The Era of Asymmetric Impact

Ram Srinivasan
Jan 5
10 min read

Happy new year!

I took the last two weeks completely off to focus on health and family.

I'm spending the first week of 2026 thinking about one thing: what stays scarce when intelligence becomes abundant. Here's why.

We've entered the Era of Asymmetric Impact.

The job-apocalypse narrative gets it backwards.

AI isn't replacing humans in most knowledge work. It's raising leverage so high that your ability to steer becomes the scarcest resource on Earth.

And in the long run, that has a name: Wisdom. This is the ability to reason about tradeoffs, second-order effects, and long-term consequences under uncertainty.

Let me show you the data, the insight, and what you do next.

The Data

We've crossed the inflection point. Not "sometime this decade." Already.

Worldwide AI spending $300B+ and on track for $632B by 2028 (IDC)
AI coding assistants write ~30–50% of code in Copilot-enabled contexts (GitHub)
68% of surveyed CEOs plan to spend more on AI in 2026 (WSJ)
Workers using AI save ~40–60 minutes/day on average (OpenAI)
AI-skilled workers earn a ~56% wage premium (PwC)
AI-exposed occupations show higher job + wage growth (Vanguard)
Some companies report ~25–30% productivity boosts when GenAI is paired with end-to-end process transformation (Bain)

You can debate individual stats. You cannot debate the trajectory. And yes, some AI projects will fail (more on why below).

But zoom out: intelligence is becoming abundant.

So what becomes scarce? The ability to aim.

The Insight (Counterintuitive, but 100% true)

AI doesn't kill human value in most roles. It relocates it.

Consider this: Moderna is <6,000 people and its CEO Stéphane Bancel has argued that doing work the “old biopharmaceutical ways” might require 100,000.

How? AI designing and prioritizing mRNA candidates, searching a combinatorial space so vast that traditional biopharma would need an army. But only because Moderna's scientists know EXACTLY which experiments to run and which to kill.

That is the new economy in one sentence: AI provides the horsepower. Humans provide the steering.

So the "physics of work" changes.

Old physics (linear) Output ≈ Time × Effort
New physics (non-linear) Impact ≈ (Leverage x Wisdom) / Friction

Here is how you manage the equation:

1/ LEVERAGE — The Multiplier (and why creation is no longer the bottleneck)

Imagine you wake up tomorrow with an army of tireless junior analysts, coders, designers, and translators who work at machine speed. That's not a metaphor. That's the power of 1,000 PhDs in your pocket. We will have 1.3B AI Agents in the workplace by 2028 (IDC).

Now plug in real-world leverage:

Amazon —$260M/year and 4,500 developer-years saved: CEO Andy Jassy said Amazon’s AI assistant helped cut the average time to upgrade an application to Java 17 from ~50 developer-days to a few hours with 79% of AI-generated code reviews were shipped without additional changes.
1800Accountant — 50% of tax-season inquiries handled by AI 1-800Accountant uses Salesforce’s Agentforce as a digital labor to absorb peak-season tax questions, resolving up to 50% of customer support requests with AI agents.
Goldman Sachs — Devin as a force multiplier Goldman’s tech chief said the bank could start with hundreds of "Devins" (autonomous agentic software engineer) and potentially scale to thousands, alongside its ~12,000 developers
Google engineer — Claude Code built in 1 hour what her team spent a year on: Jaana Dogan, Principal Engineer at Google, reported that Anthropic's Claude Code generated a working distributed agent orchestrator system in one hour that matched what her team had been developing for a year.
Andrej Karpathy — "I've never felt this much behind as a programmer": Andrej Karpathy, former AI director at Tesla and OpenAI co-founder, described the programming profession as being "dramatically refactored," with human contributions becoming "increasingly sparse" as AI tools enable developers to become "10X more powerful."

Here's the punchline: When leverage becomes cheap, creation becomes abundant.

So the bottleneck flips.

