Future Brent – A Mental Model: A 1% Nudge Toward a Kinder Tomorrow

December 9, 2025December 9, 2025 / lbhuston / Leave a comment

On Not Quite Random, we often wander through the intersections of the personal and the technical, and today is no different. Let me share with you a little mental model I like to call “Future Brent.” It’s a simple yet powerful approach: every time I have a sliver of free time, I ask, “What can I do right now that will make things a little easier for future Brent?”

ChatGPT Image Dec 9 2025 at 10 23 45 AM

It’s built on three pillars. First, optimizing for optionality. That means creating flexibility and space so that future Brent has more choices and less friction. Second, it’s about that 1% improvement each day—like the old adage says, just nudging life forward a tiny bit at a time. And finally, it’s about kindness and compassion for your future self.

Just the other day, I spent 20 minutes clearing out an overcrowded closet. That little investment meant that future mornings were smoother and simpler—future Brent didn’t have to wrestle with a mountain of clothes. And right now, as I chat with you, I’m out on a walk—because a little fresh air is a gift to future Brent’s health and mood.

In the end, this mental model is about blending a bit of personal reflection with a dash of practical action. It’s a reminder that the smallest acts of kindness to ourselves today can create a more flexible, happier, and more empowered tomorrow. So here’s to all of us finding those little 1% opportunities and giving future us a reason to smile.

Hybrid Work, Cognitive Fragmentation, and the Rise of Flow‑Design

December 8, 2025 / lbhuston / Leave a comment

Context: Why hybrid work isn’t just a convenience

Hybrid work isn’t a fringe experiment anymore — it’s quickly becoming the baseline. A 2024–25 survey in the U.S. shows that 52% of employees whose jobs can be remote work in a hybrid mode, and another 27% are fully remote.

Other recent studies reinforce the upsides: hybrid arrangements often deliver similar productivity and career‑advancement outcomes as fully on-site roles, while improving employee retention and satisfaction.

Redheadcoffee

In short: hybrid work is now normal — and that normalization brings new challenges that go beyond “working from home vs. office.”

The Hidden Cost: Cognitive Fragmentation as an Engineering Problem

When organizations shift to hybrid work, they often celebrate autonomy, flexibility, and freedom from commutes. What gets less attention is how hybrid systems — built around multiple apps, asynchronous communication, decentralized teams, shifting time zones — cause constant context switching.

Each time we jump from an email thread to a project board, then to a chat, then to a doc — that’s not just a change in window or tab. It is a mental task switch.
Such switches can consume as much as 40% of productive time.
Beyond lost time, there’s a deeper toll: the phenomenon of “attention residue.” That’s when remnants of the previous task linger in your mind, degrading focus and decreasing performance on the current task — especially harmful for cognitively demanding or creative work.

If we think about hybrid work as an engineered system, context switching is a kind of “friction” — not in code or infrastructure, but in human attention. And like any engineering problem, friction can — and should — be minimized.

Second‑Order Effects: Why Cognitive Fragmentation Matters

Cognitive fragmentation doesn’t just reduce throughput or add stress. Its effects ripple deeper, with impacts on:

Quality of output: When attention is fragmented, even small tasks suffer. Mistakes creep in, thoughtfulness erodes, and deep work becomes rare.
Long-term mental fatigue and burnout: Constant switching wears down cognitive reserves. It’s no longer just “too much work,” but “too many contexts” demanding attention.
Team performance and morale: At the organizational level, teams that minimize context switching report stronger morale, better retention, and fewer “after‑hours” overloads.
Loss of strategic thinking and flow states: When individuals rarely stay in one mental context long enough, opportunities for deep reflection, creative thinking, or coherent planning erode.

In short, hybrid work doesn’t just shift “where” work happens — it fundamentally alters how work happens.

Why Current Solutions Fall Short

There are many popular “help me focus” strategies:

The classic — Pomodoro Technique / “deep work” blocks / browser blockers.
Calendar-based time blocking to carve out uninterrupted hours.
Productivity suites: project/task trackers like Asana, Notion, Linear and other collaboration tools — designed to organize work across contexts.

And yet — these often treat only the symptoms, not the underlying architecture of distraction. What’s missing is a system‑level guidance on:

Mapping cognitive load across workflow architecture (not just “my calendar,” but “how many systems/platforms/contexts am I juggling?”).
Designing environments (digital and physical) that reduce cross‑system interference instead of piling more tools.
Considering second‑ and third‑order consequences — not just “did I get tasks done?” but “did I preserve attention capacity, quality, and mental energy?”

In other words: we lack a rationalist, engineered approach to hybrid‑work life hacking.

Toward Flow‑Preserving Systems: A Pareto Model of Attention

If we treat attention as a finite resource — and work systems as pipelines — then hybrid work demands more than discipline: it demands architecture. Here’s a framework rooted in the 80/20 (Pareto) principle and “flow‑preserving design.”

1. Identify your “attention vector” — where does your attention go?

List the systems, tools, communication modes, and contexts you interact with daily. How many platforms? How many distinct contexts (e.g., team A chat, team B ticket board, email, docs, meetings)? Rank them by frequency and friction.

2. Cull ruthlessly. Apply the 80/20 test to contexts:

Which 20% of contexts produce 80% of meaningful value? Those deserve high-bandwidth attention and uninterrupted time. Everything else — low‑value, context‑switch‑heavy noise — may be candidates for elimination, batching, or delegation.

