This is the first post in a multi-part series. The intention is to track growth with AI tools such as Claude Cowork. Hopefully, I will look back at this and think - how basic!

There’s a particular kind of low-grade stress that rarely makes it into the self-help canon. It’s not the deadline panic, the career anxiety, or the leadership pressure. It’s quieter — the constant background hum of wondering if you’re missing something. When I think of the sources of ambient angst — doomscrolling news cycles, glued to stock prices, worries about kids, family, health, diet, exercise, life — the one that underlies all of them is missing an important task.

I think it’s a natural progression to take on more as we grow into the prime of our lives. In my case, my to-do lists have evolved from solely focusing on career progression. They now include numerous businesses, ventures, projects like co-chairing VALUE Cville (an investing society), hosting a podcast, writing this Substack, and, importantly, family / kids, health, trip planning, and all the other life tasks.

Whether through scope creep or by design, each one lived in its own file. Each one required its own attention. When hyper-focused on one, a small part of me wondered if I was missing something.

In short, I wasn’t drowning in tasks. I was drowning in friction.

The Cost of Fragmentation

Here’s what I have learned about productivity systems: they don’t fail because you stop caring. They fail because they become taxing to maintain. Time, attention, mental energy — every file you have to open, ensure is updated and cross-reference is a small tax. Having five to-do files is five taxes. Multiply that by every day of the week, and you have a real overhead problem.

David Allen, whose Getting Things Done framework I’ve followed for years, talks about the brain as a bad office. It was not designed to hold your to-do list. It was designed to do the work. Every piece of information you store up there, an open loop in GTD parlance, that could live somewhere else is a CPU cycle you’re wasting.

I knew this intellectually. I just hadn’t fully solved it.

The Experiment

A couple of weeks ago I started using Claude Cowork — Anthropic’s desktop AI tool that lets you give Claude access to files on your computer. I’d been curious about Claude Code but felt intimidated since I have no coding background. However, once Claude Cowork was announced, I figured I should jump in but wasn’t sure where to start. Turns out I started exactly where I needed to: the thing causing me the most low-grade pain.

I copied all of my to-do list files into a single folder. (Worth noting: I made a copy, not a move. This is advice I received from a seasoned coder. It’s good protocol whenever you’re letting any AI iterate on your files — keep a safety net.) Then I gave Claude access to that folder and asked it to build me a master tracker: one unified dashboard with all my projects organized by status.

What came back surprised me. Not because it was magic. Because of the elegant simplicity in having a single Master Tracker which was encoded with a system I could build on.

A separate section for ideas — which, if you know GTD, you know capture is the whole game. One file. Every project. Clear status for each item: in progress, done, waiting. The system I’d been meaning to build for years, assembled in minutes.

Generic version of the Dashboard

The Lifting of Ambient Angst

A quick side story. I used to get bad back spasms, most likely from improper weight-lifting techniques. One bad spasm required a doctor’s visit as OTC meds were not working. The doctor prescribed a strong muscle relaxer. After a few days, I decided the grogginess was no longer worth it. Near my gym, was a chiropractor. I had tried a chiropractor maybe once or twice but didn’t really know if there was any effect.

However, this chiropractor cracked my back in a way that immediately lifted the back spasm. It was the oddest moment of relief — one minute, feeling a knot in the back and the next, it being completely gone.

That is how I felt about Cowork’s Master Tracker — an immediate lifting of a source of discomfort.

What It Actually Feels Like

The experience of using it is almost boring to describe, which I think is the point.

I open the tracker. I scan the projects. I move things to “done.” I ask Claude to clear out the completed items. I close the file.

I send Claude new items via chat as they come up.

That’s it.

There’s no shuffling between five windows. No half-remembered task that was in the “other” list. No wondering. The ambient angst associated with to-do list overwhelm — that low-level hum — has waned. I’d put it at a 50% reduction, which is not nothing when you consider that the noise never fully went away when I had five lists.

Claude has offered to do more. It keeps asking if I want daily prioritization, end-of-day summaries, recommendations on what to tackle next. I’ve declined — not because I don’t think it could be useful, but because I want to walk before I run. I’ve over-automated too early before (Outlook email folders!) and lost trust in the system. For now, simple is working.

Generic version of an Ideas page.

Where I’m Watching Next

A few things are on my radar. Claude connects to Notion, which I use heavily as an everyday tool. That integration could be another inflection point — though I’m aware that adding complexity before the current system is fully stable would be a mistake. One step at a time.

I’m also curious about what happens when I start querying the tracker more conversationally — asking Claude what’s mission critical, what’s been sitting too long, what I should be prioritizing. That feels like the next natural evolution. But I’m not there yet.

For a brief window, I’m sitting with what I have. One tracker. One place to look. One less reason for the background hum to run.

Some improvements are dramatic. This one was just quiet.

But not all walks are created equal. A good hike has a few important preconditions.

First, it has to be far enough removed from everyday chaos so that the monkey mind can slowly quiet down. This means no distractions from today’s society and technology; most importantly limited phone use (I find it hard to leave my phone, so ideally it can be placed in a backpack so it’s not pulled up mindlessly). Additionally, the path is away from the background noise of traffic or airplanes that beckon us back to civilization and anxious thought. This liberation allows deeper thoughts to have space to emerge. This isn’t immediate. It’s gradual. The noise seems to fade in layers, not necessarily consciously but the ambient angst tones down.

Second, it needs enough distance to allow for rumination. Time to sit with a single idea, turn it over, stress-test it or, if you’re with others, to move through different conversations with people in your pack. Over the course of the journey, surface talk gives way to something more deliberate.

Third, and this is important, it must include moments of real challenge. Our 8 year old would tell you, “If it is a paved trail it doesn’t count as a hike…” and while you don’t have to take your walks quite as seriously as he does, sections of a hike or walk that demand 100% focus.

This is key…

Walking, like the beat of daily life, can run on autopilot. The subconscious handles the system while the conscious mind drifts. Challenging terrain disrupts the loop and forces focus. In those challenging moments, the mind and body reconnect to solve a physical problem in real time. When faced with crossing an ice cold creek on slippery rocks or that snap-moment of realizing the challenge of a steep hill, you don’t have a choice but to focus your attention on the matter at hand.

That forcing function is grounding and refreshes something fundamentally human that we lose in our taxing and distracting world.

