A world with abundant intelligence.

Imagine you wake up tomorrow and the most expensive thing about thinking—time, effort, and expertise—has become cheap.

Not worthless. Not meaningless. Cheap in the same way electricity is cheap compared to the age when a factory had to run on steam and muscle. Cheap in the way computing is cheap compared to the era when storage was a cabinet and “memory” was a person. Cheap in the way bandwidth is cheap compared to the days when information moved at the pace of paper.

That’s the doorway we’re walking through: a world with abundant intelligence.

I don’t mean “everyone is smarter.” I mean something more practical, more unsettling, and more powerful: intelligence as an on-demand utility. Reasoning, summarization, planning, translation, pattern recognition, and decision support—available instantly, in bulk, through an interface. Intelligence you can rent by the second. Intelligence you can embed into every process, product, and conversation.

And like every technological abundance before it, this one won’t simply add convenience. It will rearrange what society values, what companies compete on, and what work even is.

From Scarcity to Utility

For most of human history, intelligence was scarce in the ways that mattered economically.

• Scarce because expertise took years to acquire.

• Scarce because attention was limited.

• Scarce because coordination was hard.

• Scarce because knowledge was trapped in people’s heads and local institutions.

So we built systems around those constraints. Hierarchies to concentrate decision-making. Gatekeepers to manage access. Long training pipelines to produce experts. Meetings to coordinate. Paperwork to create a record of reality. Entire middle layers of organizations to move context from one person to another.

Automation, in its earliest waves, focused on the obvious: repeatable tasks and deterministic workflows. We built scripts and macros, then pipelines, then RPA, then integrations. We learned to trust machines with the consistent things.

But abundant intelligence doesn’t just automate the consistent. It pushes into the ambiguous—the parts of work that previously required a person because the rules weren’t clean, the inputs weren’t structured, and the edge cases mattered.

That’s why this moment feels different. We are not only replacing effort. We’re reshaping judgment.

And judgment is where organizations have historically spent the most time pretending to be rational.

The New Factory Floor Is Cognitive

When electricity became abundant, factories stopped being arranged around the central steam engine. Work reorganized around smaller motors distributed across the floor. Layouts changed. Roles changed. Productivity exploded—not because one machine got better, but because the system could be redesigned.

Abundant intelligence is like that. The “factory floor” being rewired isn’t only physical. It’s cognitive.

Today, a typical organization spends a staggering portion of its energy on:

• Writing and rewriting documents no one wants to read

• Searching for information that should be easy to find

• Translating between teams, tools, and definitions

• Preparing for meetings instead of doing the work

• Triaging issues and routing tickets

• Explaining decisions after the fact

• Repeating institutional knowledge because it never gets captured cleanly

These activities exist because intelligence is scarce and context is expensive. When intelligence becomes abundant, the overhead becomes negotiable.

Not eliminated overnight. Negotiable.

So the question becomes: What do we rebuild first?

Work Shifts From Doing to Directing

In a world with abundant intelligence, the high-value skill is less often “I can do the thing” and more often:

• “I can define the right thing.”

• “I can judge whether the output is good.”

• “I can set constraints that prevent harm.”

• “I can decide what matters and what doesn’t.”

• “I can translate messy reality into a crisp objective.”

• “I can notice when the system is lying.”

This is uncomfortable because it sounds like management. But it’s not the bloated management we’ve learned to resent. It’s closer to craft—the craft of steering intelligent systems toward outcomes that are correct, safe, and useful.

Think of it as the rise of new roles that exist everywhere, whether they show up in org charts or not:

• The Intent Designer: turns vague desires into precise goals, constraints, and success metrics.

• The Evaluator: builds feedback loops, benchmarks, and tests for subjective or fuzzy outputs.

• The Process Architect: redesigns workflows assuming intelligence is cheap, but trust is not.

• The Incident Anthropologist: investigates failures, not just for bugs, but for mismatched assumptions and incentives.

• The Human Advocate: ensures systems increase dignity rather than squeezing people into machine-shaped jobs.

In other words: the core skill becomes orchestration—not of people alone, but of people and machines in a shared operating model.

This is the heart of what “Masters of Automation” is really about. Not tools. Not hype. The discipline of building systems that do work on your behalf, reliably, in the real world.

Automation Stops Being Deterministic

Here’s the part we need to say plainly: abundant intelligence is probabilistic.

Classic automation is comforting because it’s legible. If X happens, do Y. If the job fails, inspect the logs, fix the rule, rerun the pipeline. You can draw the workflow on a whiteboard and feel like you understand it.

Intelligent automation behaves differently. It’s closer to hiring a very fast intern who’s read a million documents and can draft anything—but who sometimes misunderstands your intent with total confidence.

That doesn’t make it useless. It makes it a different class of system. Which means the engineering mindset must evolve.

To build responsibly in a world of abundant intelligence, we need to treat AI-enabled automation like we treat production services:

• Observability isn’t optional. You need traces, metrics, and audit trails of what the system did and why.

• Evaluation becomes a first-class feature. Not “did it run,” but “was it correct,” “was it safe,” “was it aligned with policy,” “did it degrade gracefully.”

• Guardrails are part of product design. Permissions, escalation paths, safe defaults, and clear boundaries.

• Human-in-the-loop is a dial, not a dogma. Some actions need approval forever. Others can graduate from review to monitoring once reliability is proven.

• Failure modes must be designed, not discovered. What happens when the model is wrong? When data is missing? When the system is attacked? When the user is ambiguous?

In short: abundant intelligence doesn’t eliminate engineering rigor. It punishes the lack of it.

If Intelligence Is Cheap, What Becomes Expensive?

Whenever a capability becomes abundant, value migrates to what remains scarce.