3. Build “flow windows,” not just “focus zones.”

Rather than hoping “deep work days” will save you, build structural constraints: e.g., merge related contexts (use fewer overlapping tools), group similar tasks, minimize simultaneous cross-team demands, push meetings into consolidated blocks, silence cross‑context notifications when in flow windows.

4. Design both digital and physical environments for flow.

Digital: reduce number of apps, unify communications, use integrated platforms intelligently.
Physical: fight “always on” posture — treat work zones as environments with their own constraints.

5. Monitor second‑order effects.

Track not just output quantity, but quality, mental fatigue, clarity, creativity, and subjective well‑being. Use “collaboration analytics” if available (e.g., data on meeting load, communication frequency) to understand when fragmentation creeps up.

Conclusion: Hybrid Work Needs More Than Tools — It Needs Architecture

Hybrid work is now the baseline for millions of professionals. But with that shift comes a subtle and pervasive risk: cognitive fragmentation. Like a system under high load without proper caching or resource pooling, our brains start thrashing — switching, reloading, groggy, inefficient.

We can fight that not (only) through willpower, but through design. Treat your mental bandwidth as a resource. Treat hybrid work as an engineered system. Apply Pareto-style pruning. Consolidate contexts. Build flow‑preserving constraints. Track not just tasks — but cognitive load, quality, and fatigue.

If done intentionally, you might discover that hybrid work doesn’t just offer flexibility — it offers the potential for deeper focus, higher quality, and less mental burnout.

References

Great Place to Work, Remote Work Productivity Study: greatplacetowork.com
Stanford University Research on Hybrid Work: news.stanford.edu
Reclaim.ai on Context Switching: reclaim.ai
Conclude.io on Context Switching and Productivity Loss: conclude.io
Software.com DevOps Guide: software.com
BasicOps on Context Switching Impact: basicops.com
RSIS International Study on Collaboration Analytics: rsisinternational.org

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If this post resonated with you, and you’d like to support further writing like this — analyses of digital work, cognition, and designing for flow — consider buying me a coffee: Buy Me a Coffee ☕

* AI tools were used as a research assistant for this content, but human moderation and writing are also included. The included images are AI-generated.

When the Machine Does (Too Much of) the Thinking: Preserving Human Judgment and Skill in the Age of AI

December 1, 2025 / lbhuston / Leave a comment

We’re entering an age where artificial intelligence is no longer just another tool — it’s quickly becoming the path of least resistance. AI drafts our messages, summarizes our meetings, writes our reports, refines our images, and even offers us creative ideas before we’ve had a chance to think of any ourselves.

Convenience is powerful. But convenience has a cost.

As we let AI take over more and more of the cognitive load, something subtle but profound is at risk: the slow erosion of our own human skills, craft, judgment, and agency. This article explores that risk — drawing on emerging research — and offers mental models and methodologies for using AI without losing ourselves in the process.

SqueezedByAI3

The Quiet Creep of Cognitive Erosion

Automation and the “Out-of-the-Loop” Problem

History shows us what happens when humans rely too heavily on automation. In aviation and other high-stakes fields, operators who relied on autopilot for long periods became less capable of manual control and situational awareness. This degradation is sometimes called the “out-of-the-loop performance problem.”

AI magnifies this. While traditional automation replaced physical tasks, AI increasingly replaces cognitive ones — reasoning, drafting, synthesizing, deciding.

Cognitive Offloading

Cognitive offloading is when we delegate thinking, remembering, or problem-solving to external systems. Offloading basic memory to calendars or calculators is one thing; offloading judgment, analysis, and creativity to AI is another.

Research shows that when AI assists with writing, analysis, and decision-making, users expend less mental effort. Less effort means fewer opportunities for deep learning, reflection, and mastery. Over time, this creates measurable declines in memory, reasoning, and problem-solving ability.

Automation Bias

There is also the subtle psychological tendency to trust automated outputs even when the automation is wrong — a phenomenon known as automation bias. As AI becomes more fluent, more human-like, and more authoritative, the risk of uncritical acceptance increases. This diminishes skepticism, undermines oversight, and trains us to defer rather than interrogate.

Distributed Cognitive Atrophy

Some researchers propose an even broader idea: distributed cognitive atrophy. As humans rely on AI for more of the “thinking work,” the cognitive load shifts from individuals to systems. The result isn’t just weaker skills — it’s a change in how we think, emphasizing efficiency and speed over depth, nuance, curiosity, or ambiguity tolerance.

Why It Matters

Loss of Craft and Mastery

Skills like writing, design, analysis, and diagnosis come from consistent practice. If AI automates practice, it also automates atrophy. Craftsmanship — the deep, intuitive, embodied knowledge that separates experts from novices — cannot survive on “review mode” alone.

Fragility and Over-Dependence

AI is powerful, but it is not infallible. Systems fail. Context shifts. Edge cases emerge. Regulations change. When that happens, human expertise must be capable — not dormant.

An over-automated society is efficient — but brittle.

Decline of Critical Thinking

When algorithms become our source of answers, humans risk becoming passive consumers rather than active thinkers. Critical thinking, skepticism, and curiosity diminish unless intentionally cultivated.

Society-Scale Consequences

If entire generations grow up doing less cognitive work, relying more on AI for thinking, writing, and deciding, the long-term societal cost may be profound: fewer innovators, weaker democratic deliberation, and an erosion of collective intellectual capital.

Mental Models for AI-Era Thinking

To navigate a world saturated with AI without surrendering autonomy or skill, we need deliberate mental frameworks:

1. AI as Co-Pilot, Not Autopilot

AI should support, not replace. Treat outputs as suggestions, not solutions. The human remains responsible for direction, reasoning, and final verification.

