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If you follow technology news closely, it probably feels like artificial intelligence is advancing at a breathtaking pace. Every few weeks, there’s a new model, a new demo, or a new claim that this time everything has changed. In my experience covering AI for over a decade, I’ve never seen progress feel this fast.
And yet—after testing systems, interviewing engineers, and watching deployment inside real organizations—I’ve come to a counterintuitive conclusion: AI progress is slowing down, not speeding up.
That doesn’t mean AI is stagnating. It means the nature of progress has changed. We’re seeing rapid surface-level improvements, wider adoption, and more polished products, while foundational breakthroughs are becoming rarer, slower, and more expensive.
This distinction matters deeply. Investors, developers, policymakers, and everyday users are making decisions based on a perception of exponential growth that no longer fully matches reality. In this article, I’ll explain why AI progress feels faster than ever, why it’s actually decelerating underneath, and what that means for the next decade of technology.
To understand today’s paradox, we need to separate capability growth from visibility growth.
The early 2010s delivered genuine leaps:
Deep learning overtook traditional ML
GPUs unlocked massive parallelism
Transformers revolutionized language understanding
Each breakthrough enabled entirely new categories of applications. Progress wasn’t just incremental—it was foundational.
What followed was not a flood of new ideas, but a flood of applications:
APIs wrapped around existing models
User-friendly interfaces
Fine-tuning for specific tasks
Massive commercialization
In other words, AI stopped being a lab curiosity and became a product. This shift created the illusion of nonstop breakthroughs—even when core methods remained largely the same.
After testing multiple AI platforms side by side, I noticed something important: many “new” AI products share the same underlying architectures.
What’s changing rapidly is:
UX design
Latency
Integration into workflows
Marketing narratives
This is similar to smartphones after 2010. The technology matured, but refinement created the feeling of constant innovation.
AI models still improve when you:
Add more data
Add more parameters
Add more compute
But the gains are shrinking.
What once delivered dramatic jumps now produces:
Marginal accuracy improvements
Better edge-case handling
Slightly more coherent reasoning
In my experience, doubling model size today often produces results users can’t even perceive without benchmarks.
Humans notice:
New abilities
Better interfaces
More natural language
We don’t notice:
Plateauing reasoning
Persistent hallucinations
Conceptual limitations
This creates a mismatch between perception and reality. AI feels smarter because it sounds smoother—not because it understands more.
AI is now hitting problems that don’t scale neatly:
Reasoning across long contexts
Causal understanding
True abstraction
Robust common sense
These aren’t compute problems—they’re conceptual ones.
After testing advanced reasoning systems, what I discovered is that many “reasoning improvements” are still clever pattern matching, not genuine understanding.
High-quality data is becoming:
Scarcer
More expensive
More regulated
Training on “everything on the internet” is no longer viable, legally or ethically. This directly slows foundational progress.
The economics are brutal:
Training costs scale exponentially
Energy constraints are real
Hardware gains are slowing
This forces companies to prioritize optimization over exploration, which naturally slows radical breakthroughs.
AI will:
Improve tooling
Reduce boilerplate work
Assist—not replace—engineering judgment
In practice, AI is becoming a productivity amplifier, not an intelligence replacement.
The era of “AI will fix everything” is ending.
Winning organizations:
Focus on narrow use cases
Measure ROI aggressively
Invest in human-AI workflows
Those expecting exponential capability jumps will be disappointed.
AI won’t suddenly become godlike—or useless. Instead, it will become boringly essential, like databases or cloud computing.
The risk isn’t runaway intelligence. It’s misaligned expectations.
Look for new capabilities, not better demos
Track cost-to-performance ratios
Test edge cases, not happy paths
Ask what can’t be done yet
Separate marketing from metrics
Tooling and infrastructure
AI safety and evaluation
Domain-specific models
Human-in-the-loop systems
More stable systems
Better understanding of limits
Time for regulation and ethics
Investor disillusionment
Overhyped expectations crashing
Reduced funding for foundational research
Slower progress isn’t failure—it’s maturation.
No, but easy gains are gone.
Yes—but less frequently and with higher cost.
Because refinement improves usability, not understanding.
Unlikely. This is a cooling, not a collapse.
No—just be more selective and realistic.
Reliable reasoning, memory, and causality—not bigger models.
AI progress feels fast because it’s everywhere, polished, and constantly visible. But beneath the surface, the easy wins are gone. What remains are harder, slower, and more expensive problems.
After years of hands-on testing and analysis, my conclusion is clear: AI is transitioning from explosive discovery to disciplined engineering. That shift is healthy—but uncomfortable for those addicted to hype.
The next decade won’t be defined by sudden intelligence leaps. It will be defined by how well we integrate imperfect AI into real systems, real jobs, and real decisions.
AI isn’t slowing because it failed. It’s slowing because it grew up.
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