Agency > Intelligence: We Are All Directors of Abundant Intelligence Now
Intelligence is no longer the scarce resource. AI has made it abundant, accessible, and increasingly cheap. What's scarce now is the human capacity to direct it well. The professional advantage going forward isn't knowing more or working faster. It's knowing what to point intelligence at, when to trust it, and when to override it. That's agency. And developing it is a skill, not a personality trait.

Meta Prompting: Using AI to Design the Prompt Before You Run the Actual Query
Most people jump straight into asking AI for the answer. Skilled practitioners ask AI to help them ask the right question first. Meta prompting is a three-step pattern: describe the goal (not the task), let AI surface your blind spots and draft a better prompt, then save what works as a reusable template. The result is fewer wasted iterations, sharper first outputs, and a prompt library that compounds over time.

Finding Signal in the Silicon Valley Noise: Deciphering the Future of AI Amidst Hype, Headlines, and Hyperbole
Every major tech CEO is making bold claims about where AI is heading, and they all have enormous financial incentives to shape that narrative. The skill isn't ignoring them. It's learning to read the moves behind the messaging, separate infrastructure bets from marketing theater, and build your own informed perspective on what actually matters for your work and your institution.

Situational Awareness: Plan for Tomorrow's AI
AI capabilities are changing faster than most institutional planning cycles can absorb. This section looks at what's coming next so you can build strategy that holds up even as the technology underneath it keeps moving.

When Will Top Quartile Human Performance Be Achieved with AI?
In 2017, experts placed most AI milestones decades out. By 2023, those same timelines had collapsed by 20 to 40 years. Natural language generation, pattern recognition, sensory perception... capabilities that were supposed to be a generation away are here now. The pattern matters more than any single date on the chart: if your strategy depends on AI staying roughly where it is, the research says you're already behind.‍

What Does It Mean When AI Scores >140 on a Mensa IQ Test?
Leading AI models hit average human cognitive performance in 2024. A year later, they crossed into the top 2% of the population on standardized measures, and the trajectory hasn't flattened. IQ tests don't capture everything that matters, but they do capture something important: raw cognitive horsepower is becoming commodity infrastructure. The professional value proposition has to come from a different place now.

The Human Intelligence Landscape
‍Not all human capabilities face the same AI pressure. This framework maps the terrain: embodied intelligence, lived intuition, and ethical responsibility remain distinctly human moats. Creativity, wisdom, and contextual reasoning sit on an erosion watchlist—still human advantages, but narrowing. Learning efficiency and pattern recall? AI has already arrived. Understanding where your work falls in this landscape helps with planning.

Seven Capability Revolutions Reshaping Intelligence
‍AI isn't one thing—it's a cascade of expanding capabilities, each unlocking new forms of human partnership. From language models (2022) through reasoning, multimodal perception, and agentic execution, to emerging world models, embodied AI, and spatial intelligence. Each layer builds on the last. Understanding this evolution is essential for knowing where human agency fits in.

AIME 2025 Benchmark: Top 8 Models
‍The American Invitational Mathematics Examination is designed to challenge the top high school math students in the country. As of December 2025, the leading AI model scores 100%—perfect accuracy. Eight different models now exceed 91%. A benchmark designed to challenge elite human talent no longer differentiates between humans and AI.

Software Engineering: SWE Bench Verified
‍On the industry-standard benchmark for real-world coding tasks, the leading AI model now outperforms the top 2% of human software engineers. Six different models have crossed the 74% threshold, with Claude Opus 4.5 exceeding 80%.

GPQA Diamond Benchmark: PhD-Level Domain Expertise
‍The Graduate-Level Google-Proof Q&A Diamond benchmark tests questions so specialized that even domain experts with internet access score only 65–74%. Ten AI models now exceed that range, with top performers pushing past 90%. The "Google-proof" knowledge barrier (questions too complex to simply search) no longer stops machines.

The Signal Most Leaders Missed: It's Already Happening at the Highest Levels of Intellectual Work
Top physicists at Princeton report that 90% of the intellectual labor in their research is now performed by AI. In February 2026, GPT-5.2 conjectured an original result in theoretical physics that human researchers then proved and verified. This isn't about AI summarizing papers or drafting emails. It's producing original scientific discovery. If it's already reshaping work at that level, the question for every other profession isn't whether it reaches you. It's when.