2. The Cognitive Gym Model

Just as muscles atrophy without resistance, cognitive abilities decline without challenge. Integrate “manual cognitive workouts” into your routine: writing without AI, solving problems from scratch, synthesizing information yourself.

3. Dual-Track Workflow (“With AI / Without AI”)

Maintain two parallel modes of working: one with AI enabled for efficiency, and another deliberately unplugged to keep craft and judgment sharp.

4. Critical-First Thinking

Assume AI could be wrong. Ask:

What assumptions might this contain?
What’s missing?
What data or reasoning would I need to trust this?
This keeps skepticism alive.

5. Meta-Cognitive Awareness

Ease of output does not equal understanding. Actively track what you actually know versus what the AI merely gives you.

6. Progressive Autonomy

Borrowing from educational scaffolding: use AI to support learning early, but gradually remove dependence as expertise grows.

Practical Methodologies

These practices help preserve human skill while still benefiting from AI:

Personal Practices

Manual Days or Sessions: Dedicate regular time to perform tasks without AI.
Delayed AI Use: Attempt the task first, then use AI to refine or compare.
AI-Pull, Not AI-Push: Use AI only when you intentionally decide it is needed.

Team or Organizational Practices

Explain-Your-Reasoning Requirements: Even if AI assists, humans must articulate the rationale behind decisions.
Challenge-and-Verify Pass: Explicitly review AI outputs for flaws or blind spots.
Assign Human-Only Tasks: Preserve areas where human judgment, ethics, risk assessment, or creativity are indispensable.

Educational or Skill-Building Practices

Scaffold AI Use: Early support, later independence.
Complex, Ambiguous Problem Sets: Encourage tasks that require nuance and cannot be easily automated.

Design & Cultural Practices

Build AI as Mentor or Thought Partner: Tools should encourage reflection, not replacement.
Value Human Expertise: Track and reward critical thinking, creativity, and manual competence — not just AI-accelerated throughput.

Why This Moment Matters

AI is becoming ubiquitous faster than any cognitive technology in human history. Without intentional safeguards, the path of least resistance becomes the path of most cognitive loss. The more powerful AI becomes, the more conscious we must be in preserving the very skills that make us adaptable, creative, and resilient.

A Personal Commitment

Before reaching for AI, pause and ask:

“Is this something I want the machine to do — or something I still need to practice myself?”

If it’s the latter, do it yourself.
If it’s the former, use the AI — but verify the output, reflect on it, and understand it fully.

Convenience should not come at the cost of capability.

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References

Macnamara, B. N. (2024). Research on automation-related skill decay and AI-assisted performance.
Gerlich, M. (2025). Studies on cognitive offloading and the effects of AI on memory and critical thinking.
Jadhav, A. (2025). Work on distributed cognitive atrophy and how AI reshapes thought.
Chirayath, G. (2025). Analysis of cognitive trade-offs in AI-assisted work.
Chen, Y., et al. (2025). Experimental results on the reduction of cognitive effort when using AI tools.
Jose, B., et al. (2025). Cognitive paradoxes in human-AI interaction and reduced higher-order thinking.
Kumar, M., et al. (2025). Evidence of cognitive consequences and skill degradation linked to AI use.
Riley, C., et al. (2025). Survey of cognitive, behavioral, and emotional impacts of AI interactions.
Endsley, M. R., Kiris, E. O. (1995). Foundational work on the out-of-the-loop performance problem.
Research on automation bias and its effects on human decision-making.
Discussions on the Turing Trap and the risks of designing AI primarily for human replacement.
Natali, C., et al. (2025). AI-induced deskilling in medical diagnostics.
Commentary on societal-scale cognitive decline associated with AI use.

* AI tools were used as a research assistant for this content, but human moderation and writing are also included. The included images are AI-generated.

Build Systems for Your Worst Days, Not Your Best

November 3, 2025 / lbhuston / Leave a comment

I’ve had those days. You know the ones: back-to-back meetings, your inbox growing like a fungal bloom in the dark, and just a single, precious hour to get anything meaningful done. Those are the days when your tools, workflows, and systems either rise to meet the challenge—or collapse like a Jenga tower on a fault line.

And that’s exactly why I build systems for my worst days, not my best ones.

When You’re Running on Fumes, Systems Matter Most

It’s easy to fall into the trap of designing productivity systems around our ideal selves—the focused, energized version of us who starts the day with a triple espresso and a clear mind. But that version shows up maybe one or two days a week. The other days? We’re juggling distractions, fighting fatigue, and getting peppered with unexpected tasks.

Those are the days that test whether your systems are real or just aspirational scaffolding.

My Systems for the Storm

To survive—and sometimes even thrive—on my worst days, I rely on a suite of systems I’ve built and refined over time:

Custom planners for project, task, and resource tracking. These keep my attention on the highest-leverage work, even when my mind wants to wander.
Pre-created GPTs and automations that handle repetitive tasks, from research to analysis. On a rough day, this means things still get done while I conserve cognitive bandwidth.
Browser scripts that speed up form fills, document parsing, and other friction-heavy tasks.
The EDSAM mental model helps me triage and prioritize quickly without falling into reactive mode. (EDSAM = Eliminate, Delegate, Simplify, Automate, Maintain)
A weekly review process that previews the chaos ahead and lets me make strategic decisions before I’m in the thick of it.

These aren’t just optimizations—they’re insulation against chaos.