As a good hike progresses, a rhythm emerges and steps become more consistent. Thought patterns narrow and deepen. Conversations, if there are any, lock onto a subject and stay there. Not necessarily deep in some philosophical sense, but focused and intentional.

There’s rarely a single moment of enlightenment. No lightning bolt. Instead, there’s a slow melting. A gradual reconnection with nature, and maybe with something deeper.

It’s been a tumultuous start to the year, but Spring is coming. I hope you seek out nature for replenishment. In a world defined by constant noise and rapid change, there’s something profoundly stabilizing about environments shaped by consistent patterns over millennia.

In some sense, they remind us what steady feels like.

And sometimes, that’s exactly what the mind needs.

January is a natural time for refresh and reflection. Throughout the year, there’s no shortage of books, essays (Substacks, of course) and missives to read, podcasts to digest, lectures to watch, and the never-ending news cycle (!).

My belief is that the most important lessons aren’t new. They’re timeless.

Many of today’s meaningful ideas trace back to the Founding Fathers, the Enlightenment, the Romans, the Greeks and Eastern Philosophy. Human nature hasn’t changed much. Incentives haven’t changed. Fear, greed, patience, discipline, and compounding behave the same way they always have.

The point is that every learning cycle doesn’t need to stretch toward something new, but rather to reinforce the lessons that already work.

I have a particularly strong form of recency bias. I tend to run on the track of the most recent lesson learned - the last lesson, book, idea, or market move that felt insightful. But, my sense is that the discipline to stick with fundamentals is where the richest learning actually lives.

Steph Curry is said to take 300 - 500 shots after every practice. The work doesn’t stop because the performance was good. The work continues because that’s what makes the performance inevitable.

That’s how I think about January - not as a month to chase novelty, but as a time to keep taking practice shots. My typical default is to look for the new: the book that unlocks the breakthrough, the novel idea that leads to a multi-bagger, the person that opens the door, the ultimate hack or nugget of new information. But that misses the point. Timeless lessons, reinforced, are often far more powerful than new ones discovered.

January Plan

The goal isn’t volume. I don’t want this to feel like homework. Instead, it’s the equivalent of gym time (where all of the audio portions will be consumed). I want a digestible list that strengthens the core muscles, doesn’t lead to overwhelm and is ENERGY-PRODUCING. One where you get up with a spark.

The lesson itself is only part of it. An equally important part is re-infusing back into the subconscious making lessons recent, available, and usable when it matters.

So here is my Annual Discipline Reinforcement list.

Habits, Systems & Compounding

• James Clear — Any long-form podcast on habits
  You don’t rise to goals; you fall to systems. He seems to be everywhere in January, so this one is easy to find.

• David Allen — Getting Things Done
  ”Your mind is for having ideas, not holding them”

  He’s also on numerous podcasts. A good annual reminder to clear open loops and create mental space to focus on what matters.

Health-Span

• Peter Attia – Outlive Notes

His recent AMA on cardio-respiratory training covered many of the main concepts. I also have a copious notes on Outlive.

On a related note, DEXA Scan and VO2 Max planned for month-end.

Work, Craft & Long-Term Output

• Paul Graham — “How to Do Great Work”
  Obsession plus patience compounds. “Develop a habit of working on your own projects.” is a line that resonates.

• Paul Graham — “Maker’s Schedule, Manager’s Schedule”
  Protect deep work; it’s where real progress lives.

  For the productivity nerds: I find that deep work reduces stress levels. It’s incredibly easy to slip into Manager mode. A recurring reminder to myself: if possible, deep work first, then Manager mode. Not easy, but worth strengthening the muscle.

Business, Leverage & Alignment

• Kevin Kelly — “1,000 True Fans”
  You don’t need to be the most popular person, just trust and alignment with people who appreciate the work.

• Naval Ravikant — “How to Get Rich Without Getting Lucky”
  One of many, many lessons: Play iterated games. All the returns in life, whether in wealth, relationships, or knowledge, come from compound interest.

Investing, Risk & Decision-Making

• Warren Buffett — The Superinvestors of Graham-and-Doddsville
  Temperament matters more than IQ.

• Charles D. Ellis — The Loser’s Game
  Winning is mostly about avoiding unforced errors.

• “Even God Would Get Fired as an Active Investor”
  Short-term underperformance is often the price of long-term edge.

Meaning, Temperament & the Inner Scorecard

• Viktor Frankl — Man’s Search for Meaning (Audio Book - Simon Vance)
  Part 1 is a stark reminder of the possibilities of human nature, both terrifying and inspiring. We can only hope to learn vicariously (and NOT experientially) from this…I have found Simon Vance’s reading to be particularly impactful.

• David Foster Wallace — “This Is Water”
  ”Morning boys, how’s the water?”

• Tuning Fork Analogy

  I was introduced to this by a coach/counselor that I worked with this past year. It has resonated with me. Here is a random blog on the concept that I enjoyed.

Learning & Thinking Clearly

• Charlie Munger — “A Lesson on Elementary, Worldly Wisdom”
  Annual Reminder: You have to have mental models in your head.

• Annie Duke — “Risk Shmisk”
  The real risk: Doubling down on a losing strategy and blaming the losses on bad luck. A lesson which can be widely applied beyond investing (and poker).

The Intended Result but Unintended Outcome

This is my attempt to focus on the fundamentals until they feel obvious again.

The list isn’t about discovering something clever. It’s about reinforcing what works -just like Steph Curry getting shots up after the lights go out.

However, even thus far in the list, I digest the lessons differently that in years past. None of the words change, but I have changed…So, I see things differently.

Hopefully, this list helps…just 1%…everyday...

Please reach out directly if you have anything to add.

Diffusion, the process by which an innovation spreads through society, has been accelerating dramatically. A 2008 New York Times analysis revealed a striking pattern: every major innovation follows the same S-curve trajectory of a slow ramp-up, rapid adoption, then plateau. But the time to reach mass adoption keeps shrinking.

Source: NY Times – Archive

The landline telephone took nearly 50 years to reach 60% of U.S. homes. The internet achieved the same penetration in less than 15 years. The smartphone? Even faster, reaching 60% adoption in approximately six years after the original iPhone launch in 2007.

This acceleration isn’t just about better technology; it’s also about connection. As the world becomes more networked, exposure to innovation becomes nearly instantaneous.

We’re seeing this pattern repeat with artificial intelligence, but at an unprecedented scale and speed. According to Menlo Ventures, 61% of American adults have used AI in past six months as of mid-2025, and 19% use it daily. Daily usage rates are likely to surge in the coming years as the technology moves from experimentation to habit formation.

