Warren Buffett once quipped, “Forecasts may tell you a great deal about the forecaster; they tell you nothing about the future.” Still, predictions, when well framed, can illuminate the trajectory of technology and business strategy. With that in mind, here are six economic and technological predictions for AI in 2026.
The Peter Principle suggests that bureaucratic bloat stifles innovation. In the AI era, this tension becomes acute. Despite three years of progress, AI adoption inside large firms remains sluggish. Upskilling and technical barriers don’t fully explain the lag. In 2026, expect a wave of internal revolts: frustrated teams departing to build AI-native old-economy businesses or transform incumbents from within. One or more CEOs in financial services will be appointed primarily as “AI visionaries.” I see it in my own sector: our new platform, Eudokia, reimagines financial advisory for the agentic AI age.
2. The real bottleneck is us:
The biggest constraint to AI isn’t compute, data, or power. It’s human capital. AI will not be fully autonomous anytime soon. In the meantime, hybrid human-AI work will dominate, but we lack a sufficiently trained workforce to support it. Midterm election rhetoric will muddle the job debate, but by year’s end, the narrative of a “jobpocalypse” will give way to a clearer picture: automation demands a new labor paradigm. Expect a real conversation to begin around a post-scarcity, intelligence-driven economy — a world shaped not by lost jobs, but by unprecedented new tasks.
3. Three breakthrough technologies will define the year:
First, memory: AI systems are moving toward persistent, personalized recall. Tools such as Notebook LM and ChatGPT’s evolving context windows show what’s possible. Expect a 50x improvement in memory capabilities, with AI agents able to remember user preferences, tasks, and workflows across contexts. Second, autonomy: Prompting will recede as “context engineering” rises. AI agents will run for hours, executing tasks with minimal human input. Some startups may even offer portability of memory between systems, marking a turning point in true AI autonomy. Third, reverse prompting: AI will increasingly anticipate our needs, offering suggestions proactively within our software and communications. The result will be less command, more collaboration — a foundational shift in digital labor.
4. Benchmarks will yield to economic value:
For years, AI discourse was dominated by technical benchmarks: ARC-AGI, SWE Bench, etc. These are becoming irrelevant to real-world users. The focus is shifting to economically valuable tasks. One promising metric: GDPval, which measures AI’s ability across high-value professional tasks. GPT-5.2 recently achieved a 70.9% score — the highest to date. This reframes the AGI discussion. Instead of obsessing over artificial general intelligence, we should be building and measuring RTAI: radically transformative AI. Policymakers and economists must abandon the monolithic view of “the user.” Some are dabbling; others are already forging new business models through deep AI collaboration.
5. New financial models will be necessary to fund compute demand:
Compute demand will continue to surge, but our current financial system is ill-equipped to fund the AI and robotics revolution. The capex required for new data centers and robotic infrastructure is staggering. Just as past industrial revolutions birthed novel financial instruments, we’ll see 2026 catalyze financial innovations tailored for the AI era. This may include data center-specific funds, energy investment platforms, or even orbital infrastructure financing. From solar farms to space-based data centers like StarCloud, the future of compute will require economic reinvention.
2026 will not be the year AGI arrives, but it may be the year our institutions begin to reckon with its implications. The economy will bifurcate: one track for casual users and slow-moving corporations; another for AI-integrated pioneers crafting the post-scarcity economy. Those who ignore this shift risk becoming the horses of the internal combustion era — powerful but obsolete. The challenge ahead is not simply building better AI. It is reconfiguring our economic, corporate, and social systems to work with it.
Sebastien Laye is an economist and AI entrepreneur.
