If this is AI’s printing press moment, it’s getting cranked up to 10x speed.
At the Imagination in Action AI Summit held at MIT this week, there was no mistaking the mood: we’re no longer on the verge of a major transformation, we’re in the middle of it. AI is accelerating faster than any previous wave of tech. And many enterprises? They’re still trying to find the on-ramp.
What stood out most wasn’t just the demos of AI agents autonomously collaborating with each other, or startups training models for a fraction of Big Tech’s cost. It was the widening gap between what’s possible and what’s actually being deployed inside Fortune 1000 companies.
For leaders in innovation, R&D, and strategy, that gap is no longer academic, it’s existential.
We’re Still in the “Dial-Up” Days, but the Speed is Breakneck.
Multiple speakers likened today’s moment to the rise of the internet, or the invention of the printing press. The key difference? Those revolutions unfolded over decades. AI is evolving in months or even hours.
Agentic AI systems are already autonomously completing tasks and coordinating with other agents. Generative tools are building podcasts, writing reports, and surfacing strategic recommendations with minimal human input. Hardware is racing to keep up.
As one speaker put it: “We’re in the dial-up phase of AI, but this time, dial-up is moving at warp speed.”
MIT’s Ramesh Raskar put it simply: send AI agents to agent school, so that they would learn how to collaborate with one another. This is no longer hype; it’s infrastructure.
Enterprise Adoption is Still Buffering
Despite the rapid tech evolution, most enterprises are nowhere near AI maturity. Not because of tech limitations, but because of cultural inertia, regulatory complexity, and organizational drag.
Executives from Prudential, Fidelity, and John Hancock spoke candidly about the challenges of regulated environments, lack of clean data, and the fear of getting it wrong.
Prudential CTO Robert Sala detailed the complexity of deploying AI across 50 states, each with different regulations. Fidelity’s Lisa Huang called the cost of errors in financial services as the biggest barrier — and biggest opportunity. She framed GenAI as “a new kind of computer,” one that requires companies to rethink not just tooling, but how they define product-market fit.
But some are moving. John Hancock shared that it’s already reached a 70 percent internal AI adoption rate, proving that large enterprises can move fast if they choose to.
The larger truth? Culture is the bottleneck now. If a company is just shifting from paper to digital, it’s already behind.
Agents, Energy, and the Next Wave
Beyond language models, the summit focused on what’s coming next—and what’s quietly reshaping the edge of AI strategy:
- Agentic AI: Tools that execute tasks independently, coordinate with other agents, and act as digital teammates, not just copilots.
- Energy-Aware Compute: Crusoe CEO Chase Lochmiller emphasized the role of energy as a new bottleneck. Data centers now consume 25 percent of Northern Virginia’s energy. Smart compute is being colocated with wind and solar in places like West Texas.
- Custom Hardware: From AI-optimized chips to rethinking the computer itself, a wave of specialized compute platforms is being built for this moment, not the last one.
Snowflake CEO Sridhar Ramaswamy framed it clearly: “AI is becoming the consumption layer on top of data.” In other words, AI tools (like LLMs, copilots, and assistants) are becoming the main interface through which people and systems interact with and make use of data. And with open-source innovation booming, deployment is no longer a question of access, but execution.
It’s cheap to try 20 ideas to find two that work.
— Andrew Ng
What Fortune 1000 Leaders Must do Now
For innovation, strategy, and R&D leaders, the takeaways from the summit weren’t theoretical. They were tactical:
- Shrink adoption timelines. Deployment cycles must match the speed of innovation in weeks or months, not years.
- Make data usable. AI without clean, structured data is a Ferrari without gas.
- Create “two-way door” pilots. Low-risk, reversible experiments unlock momentum and learning.
- Upskill across the org. AI isn’t just an IT concern. Marketing, legal, operations, everyone needs fluency.
- Prioritize capability over curiosity. Playing with ChatGPT is table stakes. Enterprises need a roadmap and real AI-powered workflows.
Andrew Ng offered one of the most useful filters: “It’s cheap to try 20 ideas to find two that work. That’s just smart strategy now.”
The gap between possibility and reality is widening. But it’s still bridgeable if companies move now. Otherwise, they risk becoming the next Blockbuster in an agent-powered world.
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