Beyond the Obvious: Why Second Order Effects Matter

When a transformative technology emerges, investors rush toward the obvious winners. In the AI boom, that’s been GPU manufacturers and chip architecture companies. These first-order beneficiaries have captured tremendous value.

But history teaches us a crucial lesson: the long-term outcome for any disruptive technology’s initial winners remains uncertain. Capitalism has a way of competing away early advantages. What endures, and often creates the most lasting wealth, are the second-order effects.

Morgan Stanley Investment Management highlighted analogues from the Automobile and Wi-Fi revolutions to illustrate this pattern.

Consider the internet. The router manufacturers of the 1990s enjoyed their moment. But the real fortunes were made by companies that only existed because of the internet: Amazon, Google, Netflix, Meta. The infrastructure providers were first-order winners. The platform builders were second-order victors.

AI will likely follow a similar pattern.

The Three Tiers of AI Second-Order Effect Investment Opportunity

Tier 1: Productivity Gains (Low to Medium Benefit)

The most immediate second-order effect of AI will be significant productivity gains across both blue-collar and white-collar labor. But not all companies will benefit equally from these gains.

In “BarbAIrians at the Gate: The Financial Opportunity of AI,” Alex Rampell distinguishes between “bits” businesses (purely digital operations) and “atoms” businesses (those with physical components). Bits businesses will likely cut costs more aggressively at first, while atoms businesses may see more marginal gains.

In either case, the winners will be companies with existing competitive advantages, particularly those with:

High switching costs: Take Microsoft, a bits business that is deeply embedded in workflows worldwide. Absent a disruptive shift in how people work, Microsoft will benefit from customer reluctance to switch platforms. As it automates many functions, the efficiency gains will accrue directly to Microsoft’s bottom line. Consider how AI could replace back-office support calls, a perfect example of bits-based cost savings.

Low rivalry industries: Consider Visa and Mastercard, both bits businesses operating as an effective duopoly with massive moats. When AI improves their operational efficiency, they won’t need to fully compete away those gains through price wars. The same logic applies to the auto parts oligopoly (atoms businesses): AutoZone, O’Reilly, and Advance Auto parts operate in a rational, low-rivalry environment where AI-driven efficiency translates to margin expansion.

Investment approach: Overweight companies with existing competitive advantages and low immediate competitive threats. These businesses will quietly compound their AI-driven productivity gains.

Risk-reward profile: This is the lowest-risk, most predictable AI investment thesis. You’re betting on good companies becoming more efficient, not on massive disruption. However, massive disruption remains a risk for these staid companies.

Tier 2: “Only-Exist-Because” Companies (High Benefit for Individuals, Small for Enterprises)

This tier represents businesses and income streams that only exist because AI makes them possible.

Consider the parallel: influencers only exist because social media democratized content distribution. Before Instagram and YouTube, you needed traditional media gatekeepers to build an audience. Social media eliminated that bottleneck.

AI is creating a similar unlock for “solopreneurs.” Tasks that previously required teams for coding, design, copywriting, data analysis, and customer service can now be handled by one person augmented by AI.

Real-world example: A solopreneur could build a business that monitors news in real-time, generates relevant meme designs, and automatically lists them for sale on print-on-demand platforms. The entire operation, from trend identification, design, copywriting, to listing creation, could run with minimal human intervention.

As coding becomes accessible through natural language, and as design and market research processes become automated, what was once impossible for a single person becomes not just possible but scalable.

Investment approach: This tier is less about enterprise investing and more about individual opportunity. The beneficiaries are people who can move quickly, experiment rapidly, and capture emerging micro-markets.

Risk-reward profile: High potential for individuals, but these businesses often remain small and transient. They represent income streams, not venture-scale opportunities.

Tier 3: Category Creation (Generational Opportunity)

This is where generational fortunes are made but the path requires imagination, patience, rigorous A/B testing, and “being stubborn on vision and flexible on details” - to quote Jeff Bezos, CEO of a generational winner from the last great tech wave.

Throughout history, the companies that created entirely new categories often became the dominant players in those categories for decades.

• Coca-Cola didn’t just sell a drink; it created the soft drink category

• Gillette invented the safety razor and the razor-and-blade business model

• Home Depot created the big-box home improvement category

• Microsoft defined PC operating systems

• Google set the standard for what search means

• Amazon defined e-commerce at scale

• Salesforce pioneered cloud CRM

• Uber created ride-hailing

• Nvidia created “the world’s first GPU”

Notice a pattern: these companies didn’t compete in existing categories. They created new categories and became synonymous with them.

While it may feel strange to consider this now, the categories that will define the 2030s and 2040s are being built right now as AI spawns entirely new categories - industries and use cases that don’t exist today and aren’t readily apparent yet.

Investment approach:

This requires long-term thinking. You need to identify category-creating companies. Those that play in the infant stage (pre-IPO) will benefit greatly. However, even as these companies go public, there is still a significant potential to compound for generations.

Risk-reward profile: This is where 100x returns happen. It’s also where you can experience intense drawdowns if companies don’t prosper. The risk of taking an oversized position includes both outright loss of capital and opportunity cost if you miss other winners.

Ultimately, success requires portfolio construction that embraces volatility, accepts failures, and gives winners room to run for decades.

What I’m Watching

For Tier 3, I’m focused on two types of companies:

• AI-native companies solving problems that couldn’t be solved before: Not companies adding AI features, but companies whose entire existence depends on AI making something newly possible.

• Businesses exhibiting the spark of a network effect: Where each customer makes the product better, and AI accelerates that flywheel.

These companies may not use “AI” in every sentence. They’re just building something that couldn’t exist without it.

Over time, this list will evolve but I believe it’s critical to have a framework to build from.

The Bottom Line

The chip manufacturers are having their moment. But the fortunes of 2040 may not come from the companies selling the shovels. They will likely come from the companies building entirely new gold mines.

Second-order thinking has historically been the key to capturing generational wealth.

The critical question is: are you playing in the right tier, and do you have the conviction to stay there long enough to win?

I was recently introduced to the work of Utpal Sheth, an Indian investor who worked closely with Rakesh Jhunjhunwala. Jhunjhunwala’s record of compounding is legendary, reportedly starting in 1985 with about $500,000 and growing it into more than $6 billion by 2022, the year of his passing. Utpal has carried forward that mantle, championing their process. If you have an hour to spare, que up one of his recent talks; it’s well worth the listen.