The Real ROI: More Than Just Productivity

The return on these systems goes well beyond output. It’s about stress management, reduced rumination, and the ability to make clear-headed decisions when everything else is fuzzy. I walk into tough weeks with more confidence, not because I expect them to be easy—but because I know my systems will hold.

And here’s something unexpected: these systems have also amplified my impact as a mentor. By teaching others how I think about task design, tooling, and automation, I’m not just giving them tips—I’m offering frameworks they can build around their own worst days.

Shifting the Culture of “Reactive Work”

When I work with teams, I often see systems built for the ideal: smooth days, few interruptions, time to think. But real-world conditions rarely comply. That’s why I try to model and teach the philosophy of resilient systems—ones that don’t break when someone’s sick, a deadline moves up, or a crisis hits.

Through mentoring and content, I help others see that systems aren’t about rigidity—they’re about readiness.

The Guiding Principle

Here’s the rule I live by:

“The systems have to make bad days better, and the worst days minimally productive—otherwise, they need to be optimized or replaced.”

That sentence lives in the back of my mind as I build, test, and adapt everything from automations to mental models. Because I don’t just want to do great work on my best days—I want to still do meaningful work on my worst ones.

And over time, those dividends compound in ways you can’t measure in a daily planner.

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* AI tools were used as a research assistant for this content, but human moderation and writing are also included. The included images are AI-generated.

Investing in Ambiguity: A Portfolio Framework from AGI to Climate Hardware

October 6, 2025October 6, 2025 / lbhuston / Leave a comment

Modern deeptech investing often feels like groping in the dark. You’re not simply picking winners — you’re modeling futures, coping with extreme nonlinearity, and forcing structure on chaos. The research I’ve conducted in this area has been revealing. Below, I reflect on it, extend a few ideas, and flesh out how one might operationalize it in a venture or research‑lab context.

MacModeling

A. The Core Logic: Inputs → Levers → Outputs

At the heart of the structure is a clean mapping:

Inputs: budget, time horizon, risk tolerance, domain constraints, and a pipeline of opportunities.
Levers: probability calibration, tranche sizing (how much per bet), stage gating, diversification, optionality.
Outputs: expected value (EV), EV density (time‑adjusted), capital at risk, downside bounds, and the resulting portfolio mix.

That’s beautiful. It forces you to treat capital as fungible, time as a scarce and directional resource, and uncertainty as something you can steer—not ignore.

Two design observations:

Time matters not just via discounting but via the density metric (EV per time), which encourages front‐loading or fast pivots.
Risk budgeting isn’t just “don’t lose everything” — you allocate downside constraints (e.g. CaR95) and concentration caps. That enforces humility.

In practice, you’d want this wired into a rolling dashboard that updates “live” as bets progress or stall.

B. The Rubric: Scoring Ideas Before Modeling

Before you even build outcome models, you triage via a weighted rubric (0–5 scale). The weights:

Dimension	Weight
Team quality	0.15
Problem size / TAM	0.10
Moat / defensibility	0.10
Path to revenue / de-risked endpoint	0.15
Evidence / traction / data / IP	0.15
Regulatory / operational complexity (inverted)	0.10
Time to liquidity / cash generation	0.10
Strategic fit / option value	0.15

You set a gate: proceed only if rubric ≥ 3.5/5.

The beauty: you make tacit heuristics explicit. You prevent chasing “cool but far-fetched” bets without grounding. Also, gating early keeps your modeling burden manageable.

One adjustment: you might allow “strategic fit / option value” to have nonlinear impact (e.g. a bet’s optionality is worth a multiplier above a linear score). That handles bets that act as platform gambles more than standalone projects.

C. Modeling Metrics & Formulas

Here’s how the framework turns score + domain judgment into outputs:

EV (expected value) = ∑[p_i × PV(outcome_i)] − upfront_cost
PV: discount cashflows by rate r. For one‑off outcomes, PV = cashflow × (1+r)^(−t). For annuities, use the standard annuity PV factor, then discount to start.
EV per dollar = EV / upfront_cost
EV density = (EV per dollar) / expected_time_to_liquidity
Capital at Risk (CaR_α) = the loss threshold L such that P(loss ≤ L) ≥ α (e.g. α = 95%)
Tranche sizing (fractional‑Kelly proxy):
With payoff multiple b = (payoff / cost) − 1, and success prob p, failure prob q = 1 − p, the “ideal” fraction f* = (b p − q)/b. Use a conservative scale (25–50% of f*) to avoid overbetting.
Diversification constraints: no more than 20–30% of portfolio EV in any one thesis; target ≥ 6 independent bets if possible.

You also run Monte Carlo simulations: randomly sample outcomes for each bet (across, say, 10,000 portfolio replications) to estimate return distributions, downside percentiles, and verify your CaR95 and concentration caps.

This gives a probabilistic sanity check: even if your point‐model EV is seductive, the tails often bite.

D. The Worked Case Studies

Here are three worked examples (AGI tools, biotech preclinical therapeutic, and climate hardware pilot) to illustrate how this plays out concretely. I’ll briefly recast them with commentary.

1. AGI Tools (Internal SaaS build)

Cost: $200,000
r = 12%
3‑year annuity starting year 1
Outcomes: High / Medium / Low / Fail, with assigned probabilities
You compute PVs, then EV_gross = ~1,285,043; EV_net = ~1,085,043
EV per $ = ~5.425
EV density = ~10.85 / year
Using a fractional Kelly proxy you suggest allocating ~10% of risk budget.

Reflections: This is the kind of “shots on goal” gambit that high EV density encourages. If your pipeline supports multiple parallel AGI tooling bets, you can diversify idiosyncratic risk.

