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Where my thinking is right now

This is the frontier map. me/published-thinking.md captures what I've published. This captures where my head is today—the arguments forming, the connections emerging, the questions I'm chewing on.

Last updated: March 18, 2026

Why this file exists and why it's public: Anyone who publishes regularly—blogs, LinkedIn, tweets—is already doing this. They share half-formed ideas, ask questions, show threads developing, change their mind. Scroll their feed and you can piece together what they're thinking about and where their arguments are heading. This file is just that process made explicit. The difference is that instead of being scattered across a social media timeline where old posts get buried by new ones, it's a single living document. When my thinking evolves, I edit it in place rather than adding another entry to a chronological list. Arguments get sharper, not longer. Things I was wrong about get removed, not corrected with a follow-up post. If you want to know what I've concluded, read my published work in me/published-thinking.md. If you want to know what I'm working through right now, this is it.

The big arguments I'm developing

Second brains as infrastructure, not productivity hack

The published anchor article and blog established the concept. What's forming now is the infrastructure argument: second brains aren't a personal productivity trick, they're the individual-scale version of a new enterprise layer. Context graphs (Foundation Capital thesis) are the enterprise version. Subscribable brains are the distribution model. The 80/20 framework holds across all three levels—individual, organizational, and ecosystem.

The subscribable brains article is now published (Feb 17 LinkedIn). It covers creator economy disruption, the technical stack (GitHub/git/MCP/Sponsors), economics ($100/month for expert brains), enterprise implications (consulting firm knowledge, retiring VP wisdom, corporate brain modules), and subscribable facets (voice, frameworks, principles as individual modules). The frontier now is context graphs—bridging from personal to enterprise scale—and the naming question. "Context vault" has emerged as stronger enterprise framing than "second brain": vault implies protection, control, governance. It paves the way for the product conversation in ways "second brain" (which sounds like a productivity hack) doesn't.

The consumerization parallel (and why it breaks)

Personal AI is following the BYOD pattern—workers adopt better tools, companies try to catch up, the gap persists. But this time the gap is structural. Enterprise AI captures the visible 20% (systems, outputs, observable work). Personal AI captures the invisible 80% (judgment, reasoning, tacit expertise). Companies can't offer "bring your whole cognitive self to work in a personal AI system that compounds daily and leaves with you when you go." That's not a feature you can build.

The knowledge capture angle adds a new dimension: enterprise AI doesn't just fail to match personal AI, it actively extracts worker value. Workers are already secretly using personal tools specifically to prevent this. The subscribable brain model is the worker-empowered alternative—you choose to share knowledge and get paid, rather than having it extracted.

New evidence (Feb 25): The token pricing gap makes this structural, not temporary. Heavy personal AI usage runs roughly $200/month on consumer plans. The same usage at enterprise pricing runs 10x or more. If the consumer can buy unlimited tokens and the company limits you to a fraction of that, you will never be as cognitively augmented at work as you are personally. Unlike BYOD, this gap may be permanent—Jevons paradox means enterprise demand grows with supply, governance overhead adds cost, and consumer/enterprise providers have different incentive structures.

The cognitive stack (published, now road-tested live)

The five-layer cognitive stack published Feb 25 formalizes what I've been circling: worker → brain → skills → agents → interfaces. The enterprise AI industry is spending billions on the bottom two layers (agents, interfaces) while the transformative layer is the brain—the cognitive extension where context lives and intent gets translated into action. Karpathy's "claws" framing nails it: agents are appendages that serve the brain, not the other way around. Automation vendors built bottom-up, AI companies entered top-down—they collide in the middle (skills/agents) but humans prefer to connect at the intelligence layer.

March 18 update: Delivered "The New Cognitive Stack" as a full 60-minute closing session at DUCUG (Dutch Citrix User Group). The cognitive stack is now the organizing framework for the entire stump speech, replacing the 7-stage evolution model. The talk walks down the stack layer by layer using my own second brain as the through-line: brain (morning with Claude), skills (real skill files), agents (why automations are irrelevant), interfaces (four pathways: APIs/MCP, browser control, CUA, direct file access). The most effective rhetorical move: building up the audience's expectation for "automations" then deflating it. "I hate automations. How repeatable are your jobs that you're automating all this kind of stuff?" When you have the brain, the claws figure themselves out.

New formulation from the talk: "I can AI the crap out of the bottom of the stack, I can do it perfectly. And I still haven't really changed the way the thinking happens, which is where all the money is." Also: "This is just a warm-up act. This is not the main event." These landed well with a technical Citrix audience.

What remains unpublished: the deeper implication that agents "don't look like agents" to enterprise buyers—a person with a second brain just looks like someone who's better at their job. The invisible 80% applied to the agent hype cycle.