One of their core tenets is what Utpal calls “Terminal Value Investing”. He outlines his perspective and process in an exceptional presentation. Rather than reprise the full framework, which is well worth studying on its own, I want to focus here on the psychological shift: evolving from a think-forward approach to valuation toward a think-backward approach.

Terminal Value to Terminal State

Anyone who has slogged through finance courses or built DCF models recognizes the terminal value as the final line in a stream of projected cash flows. As Investopedia defines it:

“Terminal value (TV) is the value of a company beyond the period for which future cash flows can be estimated. Terminal value assumes that the business will grow at a set rate forever after the forecast period, which is typically five years or less.”

Tellingly, it adds:

From here, finance textbooks immediately pivot to formulas.

But there’s a crucial distinction between building financial models and building mental models. Terminal value, as typically taught, is an extrapolation exercise. Terminal State, by contrast, is a mindset shift. It reframes how we think about time, probability, and the economics that drive long-term compounding.

That’s why I have found a useful reframing, evolving from “Terminal Value,” to “Terminal State[1].”

Terminal State vs. Intrinsic Value

When evaluating any asset, whether gold, real estate, a private business, a listed stock, or a bond, the task is the same: determine what the asset is worth. Market quotes tell you only what others will pay today; but the investor’s job is to decide whether that quote is a useful signal of the underlying value.

There are many ways to answer the valuation question. The classical route for many value investors is to calculate intrinsic value.

Intrinsic Value: The Problem-Forward Frame

The term intrinsic value has been in the investing lexicon for almost a century. Benjamin Graham and David Dodd described it as:

“that value which is justified by the facts, e.g., the assets, earnings, dividends, [and] definite prospects, as distinct, let us say, from market quotations established by artificial manipulation or distorted by psychological excess” (Graham & Dodd, 2008, p.64).

In practice, intrinsic value has historically been applied in two broad ways:

· Asset-oriented: net assets, liquidation values, and “net-net” style conservatism that dominated the Depression-era playbook.

· Earnings-oriented: discounted future cash flows, earnings multiples, and the stream-of-benefits approach that Graham’s followers evolved toward after markets recovered and cash-generative businesses became more prominent.

Psychologically, intrinsic value is usually anchored to today: you take present facts and expectations and push them forward. You start from now and build out margin assumptions, growth rates, and terminal multiples, arriving at a forecast of future cash flows that you discount back to a present value that either justifies or doesn’t justify the current price.

This is a fine and rigorous approach. But it implicitly emphasizes the path from today to tomorrow, often anchoring disproportionately to near-term estimates.

Terminal State: The Problem-Backward Frame

Terminal State reverses the vantage point. Instead of projecting outward from today, you start by imagining a credible future state of the business or asset and work backward.

What does the business look like in 5, 10, or 20 years (or even 150 years – more on this below) if it thrives?

What will the industry economics be?

What portion of those economics is the business likely to capture? Put differently, is there a differentiator enabling the business to grow and/or maintain market share?

From there, you assign probabilities to those futures and value the expected terminal outcome.

At its core, Terminal State is scenario-based and probability-weighted. Rather than building a single, best-guess, multi-year forecast, you build a set of plausible end-states and estimate how likely each one is.

Mental Model: Intrinsic Value vs. Terminal State

The psychological shift is crucial: Terminal State forces you to ask questions about the mature state of the business, not whether next quarter’s numbers will miss or beat expectations. It compels the investor to test the long-run durability of competitive advantages, capital allocation decisions, and the industry’s underlying economics.

Much like solving a child’s maze puzzle, envisioning the end-state and working backwards provides a much clearer view.

As a screening tool, it’s invaluable, if the future is too cloudy, move on to businesses with more predictable outcomes. Cloudiness may stem from intense competition (low barriers to entry), disruptive technological shifts, or other structural headwinds.

Charlie Munger (and Coca‑Cola): a masterclass in terminal thinking

Charlie Munger’s recounting of the Coca‑Cola opportunity is the classic example of far horizon thinking.

In 1996, Munger presented a thought experiment: how would you turn $2 million in 1884 into $2 trillion by 2034?

Munger’s method began not by charging forward, but by inverting the problem. Instead of asking, “How do we build a giant business?” he asked, “What conditions must be true for a business to compound into trillions?” Working backwards, he presented a clear path: you’d need a universally consumed product, protected by brand power, scalable across geographies, and reinforced by psychology.

Coca-Cola checked every box. Humans everywhere drink water - about 64 ounces per day. If Coke could flavor just a quarter of that intake and capture half the market, the math pointed to trillions in value. From there, Munger layered in psychology: operant conditioning (sugar, caffeine, cold refreshment), Pavlovian association (Coke linked with joy, beauty, and togetherness), and social proof (monkey-see, monkey-do consumption). Adding distribution and trademark protection sealed the moat.

Crucially, Munger also inverted to identify what not to do: don’t allow copycat brands to dilute the trademark, don’t trigger consumer backlash with abrupt changes (see: New Coke), and don’t cheapen the product’s quality. By systematically eliminating failure points, the path to compounding became increasingly robust.

Munger’s lesson is a masterclass in Terminal State. Whether investing, building, or tackling life’s puzzles, forward thinking alone isn’t enough. The biggest insights often come from working backwards from the desired outcome - identifying the non-negotiables, the failure modes, and the lollapalooza effects that emerge when multiple forces combine.

The 1% IRR: The power of dual perspectives

The strength of Terminal State lies not in replacing intrinsic value analysis, but in complementing it.

Intrinsic Value enforces discipline;

Terminal State enforces perspective.

Together, they form a powerful toolkit: intrinsic valuation keeps you honest about today, while Terminal State thinking keeps you honest about tomorrow.

[1] Credit to Fred Losen for pressing this point.

It got me thinking about my own reps - my practice as an investor, and more broadly, as a thinker. What does muscle memory look like in this world?

Just like in basketball, there’s no single drill that builds mastery. Improvement comes from many habits: discussing ideas with the thoughtful people, turning over rocks (a term of art for evaluating lots of companies), reading widely and often. But one habit reinforces all others: writing.

Writing as Practice

Writing is a form of mental training. You can’t write clearly without thinking clearly. And the act of writing - especially in long form - forces that clarity. That alone makes it invaluable.