In real life, you’d want more conservative assumptions around traction, CAC payback, or re‑investment risk, but the skeleton is sound.

2. Biotech (Preclinical therapeutic)

Cost: $5,000,000
r = 15%
Long time horizon: first meaningful exit in year 3+
Outcomes: Phase 1 licensing, Phase 2 sale, full approval, or fail
EV_gross ≈ $10.594M → EV_net ≈ $5.594M
EV per $ ≈ 1.119
EV density ≈ 0.224 per year

Here, the low EV density, combined with a long duration and regulatory risk, justifies capping the allocation (e.g., ≤15%). This is consistent with how deep biotech bets behave in real funds: they offer huge upside, but long tails and binary risks dominate.

One nuance: because biotech outcomes are highly correlated (regulatory climates, volatility in drug approval regimes), you’d probably treat these bets as partially dependent. The diversification constraint must consider correlation, not just EV share.

3. Climate Tech Hardware Pilot

Cost: $1,500,000
r = 12%, expected liquidity ~3 years
Outcomes: major adoption, moderate, small licensing, or fail
EV_gross ≈ $2,614,765 → EV_net ≈ $1,114,765
EV per $ ≈ 0.743
EV density ≈ 0.248 per year

This is a middling bet: lower EV per cost, moderate duration, moderate outcome variance. It might function as a “hedge” or optionality play if you think climate tech valuations will re‑rate. But by itself, it likely wouldn’t dominate allocation unless you believe upside outcomes are undermodeled.

E. Sample Portfolio & Allocation Rationale

Consider the following:

You propose a hypothetical portfolio with $2M budget, moderate risk tolerance:

AGI tools: 6 parallel shots at $200k each = $1.2M
Climate pilot: a $800k first tranche with gate to follow-on
Biotech: monitored, no initial investment yet unless cofunding improves terms

Why this mix?

The AGI bets dominate in EV density and diversification; you spread across six distinct bets (thus reducing idiosyncratic risk).
The climate pilot offers an optional upside and complements your domain exposure (if you believe climate tech is underinvested).
The biotech bet is deferred until you can get more favorable terms or validation.

You respect concentration caps (no single thesis has > 20–30% EV share) while leaning toward bets with the highest time‐adjusted return.

F. Stage‑Gate Logic & Kill Criteria

Crucial to managing this model is a disciplined stage‑gate roadmap:

Gate 0 → 1: due diligence, basic feasibility check
Gate 1 → 2: early milestone (e.g. pilot, LOIs, KPIs)
Thereafter, gates tied to performance, pivot triggers, or partner interest

Kill criteria examples:

Miss two technical milestones in a row
CAC : LTV (or unit economics) fall below threshold
Regulatory slippage > 2 cycles without new positive evidence
Correlated downside shock across multiple bets triggers a pause

By forcing kill decisions rather than letting sunk cost inertia dominate, you preserve optionality to reallocate capital.

G. Reflections & Caveats

Calibration is the weak link. The EV and tranche logic depend heavily on your probability estimates and payoff assumptions. Mistakes in those propagate. Periodic Bayesian updating and calibration should be baked in as a feedback loop.
Correlation & regime risk. Deeptech bets are rarely independent — regulatory cycles, capital markets, macro shocks, or paradigm shifts can hit many bets simultaneously. Make sure your Monte Carlo simulation simulates correlation regime shocks, not just independent draws.
Optionality is more than linear EV. Some bets serve as “platform enablers” (e.g. research spinouts) whose value multiplies in ways not captured in simple discounting. Make sure you allow for a structural “option value” that escapes linear EV.
Time & capital liquidity friction. You may find you must pause follow-ons or reallocate capital midstream; your framework must be tolerant of “liquidity timing mismatch.”
Behavioral failure modes. Decision fatigue, emotional attachment to ideas, or reluctance to kill projects can erode discipline. A formal governance process—perhaps an independent review committee—helps.

H. Suggested Enhancements & Next Steps

Dashboard & real‑time monitoring: build a tool (in Notion, Google Sheets + Python, or custom UI) that ingests actual metrics (KPIs, burn, usage) and compares them to model expectations.
Bayesian updating module: as you observe results, update posterior probabilities and EV estimates.
Scenario overlay for regime risk: e.g. a “recession / capital drought” stress model.
Meta‑portfolio of strategies: e.g. combining “fast bets” (high EV density) with “venture options” (lower density but optional upside).
Decision governance & kill review cycles: schedule quarterly “kill / pivot reviews” where chosen bets are reassessed relative to alternatives.

I. Conclusion

This framework is so much more than a spreadsheet—it’s a philosophically coherent approach to venture investing in environments of radical uncertainty. It treats bets as probabilistic options, forces structure around allocation and kill decisions, and lets time-adjusted return (density) fight for primacy over naive upside.

I’d say the real acid test is: run it live. Drop in your real pipeline, score the opportunities, simulate your portfolio, place small bets, and see what your tail risks and optionalities teach you over five quarters.

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* AI tools were used as a research assistant for this content, but human moderation and writing are also included. The included images are AI-generated.

Seizing Career Leverage by Building a Body of Public Work

September 30, 2025 / lbhuston / Leave a comment

On the surface, it may seem easier to pursue another certificate, add another line to your resume, or polish a few more LinkedIn keywords. That’s the default advice. But I’ve found that the true differentiator—the thing that has consistently opened the most doors in my career and in the lives of those I mentor—is something less talked about: building a public body of work.