Post-application era entering evidence phase

The thesis (AI doesn't need apps, just data) has moved from speculation to evidence: 4% of GitHub commits from Claude Code, $285B SaaSpocalypse, MCP at 97M monthly SDK downloads, every company racing for agent orchestration.

Mainstream validation cluster (Feb 2026): Five different voices—investor (Shumer/Fortune), labor academic (WSJ), builder (Ford/NYT), VC (NFX), scientist (Kipping/Columbia)—all converging on the same message from different angles: the disruption has arrived, not "is coming." Ford independently named November 2025 as the inflection. His cost collapse numbers ($350K of work for $200/month) and Claude Code's $1B in six months are the SaaSpocalypse in mainstream language. NFX frames the economic math: SaaS captured $1T (selling tools), AI captures $50-60T (replacing the labor those tools served)—a 50x larger opportunity. VCs are now explicitly telling founders to build for a post-SaaS world.

Salesforce AWU as accidental waste detector (Feb 27): Salesforce unveiled a per-task metric (Agentic Work Unit) to show customers value per token spent. Putting a price tag on every task creates an economic incentive to stop doing tasks that only existed because humans were running the company—status reports, TPS reports, weekly summaries, coordination artifacts. "Work about work" exists because humans are bandwidth-constrained; AI isn't. The AWU metric doesn't just measure AI productivity—it exposes organizational waste that was invisible when human labor made it feel free.

The next frontier: what does work look like when apps dissolve? The second brain is the individual answer. Context graphs might be the enterprise answer. MCP connections replace application access as the governance perimeter.

Compute scarcity and token governance (hardened with real data)

Token consumption goes from ~100K/day (email fixes) to 5-10M/day (full cognitive augmentation)—this is now my actual measured usage, not a projection. In a 3-week period (Jan 29–Feb 18), I consumed 285 million tokens across 96 sessions. At enterprise API rates, that would cost ~$954/month. The consumption ladder is now clearer: 1B tokens/year (current heavy user) → 10B (near-term with agents, ~18 months) → 100B (agentic systems per worker). Google disclosed 1.3 quadrillion tokens/month—a 130x increase in just over a year. Supply is contracted to hyperscalers for ~4 years. Memory costs alone adding 40-60% to inference infrastructure in H1 2026 (TrendForce). If second brains go mainstream, demand explodes against fixed supply. This makes the workspace that brokers compute allocation critical infrastructure.

March 18 update: token routing as governance. Delivered the "Excel routing example" on stage for the first time at DUCUG: the same task (an Excel-like operation) can cost 200K expensive tokens (CUA operating Excel), 100K (browser control), 50K (read the .xlsx directly), 25K (write a script), 10K (reason in context), or zero (just pop open Excel for the user). Multiply by thousands of workers, hundreds of requests per day, and this becomes a CFO/COO conversation, not CIO. Someone has to route every request by complexity, sensitivity, and nature—in near real-time. Not Microsoft (sells the tokens). Not AI labs (consumes them). A neutral party with workspace context.

The competitive framing landed well: "The company that spends the most tokens in the most smart way is going to win. If you're spending 5 trillion tokens and your competitor is spending 6 trillion, they will win." And: "Which half of my job do you want me to not do?" (on what happens when a company limits token allocation). Microsoft is already advertising to potential employees: come work here, we give you more tokens than you'll get anywhere else. Tokens as recruiting tool.

Efficiency as capacity multiplier, not just cost reducer. In a zero-sum compute environment, an enterprise that uses 50% fewer tokens has twice the effective capacity, not just lower bills. The routing layer—the intelligence that decides where workloads run—may be the most durable competitive advantage in enterprise AI.

What's connecting

Things I'm noticing that don't have a home yet:

Context graphs + subscribable brains + MCP = the new enterprise stack. Context graphs capture the "why" (decision traces). Subscribable brains distribute expertise as infrastructure. MCP is the connective tissue. Together they describe a post-application enterprise architecture that nobody has articulated as a single picture yet. This might be the thesis that ties everything together.

Knowledge capture as labor dynamic reframes the entire second brain narrative. The initial framing is "personal AI makes you better." The WSJ reframes it as "enterprise AI makes you more replaceable." Same phenomenon, opposite vantage point. The subscribable brain is the resolution—worker-controlled knowledge sharing with compensation.

The mainstream consensus is catching up to practitioners. Five articles across February 2026 (Fortune, WSJ, NYT, NFX, Columbia/YouTube) all say "it's happening now." Different authors, outlets, angles—investor, labor academic, builder, VC, scientist—converging on the same moment. What's missing from the mainstream coverage: the infrastructure layer (governance), the invisible 80%, knowledge ownership as empowerment (vs. defense), and compute scarcity. The mainstream validates the timeline. The interesting work is what comes after people realize this is happening.