There are two key forms of writing:

• Writing for yourself

• Writing for others

Writing for myself often begins with a simple question – for example, “What’s my intention today?” The act of answering, even briefly, forces a response. That itself is powerful.

Writing for others is different. As a portfolio manager, I communicate with our LPs through periodic letters. These aren’t mere updates- they’re time capsules of our thinking, meant to convey not just what we’re doing, but why.

At first, these letters were painful to write. Prior to writing letters, I’d built countless PowerPoint decks and investment memos, but I hadn’t written for an audience I wanted to truly understand me. These letters changed that. They showed me the deeper power of writing.

The 1% IRR

Writing about investing - mental models, theses, risks - helps test and refine our thinking. The process often starts consciously, but the subconscious takes over. Patterns emerge. Insights clarify.

Even writing this Substack, I’m not trying to say something novel. I’m trying to say something important - to me. That’s the 1% IRR: each essay is a small but steady return, sharpening my understanding, deepening my muscle memory.

Why Writing Matters Even More Now

AI tools have made it easier than ever to write. But how we use them matters.

There are two approaches:

1. Let AI write the first draft

2. Write the first draft yourself, then use AI as an editor

The first is tempting - it’s the “easy button.” But if the goal of writing is to clarify your own thinking, outsourcing that first step defeats the purpose. The second approach - writing first, editing with AI - is where the value lies. AI becomes a mirror, showing you the holes in your logic. That feedback loop is invaluable.

Everyone needs an editor. We’ve been lucky to work with a great journalist. Today, AI can play that role too. In fact, AI edited this essay (and definitely improved it from the 4^th draft that I was on)!

Final Takeaway

Writing - even about familiar, well-trodden ideas – has been a critical way to take daily practice shots. It builds the muscle memory I need to think clearly and invest well. So, I keep writing. One percent better each time.

The Siren Song of Compounding

Charlie Munger’s wisdom encapsulates the essence of successful investing: find a few great stocks, hold them for decades, and let the beauty of compounding work its wonders. After a long period of riding a winner, massive wealth seems assured, leaving only the pleasant dilemmas of which non-profits to endow and how to pass on the largesse to heirs.

This idyllic outcome appears even more self-evident when we reflect on our personal experiences. We often think, “I’d be so much wealthier if I had just bought Apple when the iPhone launched, or Netflix when I first subscribed, or even Microsoft when I was frantically typing my midterm essay.” Indeed, a substantial investment years ago in any of today’s great businesses would have led to remarkable wealth creation.

The Missing Piece: The Pathway

However, this retrospective view overlooks a crucial element – the Pathway. What transpired during those years before these stocks reached the promise land? Was it a smooth ascent towards inevitable greatness, or were there periods of uncertainty, doubt, or even existential threats?

The more important question is: how would you have responded when faced with adversity? Would you have calmly brushed off the volatility, confidently knowing the stock would perform exceptionally well in the decades to come? Or would you have capitulated and sought safer havens?

Understanding Historical Returns

Our analysis reveals that most stocks fail to outperform the broader market, with less than 10% achieving annualized returns above 15% over a 5+ year holding period.

The chart below illustrates the 20-year annualized returns of big winners - stocks that have compounded at 15% or more annually. (This is not a comprehensive list).

20-Year Returns of Big Winners

Yellow = SPY (S&P 500 ETF)

Annualized Returns of stocks over a 20-year period

To illustrate the dramatic impact of compounded returns, the table below shows the growth of a $1,000 investment at different annual returns over a 20-year period. For a comprehensive compounding table, see here.

The Cost of Success: Depth x Length

While the long-term returns of top-performing stocks are impressive, it’s crucial to understand the volatility experienced along the way, which unfolds in two ways:

1. Max Drawdown

Most big winner stocks have experienced larger drawdowns than the S&P 500 at some point in their history. The psychological impact of losses often outweighs the pleasure of gains, which means a large drawdown could potentially lead to irrational behavior like selling at a low.

Max Drawdown over a 20-Year Period

Yellow = SPY (S&P 500 ETF)

Max Drawdown = Highest percent drop from a previous trading high. For example, if a stock traded at $100 and fell to $45, its drawdown is 55%.

2. The Challenge of Waiting

The intensity of a drawdown alone fails to convey the full challenge of the investment journey, particularly the prolonged duration of a drawdown.

% of Days Below a 50% Drawdown

Yellow = SPY (S&P 500 ETF)

The number of trading days the stock traded below 50% of a previous high. For example, if a stock traded at $100 then fell below $50 and stayed at that level for 10 years, it would trade 50% of Days Below a 50% Drawdown.

Case Study: MSFT vs S&P 500 – Compounded Returns vs. Drawdown

Consider Microsoft, a company that has always seemed destined for greatness. Over the past 20 years, it spent more than 10% of the time (529 trading days) trading below 50% of its previous high watermark. That’s equivalent to more than two calendar years of trading! In contrast, the S&P 500 index spent only 0.3% of the time in such a position.

Focusing on the challenging times misses the main point – MSFT compounded at 17.4% or a cumulative 2,377% over the last 20 years, while the S&P 500 compounded at 10.6% or a cumulative 652%. This is the historical proof that long-term investors should prefer a “lumpy 15% to a smooth 10%.”

The Psychological Toll

The rub of earning all that extra cash from holding a company like Microsoft is partially the volatility and partially the waiting. The effect of those 529 days shouldn’t be underestimated.

Imagine investing a significant portion of your net worth into a stock, only to see it drop by 50%. How would that feel? Now, amplify that feeling by the days, weeks, and months of mental strain.

How would you handle the scrutiny from family, friends, and colleagues questioning your decision? Is your support system truly supportive?

The point is, a drawdown isn’t typically a brief event lasting a day, a week, or even a month. As Microsoft’s history shows, there can be long periods of feeling foolish. This is a true challenge of long-term investing. The math may be elegant and inviting, but the path can be daunting. Long-term investing can be incredibly rewarding, but one must be prepared for the difficulties that lie in waiting.

Key takeaways:

1. Everyone can be Warren Buffett in theory, but there is only one in practice.

2. Investors often buy great businesses but may capitulate at the worst possible times.

3. Long-term investing requires understanding that rough patches are par for the course.

4. Recognize that these challenging periods can be both dramatic and prolonged.

5. Build safeguards into your investment process and mentally fortify yourself during good times to withstand challenging periods.