Old world bottleneck: "Can we build it?"
New world bottleneck: "Should we maintain it?"

This is the Curator's Premium. In the next decade, the highest-paid skill is not producing more. It's selecting better.

2/ WISDOM — The Constraint (and why you can't outsource the future)

As leverage explodes, the cost of a bad decision explodes with it.

That's why "more intelligence" doesn't automatically mean "better outcomes." It can mean the opposite: faster mistakes at larger scale. And here's what most people get wrong about wisdom: it's not the domain of a few wise elders at the top. The best organizational judgment is distributed. It emerges from diverse perspectives colliding. From frontline workers who see what executives miss, from cross-functional friction that surfaces blind spots. When you concentrate decision-making in fewer hands because 'they have the wisdom,' you're actually reducing your organization's intelligence.

The goal is to create systems where collective judgment can form faster than individual expertise decays.

You see this everywhere:

Netflix — "informed intuition" beats pure data Netflix has elite models and still relies on "informed intuition" for content bets. Because the decision isn't just prediction—it's portfolio risk, timing, brand, cultural resonance, second-order effects. One misfire is a nine-figure mistake.
Unilever — responsible AI and brand stewardship Unilever uses AI for optimization, but humans choose what's acceptable for long-term trust. Models optimize clicks. Leaders protect reputations.
Microsoft — Copilot is the first draft, not the final say Microsoft's framing is the correct governance posture: AI accelerates drafting; humans keep accountability. Why? Because accountability is not a feature you can ship. It's a role you must hold.

BUT when AI drafts, we are faced with an uncomfortable truth: Cognitive Atrophy.

If juniors offload all the "grunt work" (summaries, debugging, first drafts), they lose the reps that produce good judgment.

That creates a hollow organization: a handful of seniors steering a million AI outputs and not enough humans being trained to steer next.

So here's the fix, and I'm 100% confident this will separate winners from others:

Build Corporate Flight Simulators

Stop training people with busywork. Start training them with simulation.

PR crisis simulator
Security incident simulator
Outage triage simulator
Vendor negotiation simulator
Regulatory response simulator

Let juniors use AI inside the sandbox. Grade them on reasoning, tradeoffs, and second-order effects. Debrief. Repeat.

Because wisdom isn't downloaded. It's built.

And in a world where IQ is increasingly a utility, wisdom becomes the moat.

3/ FRICTION — The Denominator (and why speed without architecture becomes chaos)

This is where most companies faceplant. They "install" AI. They celebrate speed. And then they drown. That’s why ~95% of AI initiatives don’t break even or create value (MIT). Not because the tech fails, but because orgs can't integrate what they build.

As individual leverage rises, coordination becomes the bottleneck.

Here's a thought experiment: Imagine ten people in your org can each prototype ten solutions per week = That's 100 prototypes per week.

Now ask: who integrates them? Secures them? Owns them? Supports them? Maintains them?

That's the Coordination Paradox: leverage creates option overload, and option overload creates organizational friction.

So the winners do something very boring but VERY valuable. They build decision architecture.

Portfolio governance: prototypes are venture bets; kill most fast; resource the few that matter
Kill criteria: define failure before you start, so nothing becomes a zombie
Living context: record not just what you built, but why you rejected alternatives
Incentives: promote people who prune wisely, not people who accumulate projects

AI will happily generate infinite work.

Your job is to prevent infinite "work slop" from entering production.

What you do next

Stop optimizing for time. Start optimizing for the equation.

1/ Multiply leverage on purpose: Give teams AI, yes. But require curation. Make "should we ship this?" more rigorous than "can we build this?"

2/ Turn judgment into wisdom: If you don't build flight simulators, your org will borrow wisdom from a few seniors until it runs out and then it will crash at scale.

3/ Design for low friction: If you don't implement portfolio governance, AI will not make you faster. It will make you louder.