ThinkingPlanning

For me, it didn’t start with a strategic master plan. It was organic. A blog here. A talk there. Over time, though, the pattern became clear. The more consistently I created public work—writings, talks, podcasts, code, experiments—the more serendipity showed up. People would reach out. Ideas would flow. And opportunities would emerge.

Creating in public does something powerful: it makes you discoverable. It turns your ideas into tiny relationship builders scattered across the internet. They work quietly on your behalf—sharing, connecting, and engaging. They let people find you not just for who you say you are, but for what you actually do and think and build. In essence, your work becomes your calling card.

Kevin Kelly wrote about the concept of 100 True Fans, and I think that framework applies here, too. When you create with consistency and intention, your work resonates. People engage. They share. They connect. You become a node in a larger network. Not geographically constrained. Not bound to a title. But influential because of contribution.

Of course, this isn’t easy. If it were, everyone would be doing it.

The resistance is deep and evolutionary. When you make something public—your ideas, your interests, your perspective—you draw attention to yourself. You leave the crowd. And for most of human history, that was dangerous. Our lizard brains still think it is.

But here’s the truth: life happens at the edges. It happens when you step away from the herd and choose to teach, lead, explore, or question. That’s where the value is—not just in terms of career growth, but in living a more interesting life.

The tools to get started are easier than ever. A blog costs nothing but time and focus. A podcast is within reach with a decent mic and an internet connection. A video or short-form tutorial can find thousands of eyes in hours. The barrier isn’t access. It’s courage. And then—discipline.

There won’t be a singular moment where you “make it.” Instead, you’ll find momentum. The blog post you wrote last year still gets read. The talk you gave finds its way to someone’s inbox. The experiment you published helps someone else start their own.

But here’s the trick: create to help. Self-serving content evaporates quickly. But service-oriented content—something that teaches, guides, explores—can live on. Sometimes for years. Sometimes forever.

And perhaps most important: you get to choose what you create. That’s a kind of creative sovereignty many professionals never tap into. It’s a superpower. And like any superpower, it comes with responsibility.

So here’s what I tell my mentees:

Actions speak louder than words. A portfolio is more potent than a certificate on your resume.

Teach courage. Encourage contribution. Show them that real growth—personal, professional, even spiritual—happens at the edges. Not in the safe middle.

Put your work into the world. Let it work for you. And help others as you do. That’s how you build a life and career that’s not just successful, but truly extraordinary.

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* AI tools were used as a research assistant for this content, but human moderation and writing are also included. The included images are AI-generated.

When Your Blender Joins the Blockchain

September 25, 2025 / lbhuston / Leave a comment

It might sound like science fiction today, but the next ten years could make it ordinary: your blender might mix your perfect cocktail, then—while you sleep—lend its spare compute cycles to a local bar’s supply-chain optimizer. In exchange, you’d get rewarded for the electricity and resources your device contributed. Scale this across millions of homes and suddenly the world looks very different. Every house becomes a miniature data center, woven into a global fabric of computing power.

Privacy First

One of the most immediate wins of pushing AI inference to the edge is privacy. By processing data locally, devices avoid shipping raw information back to centralized servers where it becomes a high-value target. Dense data lakes are magnets for attackers because a single compromise yields massive returns. Edge AI reduces that density, scattering risk across countless smaller nodes. It’s harder to attack everyone’s devices than it is to breach a single hyperscale database.

This isn’t just theory—it’s a fundamental shift. Edge computing changes the economics of data theft. Attacks that once had high return on investment may no longer be worth the effort.

Consensus as a Truth Filter

Consensus networks add another dimension. We already know them as the backbone of blockchain, but in the context of distributed AI, they become something else: a truth filter. Imagine multiple edge nodes each running inference on the same prompt. Instead of trusting a single output, the network votes and distills multiple responses into an accepted answer. The extra cost in latency is justified when accuracy matters—medical diagnostics, financial decisions, safety-critical automation.

For lower-stakes tasks—summaries, jokes, quick recommendations—the system can scale back, trading consensus depth for speed. Over time, AI itself will learn to decide how much verification is required for each task.

Incentives and Resource Markets

The second wave of opportunity is in incentives. Idle devices represent untapped capacity. Consensus networks paired with smart contracts can manage marketplaces for these resources, rewarding participants when their devices contribute compute cycles or model updates. The beauty is that markets—not committees—decide what form those rewards take. Tokens, credits, discounts, or even service-level benefits can evolve naturally.

The result is a world where your blender, your TV, your thermostat—all ASIC-equipped and AI-capable—become not just appliances, but contributors to your digital economy.

Governance Inside the Network

Who sets the rules in such a system? Traditional standards bodies may not keep up. Here, governance itself can become part of the consensus. Users and communities establish rules through smart contracts and incentive structures, punishing malicious behavior and rewarding cooperation. This is governance baked directly into the infrastructure rather than layered on top of it.

Risks and Controls

The risks are obvious. Energy consumption, gaming the incentive systems, malicious actors poisoning updates, and threats we can’t even perceive yet. But here is where distributed control matters most. Huston’s Postulate tells us that controls grow stronger the closer they are—logically or physically—to the assets they protect. Embedding controls across a mesh of devices, coordinated by consensus and smart contracts, creates resilience that a single central gatekeeper can never achieve.

The Punchline

One day, your blender may make the perfect cocktail, make money for you when it’s idle, and contribute to a global wealth of computing resources. Beginning to see our devices as investments—tools that not only serve us directly but also join collective systems that benefit others—may be the real step forward. Not a disruption, but an evolution, shaping how intelligence, value, and trust flow through everyday life.