2001: 1,000 Songs in Your Pocket
25 years ago, Apple put your entire music library on a device that fit in your hand. At the time, that felt like the biggest thing abundance could mean.

2026: 1,000 PhDs in Your Pocket
Now the same device gives you access to PhD-level reasoning across virtually every discipline, on demand, for a monthly subscription. The question the music industry had to answer after the iPod applies to every knowledge profession now: once access is no longer the bottleneck, what actually becomes valuable?

Human x AI Innovation Flywheel
This isn't a one-directional story where AI replaces human contribution. Human agency directs intelligence toward problems worth solving. AI capability amplifies what humans can reach. More capable AI feeds back into new discoveries, which produce new innovations, which fund more capable AI. The flywheel only spins when both sides are engaged. Agency is what keeps humans in the loop as a force multiplier, not a spectator.

Five Breakthroughs (Plus One): What AI's Architects Say Must Come Next
The leaders building these systems largely agree on where the gaps are: reliability, continuous learning, perfect memory, social intelligence, embodied intelligence, and the bonus that keeps the safety community up at night, self-recursion. Where they disagree is on priority and sequence. Reading across their public statements from early 2026, the convergence tells you what's coming. The divergence tells you who's betting on what, and why.

NVIDIA and the Infrastructure Bet
Jensen Huang is the person selling the picks and shovels. Understanding NVIDIA's roadmap matters because it tells you what the AI labs will be capable of building 12 to 24 months from now. The hardware determines the ceiling.

$600B in Infrastructure: The Next 24 Months Are Already in the Ground
Four generations of NVIDIA chips are already in various stages of deployment, from Blackwell (live now) through Rubin Ultra (late 2027). The compute available to AI systems will increase roughly 21x over current levels within that window. This isn't speculation or a research paper. The hardware is purchased, the data centers are being built, and the deployment schedules are public. Whatever AI can do today, plan for it to do dramatically more on a timeline that's already locked in.

The 2028 Global Intelligence Crisis / The 2028 Global Intelligence Boom
Same forces, two very different headlines. Whether the explosion of AI capability reads as crisis or boom depends entirely on how prepared institutions and individuals are when it arrives. The framing is the point: the underlying event is the same either way.

None of This Matters. Most of This is Noise.
Experts disagree on whether AI capability plateaus or goes exponential. They disagree on whether the economic impact is a boom or a displacement crisis. But here's what makes the debate academic for practitioners: across every scenario on this chart, the preparation is identical. Build human agency. Learn to direct intelligence well. That work pays off whether we get the optimistic outcome or the difficult one.

Here's What Does: Eighteen Months to Differentiated Advantage
Four domains, stacked over 18 months. AI Agility builds the foundation of human-AI collaboration. AI Workflows maps existing processes against what to automate, augment, or protect. Agentic Workflow Design reimagines work around what AI can now do autonomously. AI Orchestration is where you learn to direct swarms of agents within systems. The constant underneath all four: build human skills that contribute to human agency. That's what compounds.

Now, We Are All Directors of Intelligence
This isn't a job title. It's a description of what professional work has become. Every person with access to AI is now directing cognitive resources that didn't exist three years ago. The gap between people who recognize that and people who don't is already visible, and it's widening.

Vinod Khosla: "We've Rationed the Time of Our Most Expensive People"
Khosla names the constraint that shaped every workflow, org chart, and software purchase for decades: expert time was scarce, so everything was built to conserve it. AI dissolves that constraint. The workflows designed around rationing are still running, but the reason they were designed that way no longer holds.

Innovation and the Human Agency Gap
Four revolutions, each arriving faster than the last. Agriculture took 10,000 years to reshape civilization. Industry took 200. Digital took 25. The Intelligence Revolution is compressing that timeline further, and the gap between how fast technology moves and how fast humans adapt is wider than it's ever been. That gap has a name: the Human Agency Gap. Closing it is the work.

In the Intelligence Revolution... Agency > Intelligence
The thesis in two words and a symbol. When intelligence is everywhere, the ability to direct it well becomes the scarce resource. This is the inversion most institutions haven't absorbed yet.