1% IRR Takeaways:

• Since the inception of the S&P 500 in its current form, the index has compounded ~10.3% per year from 1957 to 2023.

• While the stock market goes up over the long term, stocks do not. From 1926 to 2023, ~52% of stocks listed in the US have negative lifetime returns.

• Data analysis reveals that a few businesses are responsible for driving stock indices higher.

• Whether you are a “look for the winner type” or an “avoid loser type” isn't important. However, understanding your archetype is.

Defining Investment Success

Key Point: A “good investment” is one that generates returns surpassing an objective benchmark, not merely yielding a positive return.

The primary goal of investing is to enhance future purchasing power. Various investment vehicles exist to achieve this objective, ranging from low-risk treasuries to higher risk options like stocks, private investments, commodities, etc.

Investing in the U.S. economy has been a remarkable strategy over the past century. The S&P 500, a market-cap weighted index of the 500 largest U.S.-listed companies, serves as an excellent way to invest and gain exposure the U.S. economic spirit.

For investors venturing beyond index funds, the S&P 500 provides a crucial benchmark for measuring opportunity cost. Therefore, the index becomes an objective standard for comparison when analyzing the historical performance of individual stocks.

First Principles - Evaluating Historical Data of Individual Stocks

Looking Up

By analyzing the history of all U.S. stocks from 1926 to 2023 (Hendrik Besseminder, professor at Arizona State University, has done excellent research compiling and analyzing historical stock prices), certain patterns emerge.

Focusing only on the positive outcomes:

• ~48% of all stocks produced positive lifetime returns

• ~28% of all stocks achieved annualized returns exceeding 10%

• The percentage of outperforming stocks decreases when factoring in holding periods

Focusing on companies that have existed for more than 5 years (a proxy for long-term investments), the data tells a more challenging story.

Data Story - Understanding Historical Stock Returns

Picking winning stocks has historically been a coin flip (Figure 1).

However, finding the companies that compound at above market rates for long-term periods offers odds more like rolling a die and betting only on 6.

The data underscores a crucial point: of all the 28,843 U.S. companies ever listed, fewer than 3,000 have provided annualized returns exceeding 15% over a 5+ year holding period. Additionally, with so many negative-performing stocks, the indices are driven higher by a fraction of stocks.

Fork in the Road

Investors will have a natural predilection when considering investment decisions and typically fall into two categories:

(1) Those who seek winners

(2) Those who aim to avoid losers

Every person has a genotype (an organism’s genetic information), and phenotype (a set of observable traits). In a study published January 14, in Nature Genetics 2019, an international research team identified 124 genetic variants associated with a person’s willingness to take risks.

Key points:

• There is no individual variant that meaningfully affects a particular person’s risk appetite.

• Non-genetic factors, i.e., life circumstances, matter more for risk tolerance than genetic factors.

While genetic factors play a role in risk tolerance, life experiences and circumstances weigh more significantly on our risk-taking behaviors.

Investor Archetypes

1. The Entrepreneurial Optimist: Focuses on potential upsides and sees promise in various businesses.

2. The Distrusting Pessimist: Views C-Suite executives with skepticism and is concerned about shareholder exploitation.

Both types of investors are right and wrong. It’s not important to be one or the other, the key is to recognize your inherent bias.

In a recent interview, Reed Hastings, co-founder and executive chairman of Netflix, made this confession – “If you mean financial investments, the few times I’ve done investing, I’ve lost my shirt and I realize I’m just so optimistic. I like anybody who seems to have a good idea, I’m like, “Sure.” So I think it’s a different DNA than investors have that are differentially good investors. So I’m a pure index fund investor. I’m Netflix plus index fund.”

Mr. Hastings comment exudes self-awareness. Not everyone has to take such an extreme approach, but guardrails around optimism are important.

1% IRR Improvements:

• Statistically, finding winning stocks is challenging. For any random stock, it’s more likely to lose money than beat an index.

• Know thyself: If you are an optimist, look down before you look up.

As Ben Graham once said: “The intelligent investor is a realist who sells to optimists and buys from pessimists.”

Disclaimer:
The content published on The 1% IRR Project is provided for informational and educational purposes only and does not constitute financial, investment, legal, or other professional advice. The author is not a registered investment advisor, broker-dealer, or financial planner. Nothing on this site should be construed as a recommendation to buy or sell any securities or to make any financial decisions. You should consult with a qualified financial advisor or other licensed professional before making any investment or financial decisions. All views expressed are solely those of the author and are subject to change without notice.

“The first rule of an investment is: Don’t lose. And the second rule of investment is: Don’t forget the first rule. And that’s all the rules there are.“

While seemingly simple, Buffett’s rules are almost mathematically impossible. Nevertheless, the core message is clear: prioritize the downside and let the upside take care of itself. In other words, “look down, before you look up.”

The purpose of the 1% IRR Project is to deconstruct investing into first principles such that they can be reconstructed for better decision-making.

To deconstruct the potential outcomes for a long-term investor, a good starting point is to look at the history of stock prices over a long period of time, ensuring consideration of various economic cycles.

Breaking into First Principles: Evaluating Historical Data

Looking Down: A Sobering Reality

Certain patterns emerge when analyzing the history of all U.S. stocks from 1926 – 2023 (Hendrik Besseminder, professor at Arizona State University, has done excellent research compiling and analyzing historical stock prices).

Focusing on the Negative Outcomes:

- Approximately 52% of all stocks had negative lifetime returns

- About 33% of all stocks lost more than 75% of their value. In other words, when stock prices fall, they fall hard.

A similar study from JP Morgan analyzed the lifetime returns of all stocks in the Russell 3000 from 1980 to 2014 or until a firm was acquired, merged, or delisted.

Summary of the Negative Outcomes:

- Roughly 40% of all Russell 3000 stocks had negative absolute returns.

- Comparing stock prices against the Russell 3000 index, the median stock underperformed by an excess lifetime return of -54%.

- About 66% of stocks had negative excess returns vs. the index.

The difference between the studies is likely due to JP Morgan’s study (1) excluding micro-caps and (2) evaluating market data during a secular bull market.

Key Takeaways:

- Many stocks will fail. Statistically, when evaluating an investment, it’s a coin flip whether it will work.

- Markets go up but stocks go down. The data looks even bleaker when comparing against a market index since 2/3^rd of stocks historically underperformed the market.