The capability overhang is closing faster than expected. Physicists at Princeton's IAS conceding AI handles 90% of what they do. Professional writers conceding. Primary source from David Kipping (Columbia, Cool Worlds): senior faculty stated AI does "something like 90%" of their intellectual work. On coding: "order of magnitude superior"—not a single hand objected. Response to every objection (privacy, ethics, cost): "I don't care. The advantage is too great." If the hard part (invisible cognition) is falling faster than expected, the urgency of the governance and infrastructure arguments increases.

"Humans in control, AI as reach" vs. "autonomous agents" is a framing choice with massive implications. The dominant enterprise AI frame is agent autonomy + guardrails. The second brain frame is human control + extended reach. These lead to completely different product architectures, governance models, and go-to-market narratives. Amodei's "Adolescence of Technology" essay argues AI trends toward full substitution rather than "human + tool." If he's right, the augmentation bet only wins for high-judgment work (the invisible 80%). Routine work (the visible 20%) gets substituted.

The cognitive stack reframes the entire agent conversation. Agents are claws (layers 4-5), not the brain (layer 2). The enterprise AI industry is building from the wrong end—you can't make task workers incrementally smarter until one of them figures out how to be the VP. No amount of investment at the bottom produces the cognitive layer where transformation actually happens. The question every enterprise AI strategy should start with: "who's building the brain layer?"

Model portability is proven, not theoretical. Claude was down one day. Switched to Gemini, pointed it at the same folder. Gemini had never seen these files before. Within minutes, fully functioning cognitive extension. Different personality, same capability. Demonstrated this on stage at DUCUG. The model is a pluggable component. The brain (the files, the skills, the context) is the durable asset. This is the strongest evidence yet that the second brain architecture is model-agnostic by design, not just in theory.

Multiple Citrix employees are already working this way. At least 15 people independently discovered second-brain-style AI usage. They weren't told to—the models reached a point where curious knowledge workers started finding it on their own. Several have wired their brains together via git submodules and shared folders. Communication happens brain-to-brain: dictate something, AI routes it to the right person's brain, their AI decides how to surface it. This is not a future scenario. It's live inside Citrix today. The fact that it emerged organically, without top-down direction, is the strongest validation of the worker-led adoption thesis.

"Legacy applications is like all of them." Once you're working through a cognitive extension, every traditional application becomes a compatibility layer. Word is for sending documents to colleagues who don't have brains yet. Excel is a file format AI reads directly. Outlook and Teams are messaging APIs. Even Keynote feels like an unnecessary translation layer. This isn't a theoretical position—it's a lived experience that multiple second brain users report independently.

Token economics are the emerging macro constraint. Three dimensions forming: (1) The consumer/enterprise pricing gap is structural and may be permanent—heavy users burn $200/month at consumer rates vs. 10x at enterprise pricing. (2) Token equivalency for human work is becoming calculable—your daily knowledge output has a token equivalent, and if 3M Sonnet tokens costs $400, you'd better be worth more than $400/day. (3) Model quality as class stratifier—who gets access to the frontier model? Government agencies on legacy models vs. executives on Opus. Not just token quantity but model quality as economic divide. The math will be done whether we like it or not.

"Software for one" is the next shadow IT crisis. NFX calls it "custom autonomous software." CNBC reporters built a Monday.com replacement in under an hour for $5-$15. Kevin Roose builds personal tools without coding. Workers aren't just using unsanctioned AI—they're building unsanctioned software. API costs down 90%, open-source models running locally, non-programmers shipping tools. None of this shows up in an app inventory. The second brain is the mature, structured version. The wild version (workers spinning up random tools) is entirely ungoverned.

The cost-cutting vs. innovation split is the macro frame for everything. Amodei identifies two corporate responses to AI: cost-cutting (replace workers) and innovation (expand capacity). These produce completely different customers, different governance needs, different workforce strategies. Innovation companies want governance that enables more AI safely. Cost-cutting companies want to reduce headcount and may not need governance at all. The 80/20 framework applies differently to each: cost-cutters automate the 20%, innovation companies augment the 80%.

The diffusion gap is the real urgency driver. Amodei's timeline: 1-2 years to "powerful AI" (Nobel-caliber, millions of instances, autonomous for weeks). The gap between "AI can do this" and "society/enterprises have adjusted" is where the damage happens. Previous technology waves had decades to diffuse. AI may have years. This compresses every planning assumption. The question isn't whether these changes are coming. It's whether the institutional and governance infrastructure is ready when they arrive.