Andrej Karpathy on Agency
One of the people who built these systems looked back at his own assumptions and concluded he had the hierarchy wrong for decades. Cultural obsession with IQ, with raw intellectual horsepower, obscured what actually drives outcomes. Agency is more powerful and more scarce than intelligence. Coming from someone who co-founded OpenAI and ran AI at Tesla, that's worth sitting with.

The Agency Distribution
Across 11,308 employees, only 17% show a high internal locus of control. They'll adapt no matter what happens. Another 29% sit at the low end and need sustained intervention. The real story is the 54% in the middle: the uncertain majority whose trajectory depends almost entirely on the conditions their organization creates around them. That's where institutional investment pays off.

General Personal Agency Scale: Measuring Human Agency Directly
Agency isn't a vague concept. Rutgers validated a six-factor measurement instrument across 7,109 respondents: locus of control, intentionality, self-determination, human judgment, self-efficacy, and accountability. The national distribution skews left, meaning most people report moderate to high agency, but self-report is notoriously inflated. What matters for practitioners is that we now have a way to define, measure, and develop agency as a specific set of capacities rather than treating it as a personality trait you either have or don't.

Agency Responds to Design
BCG found that 15% of frontline employees feel positive about generative AI when left to figure it out alone. With leadership support, that number jumps to 55%. Same people. Same technology. The only variable is the conditions around them. Agency isn't fixed. It responds to how organizations design the experience of change.

The Diverging Trajectories: Agency Determines Who Creates Value as AI Accelerates
Three paths, three outcomes. The 17% with high agency will adapt regardless. The 29% at the bottom face deepening dependency without sustained support. The 54% in the middle is where the leverage lives. With the right conditions, they activate and create an Agency Dividend that compounds over time. Without support, they drift toward dependency. The challenge for institutions isn't the top or the bottom. It's what happens to everyone else.

The Power of Inquiry to Spark Breakthrough Ideas
When answers are abundant and cheap, the person who asks the better question gets disproportionate results. Inquiry is becoming a professional skill, not a soft one. The ability to frame a problem well, to ask what hasn't been asked, matters more now than the ability to research an answer manually.

Intelligence Frontier: Thinking Beyond Displacement and Efficiency Strategies
Most organizations are stuck asking how AI can make existing work faster or cheaper. That's the wrong altitude. The better questions: What's already working that you'd 10X if intelligence were unlimited? What problems feel impossible only because you can't hire enough talent to solve them? And the one that reframes everything: What are the things only humans can do, and how do you use abundant intelligence to multiply those?

We Are Lacking for Abundant Intelligence Use-Cases
Most AI ideas are clustered around core use cases that require only moderate model intelligence. The biggest area of opportunity sits in the upper right of the chart, at the intelligence frontier, where more capable models unlock entirely new categories of value. The bottleneck isn't the technology. It's a lack of ideas about what to do with it.

As model intelligence increases, the constraint shifts from capability to agency and imagination. The organizations that learn to direct intelligence to meaningful outcomes and generate ideas at the frontier will pull away from those still optimizing the obvious.

AI Agility: Human x AI Collaboration (0-6 Months)
Research from Northeastern and Harvard found that the skills predicting successful AI collaboration aren't technical. They're Theory of Mind skills: perspective-taking, belief tracking, goal inference. Meanwhile, Berkeley Haas documented what happens when organizations chase speed without intention: 62% of entry-level workers reported burnout as AI intensified work rather than reducing it. The foundation matters. Going faster to increase outputs misses the real value. Sustainable, intentional use is what separates performance from fatigue.

Seven Domains of Building AI Agility
Seven competency areas, stacked in three layers. Foundation covers AI fluency, collaboration strategy, and responsible use. Application moves into creating tangible value and building sustainable habits that protect well-being alongside performance. Integration is where it gets personal: applying AI to your specific role and workflows, then preparing for what comes next. Each layer builds on the one before it. Skip the foundation and the application layer collapses.

Generative AI Value-Creation Pyramid
Four levels. Most organizations are stuck at level one: individual productivity improvements, quick wins, foundational skills. The real value lives higher up. Level two is collective intelligence, where AI becomes a team member and governance gets embedded. Level three redesigns core processes for abundant intelligence. Level four is visionary innovation, creating new markets and business models that weren't possible before. The shift from value capture (automating fast work) to value creation (augmenting slow work) is where most organizations stall.