The Creative Destruction of Capitalism

The byproduct of a healthy capitalist system is that many companies will be destroyed. In Joseph Schumpeter’s classic work, “Capitalism, Socialism, and Democracy”, he made this poignant assertion about capitalism:

The opening up of new markets … and the organizational development … illustrate the same process of industrial mutation—if I may use that biological term—that incessantly revolutionizes the economic structure from within, incessantly destroying the old one, incessantly creating a new one. This process of Creative Destruction is the essential fact about capitalism [emphasis added].

The steady-state of a capitalist society is that the old will give way to the new. As new industries emerge, old ones decline or disappear entirely, driving economic evolution. This is an endless cycle.

Accelerating Creative Destruction

A 2016 study by Vijay Govindarajan of Dartmouth's Tuck School of Business analyzed 29,688 firms listed from 1960 through 2009.

The paper’s abstract tells the story:

Life is short. That’s never been more true for corporations today. An analysis of all 29,688 firms that listed from 1960 through 2009, divided into 10-year cohorts, reveals that newly listed firms in recent cohorts fail more frequently than did those in older ones. Creative destruction is accelerating because there is a fundamental shift in the American economy. The pre-1970 firms tended to be heavily invested in physical infrastructure, such as factories and inventories. Later cohorts have relied increasingly on intangible assets, such as databases, proprietary algorithms, and expert workers. This transformation is a double-edged sword. The good news is that newer firms are more nimble. The bad news for these firms is that their days are numbered. That is, unless they continuously innovate.

Corporate Survival

Key Takeaway:

- Corporate mortalities are accelerating.

- The weighting of companies built upon hard assets is declining while soft-asset companies are rising.

- Soft asset companies generally have fewer barriers to entry, and therefore higher mortality rates.

1% IRR Improvement:

Understand the game that is being played.

o Recognize that a healthy capitalist system naturally destroys businesses.

o Acknowledge the accelerating pace of destruction.

o Realize that many of today’s companies will not be around in 5 years, less in 10 years, and even fewer in 20 years.

A mountain climbing analogy:

o If ~50% of climbers fall on a certain path, you will be cautious.

o Knowing that an increasing number of climbers are falling earlier in their journey, you will be even more vigilant.

In other words, you will look down before looking up.

Part 2 will explore “Looking up” or the state of winners.

Disclaimer:
The content published on The 1% IRR Project is provided for informational and educational purposes only and does not constitute financial, investment, legal, or other professional advice. The author is not a registered investment advisor, broker-dealer, or financial planner. Nothing on this site should be construed as a recommendation to buy or sell any securities or to make any financial decisions. You should consult with a qualified financial advisor or other licensed professional before making any investment or financial decisions. All views expressed are solely those of the author and are subject to change without notice.

• The growth of Noise has been unprecedented and accelerating.

• Technology continues to evolve to generate more Noise, rather than filter it out.

• AI is rapidly amplifying Noise production.

• Going forward, Noise will increasingly challenge decision-makers across all fields.

In an ideal world, you could effortlessly filter out unnecessary information (Noise) and focus solely on meaningful data (Signal). You would curate your information flow to include only relevant materials that inform decision-making.

However, the sheer volume of data growth is making it impossible to passively absorb information, making it increasingly difficult to find the Signal.

Noise vs. Signal

The purpose of the 1% IRR Project is to deconstruct investing into first principles so that they can be reconstructed for better decision-making.

To deconstruct Signal vs. Noise, it’s crucial to understand the current state of Noise. Being aware of the increasing trend of Noise helps frame the search for Signal.

Part 1: The Noisy State of Noise

In 2003, Google CEO Eric Schmidt stated: “Every two days now we create as much information as we did from the dawn of civilization up until 2003. That’s something like five exabytes of data.”

He added, “The real issue is user-generated content.”

This comment predates Facebook’s founding in February 2004.

Since then, data has exploded.

DOMO, Inc., a data science company, has been publishing “Data Never Sleeps” since 2013, providing insights into the amount of data being generated and published every minute of every day.

In 2013, a subset of the stats per minute included:

• YouTube users upload 48 hours of new video

• Instagram users share 3,600 new photos

• Brands and organizations on Facebook receive 34,722 “likes”

• Over 100,000 tweets are sent

A decade later, the growth has been staggering.

This trend extends beyond user-generated content to include economic indicators. According to Nate Silver, the U.S. government provides over 45,000 economic indicators, with another 4,000,000 from the private sector.

The Future of Noise

The astonishing part of the data reviewed thus far is that it is before AI is auto-creating billions of pieces of information. There is a storm cloud of Future Noise on the horizon!

In February 2024, Sam Altman tweeted “openai [sic] now generates about 100 billion words per day …”, which the NY Times characterized as “a million novels’ worth of text every day …”

NewsGuard, a website dedicated to tracking misinformation, has identified 1,075 AI-generated news and information sites operating with little to no human oversight.

It’s important to recognize that AI is still in its infancy, with exponential growth expected.

By-Products of Noise

Financial markets will likely be affected by increasing Noise levels, potentially leading to more volatile moments.

More critically, market participants are faced with a challenge – excessive Noise impacts thinking itself. In the search for truth, the road is paved with relevant information. The prevailing viewpoint is that more data is better. While curiosity drives the search for more information, there is an argument that sifting through Noise could be counterproductive. Afterall, it steals a precious commodity – time.

1% IRR Improvements:

• First Principle: Recognize the sheer volume of Noise

• Anticipate the impending surge in Noise.

• Develop strategies to effectively filter Signal from Noise, a topic we will explore in subsequent essays.

When Noise Becomes the Greatest Signal in the Universe

Here is the original story published in PBS. It is short and worth the read, however, for the TL;DR - below, is the ChatGPT summary 😉 …

In 1960, Bell Labs built a large antenna in Holmdel, New Jersey, for an early satellite transmission system called Echo, which bounced radio signals off balloons in the atmosphere. However, the launch of the Telstar satellite soon made the Echo system obsolete. Two radio astronomers at Bell Labs, Arno Penzias and Robert Wilson, wanted to use the Holmdel antenna for research, and when Telstar was launched, the antenna was freed up for their work.

As they began using the antenna, they encountered a persistent noise, a microwave signal coming from all directions. After ruling out various potential sources—such as interference from the city, the galaxy, and even pigeon droppings—they concluded it was not a technical issue.