If intelligence is abundant, scarcity shifts to:

1) Trust

Trust in outputs. Trust in provenance. Trust that actions were authorized. Trust that the system won’t leak data, hallucinate a policy, or quietly corrupt a process.

Organizations will pay an increasing premium for systems that are trustworthy, not merely clever.

2) Attention

If everyone can produce infinite content, marketing copy, presentations, emails, and “thought leadership,” then attention becomes the real currency. The winners won’t be those who generate the most. They’ll be those who can consistently generate signal.

3) Taste and Judgment

Taste is the ability to choose what good looks like. Judgment is the ability to decide what matters. Neither is solved by raw intelligence. In fact, abundant intelligence increases the need for taste—because the space of options expands dramatically.

4) Clean Data and Clear Definitions

Abundant intelligence can reason, but it can’t magically fix incoherent organizations. If your metrics are wrong, your sources conflict, your definitions shift by department, and your incentives are misaligned, the system will faithfully accelerate your confusion.

5) Accountability

When a person makes a decision, you know who to talk to. When an automated system makes the decision, accountability can become a fog. That fog will be expensive—legally, ethically, and operationally—unless it’s designed out.

Organizations Get Rewritten

One of the biggest misconceptions floating around is that abundant intelligence just means “more productivity.” That’s true, but incomplete.

The deeper change is structural.

When the cost of coordination drops, organizations don’t just do the same things faster. They reorganize.

You’ll see:

• Smaller teams with larger output

• More “solo-plus-agents” operators who look like companies

• A shift from role-based work (“that’s not my job”) to outcome-based work (“ship the result”)

• Faster iteration cycles, because drafting, testing, and summarizing compress dramatically

• New internal markets for automation: teams buying and selling agent workflows like services

• A premium on API-first thinking—not just in software, but in operations (“How does this function get invoked, measured, and governed?”)

But you’ll also see a counter-force: consolidation. If intelligence is a utility, whoever controls the infrastructure, the distribution, and the data pipelines can concentrate power.

So the future won’t be purely decentralized or purely centralized. It will be a tension between the two—just like every major technology wave.

Education Must Evolve: Intelligence Literacy

If intelligence is abundant, the most important thing we can teach people isn’t memorization. It’s:

• How to ask good questions

• How to verify claims

• How to spot manipulation

• How to define objectives and constraints

• How to reason about tradeoffs

• How to build a mental model of systems

In other words: intelligence literacy.

Because abundant intelligence will produce a paradox: we’ll have more “answers” than ever, and more confusion than ever, at the same time.

When anyone can conjure a convincing explanation on demand, the differentiator becomes the ability to ground reality—to demand evidence, to test assumptions, and to keep the feedback loop connected to the world.

The Moral Question: What Do We Automate?

This is where we need to be honest. Automation is not neutral.

A world with abundant intelligence can become:

• A world where people do more creative work, with fewer drudgery tasks

• Or a world where humans are reduced to compliance checkers for machine output

• A world where small teams can compete with giants

• Or a world where giants become unassailable

• A world where knowledge becomes universally accessible

• Or a world where misinformation becomes industrialized

The technology doesn’t pick. We do—through incentives, policy, product design, and cultural norms.

So the real question for builders, founders, and automation leaders is not “Can we?” It’s:

What should we automate—and what should we protect as human?

There are tasks humans hate that should be automated aggressively: repetitive triage, basic routing, tedious formatting, transcription, extraction, reconciliation, boilerplate drafting.

But there are also tasks that look inefficient but are deeply human: mentoring, ethical deliberation, conflict resolution, care work, meaning-making, and the responsibility of final judgment in high-stakes decisions.

The goal isn’t to remove humans. It’s to remove waste, while increasing dignity.

A Practical Blueprint for Building in This World

If you’re building automations—whether for an enterprise, a startup, or your own life—here’s a grounded approach that holds up under reality:

Start with the “Loop,” Not the Model

Don’t begin with “Where can I use AI?”
Begin with: “Where is there a closed-loop process with measurable outcomes?”

If you can’t measure it, you can’t improve it. If you can’t improve it, you can’t trust it. If you can’t trust it, you can’t automate it fully.

Design for Escalation

Every intelligent system needs a plan for ambiguity. Define:

• When does it ask a human?

• When does it refuse?

• When does it log and proceed?

• When does it roll back?

Most disasters aren’t caused by wrong outputs. They’re caused by wrong outputs taken as actions.

Treat Context Like a Product

In abundant intelligence, context is the fuel. Garbage context yields convincing garbage results.

Invest in:

• Clean knowledge bases

• Clear documentation

• Versioned policies

• Source attribution

• Data contracts

• A single definition of core metrics

Build Evaluation Into the Workflow

Create scorecards. Create sampling. Create red-team tests. Create regression suites for prompts and policies.

You wouldn’t ship code without tests. Don’t ship agent workflows without evaluation.

Keep Humans Accountable, Not Buried

Humans should be the authors of intent and the owners of outcomes. Automation should make responsibility clearer, not murkier.

If your system makes it hard to answer, “Who decided this?” you’ve built a risk generator.

The Closing Thought

A world with abundant intelligence doesn’t mean a world where everything is easy.

It means a world where the bottleneck shifts.

We will no longer be limited by our ability to produce words, analyze data, draft plans, or generate options. We will be limited by our ability to choose wisely, to govern responsibly, to build systems that deserve trust, and to stay anchored to reality.

Abundant intelligence is a multiplier.

It will multiply good strategy and bad strategy.
It will multiply healthy culture and toxic culture.
It will multiply clarity and confusion.
It will multiply courage and complacency.

So if you’re listening as a builder, here’s the challenge worth carrying into the next decade:

Don’t just automate tasks. Automate toward a better world.
One where intelligence is abundant—and dignity, trust, and accountability rise with it.