Fei-Fei Li on Human-AI Collaboration
The founder of World Labs and one of the most respected voices in AI research sees the same thing practitioners are experiencing on the ground: the collaboration between humans and AI will be the most productive path forward. Coming from someone building the next generation of world models at Stanford, that framing carries weight. It's not AI alone. It's the partnership.

AI Workflows: Integrating AI into Your Work Habits (0-6 Months)
Every process gets classified into one of three categories: automate (repetitive, codified, low-stakes), augment (where AI makes your best human thinking significantly better), or protect (work requiring tacit knowledge, trust, and accountability that stays human). Most organizations jump straight to automation and miss the augmentation gap entirely, which is where the larger untapped value lives. There's also a question most leaders avoid: what is human work for us right now, with the AI that actually exists today? The honest answer forces a distinction between work that is irreducibly human and work that is merely familiar.

Andrew Ng on Workflows
The co-founder of Google Brain and Coursera sees the same pattern from the research side: the next wave of AI breakthroughs won't come from building bigger models. They'll come from how people design the workflows around them. The model is the ingredient. The workflow is the recipe.

Three Forces of the Agency Economy
Human Agency, Agentic Workflows, and Agentic AI. Three forces, connected by multiplication. Human agency without agentic AI is limited in reach. Agentic AI without human agency is powerful but directionless. Agentic workflows sit in the middle, where the two meet in practice. The Agency Economy runs on all three working together.

Agentic Workflow Design: Human Agency x Agentic AI (6-12 Months)
A workflow tool waits for a prompt. An agentic system pursues a goal, taking actions and making judgments without a human at each step. That autonomy is the value and the risk. The HBR 2x2 framework maps every task across two dimensions: cost of error and knowledge type. Low-stakes explicit data tasks can run autonomously. High-stakes tacit knowledge tasks stay human-led. Two questions leaders tend to avoid: if this agent makes the wrong call, can we catch it before it matters? And if an agent handles this for 12 months, who still knows how to do it? This is a prerequisite chain, not a curriculum menu. If you haven't built AI Agility and Workflow foundations first, don't start here.

A Framework for Choosing Where and How to Use Generative AI
The expanded HBR 2x2 from Anand and Wu. Four quadrants based on cost of error (low to high) and type of knowledge required (explicit data to tacit). "No regrets" tasks like bulk customer inquiries and document summarization can run with full AI autonomy. "Quality control" tasks like drafting contracts and writing production code need AI producing the work and humans verifying it. "Creative catalyst" work like ads and product development has AI generating options and humans selecting. "Human-first" work like strategy and disciplinary decisions keeps humans leading with AI in a supporting role. The framework gives organizations a concrete decision tool instead of debating AI adoption in the abstract.

David Solomon: "The Last 5% Now Matters Because the Rest Is Now a Commodity"
The CEO of Goldman Sachs is saying out loud what a lot of leaders are still processing quietly. When AI can produce 95% of the work, the differentiator shifts entirely to that final 5%: the judgment, the context, the taste, the accountability. That's where human value concentrates. And it's a very different skill set than producing the other 95% ever was.

10 Stages to the Goldman Sachs S-1 Workflow
The S-1 filing process broken into ten phases, each mapped to a Human Agency level. Mandate and framing starts at H5, full human agency. Data assembly drops to H1, autonomous AI. Financial modeling sits at H2. The workflow oscillates across the entire scale depending on what each phase actually requires. This is what agentic workflow design looks like in practice: not one blanket decision about how much AI to use, but a deliberate call at every stage.

Human Agency Scale: Opportunities for Human-AI Collaboration
Five levels, from H1 (AI operates autonomously, humans intervene on alerts) to H5 (complete human control, AI silent or passively available). The scale runs along a spectrum from AI Drives to Partnership to Human Drives. Not every task belongs at the same level. The skill is knowing which level fits which task, and being deliberate about that choice rather than defaulting to wherever the technology lands you.

Goldman Sachs S-1 Workflow: Human Agency Task Time Distribution
‍When you look at where the actual time goes, 60% of the workflow runs at H1, fully autonomous AI. Another 15% sits at H2, delegated AI agency. Only 15% of total task time requires H5 or H4 levels of human involvement. Solomon's "last 5%" lands differently when you see it mapped: a small fraction of the total workflow carries almost all of the judgment, accountability, and reputational weight.