Beyond the Threshold: Why AI’s Revolution Outpaces the Internet

Artificial intelligence has crossed a line we will not recross. When Cognition CEO Scott Wu declared that today’s AI would eclipse the internet as the most significant technological shift of our lifetimes—even without further breakthroughs—he put words to what many technologists already feel in their gut: the threshold is behind us. The questions now center on speed and consequence, not on possibility.

The Internet Was Just the Warm-Up

The web rewired how humans connect to information; AI rewires how we process that information. Where browsers extended our reach, modern models redraw the boundary of cognition itself, offloading reasoning, pattern-finding and decision support. Industries from finance to hospitality already trust AI agents to approve expenses or parse performance data—tasks once guarded by human gatekeepers.

An illustrative control room showing AI orchestrating decision flows.

Crossing the Capability Gap

The remarkable fact is that AI’s core abilities are “good enough” to disrupt today’s workflows. Language models draft code, craft legal summaries and tutor students with competence that rivals junior professionals. Vision models read X-rays; speech models translate in real time. For most commercial use-cases, the bottleneck is no longer capability.

Yet, skeptics raise an important flag: raw scale alone does not grant true general intelligence. Human cognition thrives on data efficiency—learning from sparse examples—whereas transformer architectures guzzle terabytes. Without architectural leaps, some argue, models may plateau short of AGI. The debate is healthy, reminding us that progress is more than parameter counts.

Deployment Is the New Frontier

If AI is already competent, why hasn’t every firm re-engineered itself around digital employees? The answer is deployment. Integrating a model into secure workflows, aligning outputs with policy and measuring ROI demand painstaking systems work. Enterprises that treat AI as a “fancy calculator” miss its potential; those willing to redesign processes reap compounding advantage. The gulf between capability and adoption widens each quarter.

Visual metaphor for human and machine cognition converging.

Society’s Lagging Frameworks

Technological accelerants rarely wait for social scaffolding. Unlike the gradual roll-out of broadband, AI’s leaps arrive in sudden, disorienting jolts. Laws, labor agreements and educational curricula struggle to keep pace. Calls for a new social contract—one that anticipates cognition as a commodity—grow louder. Without it, power may concentrate where compute and capital already reside, amplifying geopolitical tension.

The AGI Question

True Artificial General Intelligence—systems capable of self-directed growth and reflection—remains aspirational. Critics point to the inherent data inefficiency of current models and proclaim that AGI will demand novel paradigms. Proponents counter that iterative scaling, synthetic data and recursive self-improvement could inch us there sooner than expected. Regardless of which camp proves right, today’s “narrow” AI is sufficiently powerful to remake value chains long before AGI settles the argument.

A Call to Build Responsibly

We have crossed the Rubicon quietly. From here, velocity matters more than proof-of-concepts, and stewardship matters more than hype. Organizations must shift their mindset from experimenting with models to employing them as colleagues. Regulators and ethicists must draft rules that align incentives without throttling innovation. And each of us—citizens, workers, leaders—must decide how to thrive in a world where human cognition is no longer the sole engine of economic value.

The threshold is behind us; the future, still unwritten, accelerates toward us every six weeks. Our readiness—not the technology—will determine whether this revolution dazzles or destabilizes.

---