeaways:

• Efficiency Scaling: IBM’s experts argue the next gains come from hardware-aware models, new accelerator strategies, and edge optimizations—because GPUs are still king, but kings are expensive and supply chains have opinions.
• Systems Win: The predictions emphasize systems and integration over model bragging rights: the winners make AI usable, governable, and secure in real workflows, not just impressive in a demo.
• Quantum Tease: IBM highlights continuing quantum milestones and convergence with AI developer tools, framing quantum as a future unlock for tough optimization and discovery problems—pending reality’s usual schedule slippage.