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* AI tools were used as a research assistant for this content, but human moderation and writing are also included. The included images are AI-generated.

n=1: Living as a Person of Your Time

September 24, 2025 / lbhuston / Leave a comment

There’s a strange, powerful truth that often goes unsaid: most of our success, failure, identity, even relevance — is bound to the era in which we’re born.

I was born at a time that happened to align with the rise of the personal computer, the evolution of networking, and the early waves of the Internet. I grew up alongside it. My teenage years were filled with bulletin boards and local area networks, and by the time I entered the workforce, the digital transformation had begun. The timeline fit. The wind was at my back.

Entrepreneurship found me early too. I hit my stride during the explosion of multi-level marketing and the rise of the self-help scene. Those environments — flawed and messy as they were — gave me tools: confidence in public speaking, an understanding of social persuasion, and most of all, a belief that being different could be powerful. Even pro wrestling played its part. It taught me about persona — the value of a character who stands out and leans in.

These experiences weren’t universal. They were specific to my time. My life is a living experiment with a sample size of one — n=1.

ChatGPT Image Sep 24 2025 at 04 14 15 PM

Timeless Wisdom vs. Timely Application

I’ve always had mentors. A supportive family. A spouse who stands by me. And I’ve drawn heavily from Stoicism and spiritual teachings that have endured for centuries. But I don’t mistake timeless wisdom for universal utility.

What worked for Marcus Aurelius or even my own mentors doesn’t always work here, now, for me. That’s why nearly every major move I’ve made — in business, in life — has been driven by experimentation. Scientific method. Trial and error. Observing, adjusting, iterating. Always adjusting for context.

I hunt for asymmetry: small bets with big upsides. And I often use a barbell strategy — thank you, Ray Dalio — allocating the bulk of my resources into stable, known returns while reserving the rest for moonshots. Life, like any investment portfolio, is about managing risk exposure.

And I do it all as asynchronously as possible. Not just in how I work, but in how I think. Time is a tool. I refuse to be trapped by the tyranny of the immediate.

Lessons That Don’t Translate

If I had been born twenty years earlier, I might have missed the digital wave entirely. Or maybe I would have found a different current — maybe mainframes or military networks. If I were born twenty years later, I might have missed the golden age of early web entrepreneurship, but perhaps mobile and app ecosystems would have taken its place.

That’s the point. What worked for me worked because of my timeline. But it might not work for anyone else — even if it looks appealing from the outside.

That’s why I’m cautious about what I try to pass on. I don’t offer a playbook. I offer tools. Mental models. Systems thinking. Frameworks that others can adapt and test for themselves. And I encourage every single person to apply n=1 experimentation to those tools. Because the context in which you live matters just as much — or more — than the tool you use.

Legacy Without Monuments

When my time is up, I don’t need monuments. I’m not chasing statues or street names.

What I do hope for is simpler, quieter. I hope that others see my life as one lived with compassion, generosity, and love. I hope they learn from what I’ve tried, and test those learnings against their own lives. I hope they make better decisions, kinder impacts, smarter plays.

I hope they live their own n=1 experiment, tuned to their time, their truth.

Because the only real legacy is what echoes forward in the lives of others — not through imitation, but through adaptation.

* AI tools were used as a research assistant for this content, but human moderation and writing are also included. The included images are AI-generated.

From Tomorrow to Today: Making Futurism Tangible in Your Daily Routine

September 9, 2025 / lbhuston / Leave a comment

Futurism often feels like an ethereal daydream—grand, inspiring, but distant. Bold predictions about 2040 stir our imaginations, yet they rarely map into our Monday mornings. Here at notquiterandom.com, I’m proposing a subtle shift: what if we harness those futuristic visions and anchor them in our 2025 daily habits? This is practical futurism in action—turning forecasts into small, meaningful steps we can take now.

Idea

The Disconnect: Why Futurism Feels Abstract

Futurism often lives in abstraction: TED talks and futurology books project us forward—yet too often, they’re unmoored from our present experiences.
Technology predictions feel lofty, not livable: We talk AI, distributed computing, or extended reality—but rarely consider how they’ll shape our morning routines, grocery runs, or mid-day breaks in the near term.
Audience craving near-term relevance: Tech-savvy professionals, committed yet pragmatic, want today’sutility—not just speculation about 2040.

What’s Missing: Bridging Forecast with Habit

The gap lies in translation—how do we take big-picture forecasts and convert them into rational, actionable daily practices? It’s not enough to know that “AI will transform everything”—we need to know how it can help us, say, stop overthinking, streamline our routines, or fuel better decision-making today.

Learning from Others: What Works, and Why It’s Still Too Vague

Future-self mentoring: A Medium article suggests asking your “future self” for advice—pragmatic, reflective, and personal.
Habit stacking for incremental change: Insert new habits into existing ones—an early morning walk after brushing your teeth, for instance.
AI as daily assistant: From summarizing Zoom calls to smart recipe creation, these are mini-futures we can live now.

But even these are one-offs rather than a cohesive method. What if there were a structured approach for individuals to act on futurism—not tomorrow, but today?

Core Pillars: Building Practical Futures in 2025

1. Flip 2040 Predictions into 2025 Micro-Actions

Take a prediction—say, “AI-enabled personalization everywhere by 2040”—and turn it into steps:

Experiment with AI tools that tailor your workout or meal plan (like those that adapt to mood or leftovers).
Automate a routine task you dread—like using AI to summarize meetings.
These are small bets that reflect future trends in digestible chunks for today.