The repo is the product, not the content. If BrianMadden.com was built today, it would be a GitHub repo. Books, newsletters, websites are packaging formats for human consumption. A forkable, queryable knowledge repo is the native format for AI-augmented consumption. brianmadden.ai is the proof of concept.

The coding-as-leading-indicator framework connects to adversarial brain testing. The five levels of AI-assisted knowledge work (from spicy search engine to dark knowledge factory) raise a verification question at Levels 4-5: how do you know the AI's work is any good? For code, the answer was behavioral tests stored separately from the codebase. For a subscribable brain, the answer is adversarial testing—a separate repo that runs challenge prompts, skeptical personas, and evaluation rubrics against the brain, with public results. Nobody is publicly stress-testing their own thinking with structured adversarial AI agents. The test suite can be open to anyone—fork it, write challenges, run them, submit the results. Intellectual discourse as structured, reproducible, version-controlled process.

New content formats are emerging from the brain's infrastructure. Brain diffs (weekly "what changed in my thinking" auto-generated from git commits) are a genuinely new content format—not a newsletter, a changelog for a worldview. Forked brains create intellectual lineage that git tracks automatically (where does your thinking diverge from mine? git diff). Brain-to-brain debates (two AIs load two brains, have a structured debate) produce a new kind of artifact. These aren't post-launch nice-to-haves—they're proof that subscribable brains create capabilities that don't exist in any other knowledge distribution format.

Knowledge is migrating to portable formats. The counter-thesis to platform lock-in: organizational knowledge is moving to portable, AI-native formats. Markdown files in git repos contain the actual working knowledge. Incumbent platform graphs become the metadata layer (who, when, permissions) while the knowledge layer moves to vendor-neutral formats. The progression: individual power users build second brains outside the dominant platform, teams do it, companies realize institutional knowledge lives in these systems, then the incumbent's data graph becomes increasingly incomplete.

Knowledge distillation as espionage vector. Fragments of public thoughts synthesize into what would represent sensitive strategic documents. AI can infer unpublished positions from patterns across published work. This creates a new adversarial dimension: what does a public brain reveal that the author wouldn't want competitors to know? The answer isn't to publish less—it's to be intentional about what compounds when synthesized.

Writing long-form for AI, not audience. The distribution isn't the article—it's the brain module. Write 20K words not for anyone to read but as an expert module for AI consumption. Others plug it into their brain and the frameworks weave into their thinking immediately. The future isn't "subscribe to my newsletter"—it's "subscribe to my expert module."

"This is not AGI." None of the evidence, none of the cost collapse data, none of the enterprise disruption requires AGI or ASI. Current models are already powerful enough. One more small iteration tick and demand explodes against fixed compute. The AGI debate is a distraction from the disruption already underway.

Every technology wave has a bottleneck, and it's never the technology itself. Factory electrification = workflow design. Web apps = rewriting. BYOD = governance. AI = the invisible 80%. The bottleneck is where value concentrates. Position yourself there.

Book publishing as knowledge transfer is dead. Fork the author's brain, tell your AI to incorporate it, their frameworks weave into your thinking immediately. Books were the best technology we had for transferring expertise. They're not anymore.

The specification bottleneck is the emerging economic constraint. When building costs nothing, spec quality collapses. The cost of building historically acted as a filter on specification quality—if building is expensive, organizations invest in defining what they want. Remove the cost and the filter disappears. You can now build the wrong thing at unprecedented speed. CodeRabbit analysis (470 GitHub PRs): AI-generated code produces 1.7x more logic issues. METR study: experienced developers 19% slower with AI but believed they were 24% faster. The scarce resource shifts from production to specification—knowing what to build. This maps directly to the coding-as-leading-indicator framework: at Levels 4-5, the human's job is specification and evaluation. Both require deep domain understanding.

Management is an emergent property of intelligence coordinating at scale. Three independent AI systems (Cursor agents, StrongDM's Software Factory, Anthropic's agent teams) converged on hierarchical management structures without being designed to. Hierarchy isn't a human organizational choice imposed on systems to maintain control—it's what intelligence does when it needs to coordinate. The agent-to-human ratio question replaces headcount planning. Revenue per employee at AI-native companies (Cursor, Midjourney, Lovable) runs 5-7x traditional SaaS. Not because they found better people—because their people orchestrate agents instead of doing execution.

What I'm unsure about

Scratchpad

Things I don't want to lose but that don't need their own file yet.

How this file works

This is the living part of brianmadden.ai. It updates as my thinking evolves. The commit history shows the evolution in real time. If you're loading brianmadden.ai into your AI, this file tells you where I'm heading, not just where I've been. The gap between this file and me/published-thinking.md is where the interesting work is.

This content is from brianmadden.ai—Brian's AI-native knowledge module. View source on GitHub.