At the same time, physicist Robert Dicke at Princeton was developing theories about the Big Bang, predicting that the explosion's residue would be a low-level background radiation across the universe. When Penzias and Wilson contacted Dicke’s team, they realized they had inadvertently discovered this cosmic background radiation, supporting the Big Bang theory. Penzias and Wilson’s work, published in collaboration with Dicke, earned them the Nobel Prize in Physics in 1978. Their discovery became key evidence for the Big Bang and reshaped the field of cosmology.

Take-away:

Sometimes, Noise = Signal!

Disclaimer:
The content published on The 1% IRR Project is provided for informational and educational purposes only and does not constitute financial, investment, legal, or other professional advice. The author is not a registered investment advisor, broker-dealer, or financial planner. Nothing on this site should be construed as a recommendation to buy or sell any securities or to make any financial decisions. You should consult with a qualified financial advisor or other licensed professional before making any investment or financial decisions. All views expressed are solely those of the author and are subject to change without notice.

Share

-Ben Franklin

Key Takeaways:

• Compounding is arguably the most important concept in investing.

• Compounding is conceptually simple but often thought of as a “complex calculation”

• Understanding and implementing compound calculations are two different modalities, with implementation being more difficult

• “Insite, In Mind” is a useful method - detailed below.

To help understand compounding’s powerful effects, stories and tools are shared such as The Game of Rice; pithy quotes from Einstein about compound interest being the 8th wonder of the world, and of course the standard compound interest table (a financial advisor favorite).

Such rhetorical devices are helpful to fire up the mind, but the lesson can be fleeting. Not for lack of want, but for lack of use. Most folks consider linear calculations the default state - simple math at the grocery store or a restaurant, or when calculating travel time. Percentages are another default computation, and for the most part they are a linear extension. Meaning, 20% of a $100 restaurant bill is $20. The next step is the summation. From there, our math work is done, and we merrily go about our day (depending on the quality of the meal).

There are few daily circumstances that require thinking in terms of compounding.

For investing, however, thinking in exponentials is all that matters. The math required to calculate a one-year gain is linear – a $100 investment earns 7% therefore, I have $107. However, the math used to understand how long it takes to double your money from an investment earning 7% annually is more complicated. The Rule of 72 is a quick shortcut. -- simply divide 72 by the annual rate of return. In our example, 72 / 7 = ~10.2 years, so our money doubles in about 10.2 years.

Making Compounding the Default Calculation:

• As investors, our concern is how to make exponential math a default mental model as opposed to a tool-based approach (e.g., using Excel)?

• Compounding calculations should be ensconced as the base case, and we must revert to linear calculations.

• Charlie Munger provides a great answer we call “Insite, In Mind”

As described in a January 1996 article from Forbes featuring Munger on the cover:

Buffett and Munger share a deep respect for the awesome, mysterious power of compound interest. Charlie Munger loves to quote his hero, Benjamin Franklin, on the subject ... (recall the quote above).

Munger is rarely without a compound rate of return table. He illustrates its magic by taking an investment of $1 and demonstrating that a return of 13.4% a year, after taxes, over 30 years, will make that $1 worth $43.50. To Munger it's much better to depend on compounding than on market timing.

“Understanding both the power of compound return and the difficulty getting it is the heart and soul of understanding a lot of things,” says Munger in typically grandiose terms.

1% IRR Implementation:

• Always have a compound interest table (aka “Munger Table”) readily visible.

I keep mine on my desk. Looking at the table is a forcing function for long-term thinking. The dollar value of compounded money is so great that it becomes a habit to obsess over the long-term outcomes of decisions; a byproduct is eschewing the search for short-term gains.

Pro Tip: I highlight the 30-year, 15% Annual Return (why aim low?). Without looking, can you guess the compounded return? See the green highlight below for the answer.

Disclaimer:
The content published on The 1% IRR Project is provided for informational and educational purposes only and does not constitute financial, investment, legal, or other professional advice. The author is not a registered investment advisor, broker-dealer, or financial planner. Nothing on this site should be construed as a recommendation to buy or sell any securities or to make any financial decisions. You should consult with a qualified financial advisor or other licensed professional before making any investment or financial decisions. All views expressed are solely those of the author and are subject to change without notice.

It got me thinking about my own reps - my practice as an investor, and more broadly, as a thinker. What does muscle memory look like in this world?

Just like in basketball, there’s no single drill that builds mastery. Improvement comes from many habits: discussing ideas with the thoughtful people, turning over rocks (a term of art for evaluating lots of companies), reading widely and often. But one habit reinforces all others: writing.

Writing as Practice

Writing is a form of mental training. You can’t write clearly without thinking clearly. And the act of writing - especially in long form - forces that clarity. That alone makes it invaluable.

There are two key forms of writing:

• Writing for yourself

• Writing for others

Writing for myself often begins with a simple question – for example, “What’s my intention today?” The act of answering, even briefly, forces a response. That itself is powerful.

Writing for others is different. As a portfolio manager, I communicate with our LPs through periodic letters. These aren’t mere updates- they’re time capsules of our thinking, meant to convey not just what we’re doing, but why.

At first, these letters were painful to write. Prior to writing letters, I’d built countless PowerPoint decks and investment memos, but I hadn’t written for an audience I wanted to truly understand me. These letters changed that. They showed me the deeper power of writing.

The 1% IRR

Writing about investing - mental models, theses, risks - helps test and refine our thinking. The process often starts consciously, but the subconscious takes over. Patterns emerge. Insights clarify.

Even writing this Substack, I’m not trying to say something novel. I’m trying to say something important - to me. That’s the 1% IRR: each essay is a small but steady return, sharpening my understanding, deepening my muscle memory.

Why Writing Matters Even More Now

AI tools have made it easier than ever to write. But how we use them matters.

There are two approaches:

1. Let AI write the first draft

2. Write the first draft yourself, then use AI as an editor

The first is tempting - it’s the “easy button.” But if the goal of writing is to clarify your own thinking, outsourcing that first step defeats the purpose. The second approach - writing first, editing with AI - is where the value lies. AI becomes a mirror, showing you the holes in your logic. That feedback loop is invaluable.

Everyone needs an editor. We’ve been lucky to work with a great journalist. Today, AI can play that role too. In fact, AI edited this essay (and definitely improved it from the 4^th draft that I was on)!

Final Takeaway

Writing - even about familiar, well-trodden ideas – has been a critical way to take daily practice shots. It builds the muscle memory I need to think clearly and invest well. So, I keep writing. One percent better each time.