And here's the binary reality, there is no middle ground:

Winners will use AI to amplify human wisdom and ship at machine speed.
Others will use AI to generate endless output and call it progress while friction eats them alive.

Though we should be honest: the steering advantage we have today may not be the one we have tomorrow, which means the real skill is building organizations that adapt as the boundary keeps shifting.

This isn't 300 years from now. It's this quarter.

AI increases leverage. That part is universal.

The competitive edge comes from the two things that don't scale automatically. Those are: (1) Low friction decision architecture and (2) Judgment that compounds into wisdom. And that CANNOT be bought from your favorite LLM manufacturer.

This is the Era of Asymmetric Impact.

And if you're not preparing for it, you're already falling behind. A quick pressure test (before we wrap)

What if the AI bubble pops and budgets tighten? Then the winners won’t be the ones with the most pilots. They’ll be the ones with the cleanest portfolio discipline: fewer bets, clearer ROI, and kill criteria defined upfront.

What if you can’t build wisdom through flight simulators? Then you’re still building it, just the old way: through real outages, real customers, real reputational hits. The simulator is a way to compress reps and make the learning cheaper than the consequences.

What if leverage keeps rising but oversight can’t keep up? Then “more AI” turns into negative ROI: more surface area, more integration debt, more maintenance. That’s friction winning the equation.

What if AI simply makes you faster at being what you already are? Glad you asked.

AI doesn't make bad organizations good. It makes them faster at being what they already are.

If your organization can't decide what to build, AI will help you build more things you shouldn't maintain. If your organization doesn't develop judgment, AI will help you make faster mistakes at larger scale. If your organization runs on coordination friction, AI will create more work than it eliminates.

The leverage is already here. The question is: do you have the architecture to aim it?

Bonus: 2026 AI Trends Analysis

Here's something I wanted to try.

I took the framework above—(Wisdom × Leverage) / Friction—and used it to produce this trends analysis.

Here's how:

Wisdom: I gave Gemini 3.0 an overarching strategic lens (what matters vs. what's just noise).
Leverage: I used Gemini Deep Research to synthesize hundreds of signals into five core trends.
Friction Reduction: I used Google's NotebookLM to convert the synthesis into a video overview. No manual editing, no production bottleneck.

The whole process took less than 60 minutes. A year ago, this would've taken a team a week.

That's the shift. AI didn't replace strategy, it amplified it. The thinking is still mine. The execution is 10X faster.

What you're looking at above is proof that the right architecture lets you move from idea to insight to artifact in a single afternoon.

This is what leverage looks like when you design the system correctly.

Happy new year again!

Here's to designing better in 2026.

Until next time,

Ram — Ram Srinivasan MIT Alum | Author, The Conscious Machine | Global AI Adoption Leader.

Published in Business Insider, Fortune, Harvard Business Review, MIT Executive Viewpoints and more.

—

A Message From Ram:

My mission is to illuminate the path toward humanity's exponential future. If you're a leader, innovator, or changemaker passionate about leveraging breakthrough technologies to create unprecedented positive impact, you're in the right place. If you know others who share this vision, please share these insights. Together, we can accelerate the trajectory of human progress.

Disclaimer:

Ram Srinivasan currently serves as an Innovation Strategist and Transformation Leader, authoring groundbreaking works including "The Conscious Machine" and the upcoming "The Exponential Human."

All views expressed on "Explained Weekly," the "ConvergeX Podcast," and across all digital channels and social media platforms are strictly personal opinions and do not represent the official positions of any organizations or entities I am affiliated with, past or present. The content shared is for informational and inspirational purposes only. These perspectives are my own and should not be construed as professional, legal, financial, technical, or strategic advice. Any decisions made based on this information are solely the responsibility of the reader.

While I strive to ensure accuracy and timeliness in all communications, the rapid pace of technological change means that some information may become outdated. I encourage readers to conduct their own due diligence and seek appropriate professional advice for their specific circumstances.

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