2. Scenario Planning—For You, Not Just Companies

Rather than corporate foresight, create a mini “personal scenario plan”:

Optimistic 2025: AI helps you shave hours off your weekday.
Constrained 2025: Tight budgets—but you rely on low-cost hacks and habit stacks.
Hybrid 2025: A mix—automated routines and soulful analog rituals share your day.
Plan habits that thrive in each scenario.

3. The “Small Bets” Approach

Reed habit stacking into futurism:

Choose one futuristic habit (e.g., AI-curated learning podcast during walks).
Run a low-stakes trial—maybe one week.
Reflect: Did it help? Discard, tweak, or embed.
This mimics how entrepreneurs iterate and adapts futurism into a manageable experiment.

Illustrative Mini-Plan: Futurism Meets the Morning Routine

Habit Stack: After brushing teeth, open AI habit tracker that suggests personalized micro-tasks (breathing, brief learning, stand-up stretch).
Try the 2-Minute Trick: Commit to two minutes of something high-tech or future-oriented—like checking that AI tracker—then see if you naturally continue.
Future-Self Check-In: End the day by journaling a quick note: “If I were living in 2040, how would my present behavior differ?”

These micro-actions fuse futurism with routine, making tomorrow’s edge realities feel like tomorrow’s baseline.

Why It Resonates with notquiterandom Readers

Our audience—rooted in tech awareness, skeptical optimism, and personal agency—wants integrity, not hype. This blend of grounded futurism and reflective practice aligns with:

Professional curiosity
Self-directed experimentation
Meaningful progress framed as actionable—no grand leaps, just deliberate stepping stones

Conclusion: Begin Your 2025 Future Habit

The future doesn’t have to be a distant horizon—it can be woven into your habits now. Start small. Let habit stacking, mini-scenarios, and future-self reflection guide you. Over time, these microscale engagements seed long-term adaptability and readiness.

Your Turn

Ready to design your first micro-bet? Whether it’s a futuristic habit stack, an AI tool tryout, or a scenario exercise, share your experiment. Let’s co-create real futures, one habit at a time.

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* AI tools were used as a research assistant for this content, but human moderation and writing are also included. The included images are AI-generated.

The Coming Collision of Quantum, AI, and Blockchain

September 3, 2025 / lbhuston / Leave a comment

I’ve been spending a lot of time lately thinking about what happens when three of the most disruptive technologies on our radar—quantum computing, artificial intelligence, and blockchain—don’t just mature, but collide. Not in isolation, not as separate waves of change, but as a single force of transformation. I’ve come to believe this collision may alter our global systems more profoundly than the Internet ever did, and even more than AI is doing on its own today.

More Than the Sum of the Parts

Each of these technologies is already disruptive. Quantum promises computational power orders of magnitude beyond anything we can imagine today. AI is rapidly reshaping how we create, work, and decide. Blockchain has redefined ownership, trust, and verification.

But imagine them intertwined. AI powered by quantum computing. Identities and financial transactions rooted in shared blockchains, public and private. Blockchain as the arbiter of identity, of non-repudiation, of who we are and what we’ve agreed to. Smart contracts enhanced by AI that can generate, adjust, and arbitrate terms on the fly. Quantum cryptography woven into blockchains that operate at scales and speeds impossible with today’s systems. AI itself acting as the oracle for contracts, feeding real-time insights into automated agreements.

That’s not incremental progress—that’s tectonic shift.

Systems That Won’t Survive the Collision

Some sectors will feel the tremors first. Finance is obvious, even without the collision. Add in these forces together and you have leverage points that could reset the foundations of how money moves, how markets behave, and how trust is established.

Healthcare, defense, and governance won’t look the same either. Identity frameworks built on quantum-secure blockchains could redefine everything from medical records to voting. Critical infrastructure may evolve to the point where the old approaches don’t make sense anymore—financially, socially, or technologically.

And overlay it all with quantum AI: an intelligence capable of holding vast landscapes of knowledge and spinning out probable solutions to nearly any problem, no matter the complexity. That’s not science fiction—it’s a future horizon. Maybe not tomorrow, maybe not in five years, but possibly in my lifetime.

The Double-Edged Sword

I’m not naive about the risks. All swords cut both ways. Bad actors will find ways to exploit these systems. Tyranny won’t vanish, even in a world of shared prosperity. People are driven by power, and that’s unlikely to change.

But the upside is massive. For emerging economies especially, these collisions could level the field, bringing access, transparency, and efficiency that the old systems have long denied. If global prosperity rises, maybe some incentives for malicious behavior diminish.

Early Sparks and Long Horizons

We’ll see hints and echoes of this in the next decade. Experiments, prototypes, niche applications that give us glimpses of the possible. But the real shifts, the agricultural-revolution-scale changes, may sit 20 to 30 years out. If that horizon holds true, the world my grandchildren inherit will be unrecognizable in ways both challenging and awe-inspiring.

Looking Ahead

I don’t claim to have the answers. What I have is a sense that the collision of quantum, AI, and blockchain is not just coming—it’s inevitable. And when it hits, it will be bigger than the sum of the parts. Bigger than the Internet. Maybe even bigger than the scientific revolution itself.

For now, the best we can do is pay attention, experiment responsibly, and prepare ourselves for a future where the unimaginable becomes the baseline.

Supporting My Work

If you found this useful and want to help support my ongoing research into the intersection of cybersecurity, automation, and human-centric design, consider buying me a coffee:

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* AI tools were used as a research assistant for this content, but human moderation and writing are also included. The included images are AI-generated.