Slow Burn, Not Instant Blaze: The *Real* AI Timeline
I was never of fan of hype and with GenAI I find it particularly concerning. So whenever i hear about an opinionated view on a topic this close to my hearth, I pay extra attention.
Arvind Narayanan and Sayash Kapoor from Princeton University wrote a nuanced piece that cuts through the noise and looks at a more probably outcome, which in short is: AI adoption will come, AI will continue to improve but it will not disrupt our lives as much as the AI silicon valley companies tell you.
Narayanan and Kapoor argue that while today's AI *feels* revolutionary, the full economic and societal transformations are going to unfold over decades, not in a few short years. He breaks down the AI landscape into three key components: AI methods (the algorithms), AI applications (the products built using those algorithms), and AI adoption (the widespread, real-world use of those applications). And here's the kicker: each of these operates on its own, much longer, timeline.
AI Methods: The Foundation Takes Time to Pour
Think about the algorithms themselves. These are the core engines driving AI. New advancements are being made all the time. Narayanan would say these "AI methods" are progressing at a steady but not necessarily breakneck pace. Expect continued, if sometimes slow, iteration on the building blocks of AI. This sentiment echoes the AI Snake Oil report findings, which suggest that progress may be slowing down.
AI Applications: Building the Houses…Eventually
Next up: AI applications. These are the tangible products we see and use every day – the chatbots, the image generators, the AI-powered marketing tools. Building these applications on top of those foundational algorithms also takes time. These are being built now, and will continue to develop.
Us building CASSI is a good example. It will be the perfect tool for social media marketing, but it´s only currently being build and will need some more time to reach the feature set and maturity we want it to have.
We’re seeing a surge in the development of expert-level AI tools. Some expect expert level AI by 2027, but the key here is scaling.
AI Adoption: Living in the Houses...Much, Much Later
Here's where things get *really* interesting. Even with the best algorithms and the coolest applications, widespread adoption is the final (and slowest) piece of the puzzle. Just because a technology *exists* doesn’t mean everyone’s going to use it tomorrow. We're talking about changing workflows, retraining employees, addressing ethical concerns, and navigating complex regulatory landscapes. AI adoption faces challenges with misuse, but as they point out, we can create beneficial apps too.
Narayanan also speaks in the Hardfork podcast and gives the example that while A LOT of people already use AI almost every day, if you look at HOW MUCH and WHAT FOR they use it, the adoption rate is more similar to other technologies, like the Internet or Smartphones.
Think about it. Even with something as ubiquitous as the internet, it took decades for it to truly become ingrained in every aspect of our lives. GenAI will likely be the same.
Why This Matters (And What You Should Do About It)
So, why does all of this matter? Because understanding the *real* AI timeline helps us avoid the trap of short-term hype and focus on long-term strategic planning. Instead of panicking about robots stealing your job next week, start thinking about how AI can *augment* your work in the coming years.
Here are a few things to consider:
Focus on Education: Understand the *fundamentals* of AI. Don't just chase the latest shiny object.
Experiment Strategically: Identify areas where AI can realistically improve your business or workflow. Start small, iterate, and learn. Maybe try CASSI first 😉
Advocate for Responsible AI: Engage in conversations about ethical guidelines and policy. After all, the decisions we make *now* will shape the AI landscape for decades to come.
The AI revolution is happening. But it's not a sprint. It's a marathon. And understanding the long-term perspective is the key to not just surviving but thriving in the AI-powered future.
