Six Books to Understand Iran

Six Books to Understand Iran Do you want to understand Iran's recent history so you can critically think about what is going on? The Economist recommends these six books so you can build your own perspective. Have a look, you might find some interesting. I have added all to my wishlist. 1. America and Iran: A History, 1720 to the Present. By John Ghazvinian. A sweeping account of nearly three centuries of contact between the two countries, long predating the adversarial present. Ghazvinian draws on American and Iranian archives to show that the relationship was once unusually warm, and traces how it soured. A useful corrective for anyone who assumes hostility was always the default. 2. For the Sun After Long Nights: The Story of Iran's Women-led Uprising. By Nilo Tabrizy and Fatemeh Jamalpour. An eyewitness account of the 2022 Woman, Life, Freedom movement that erupted after the death in custody of Mahsa Jina Amini. The book alternates between Jamalpour reporting from inside Iran and Tabrizy covering events from abroad, built partly from the encrypted letters they exchanged. Longlisted for the 2025 National Book Award. 3. In the Rose Garden of the Martyrs: A Memoir of Iran. By Christopher de Bellaigue. A British journalist's memoir of living in Tehran in the early 2000s, married to an Iranian and trying to understand the country on its own terms. De Bellaigue uses the long shadow of the Iran-Iraq war, and the state's cultivation of martyrdom, to explain how the Islamic Republic shaped a whole generation. One of the more intimate portraits of post-revolutionary Iran. 4. Iran: A Modern History. By Abbas Amanat. Amanat's nine-hundred-page single-volume history, running from the Safavid dynasty in the sixteenth century to the Islamic Republic. Published by Yale, it is widely treated as the standard comprehensive reference, integrating politics, culture, religion and intellectual life. A commitment, but probably the best overview on the list. 5. King of Kings. By Scott Anderson. Anderson's narrative of the 1979 Iranian Revolution, centered on how the Shah's regime and the Carter administration sleepwalked into disaster. Drawing on fresh interviews, including with Empress Farah, he frames the revolution as a world-shattering event on a par with the French and Russian ones. A New York Times bestseller from 2025. 6. Iran's Grand Strategy: A Political History. By Vali Nasr. Vali Nasr, one of the leading scholars of Iran and the wider Middle East, argues that Tehran's foreign policy follows a coherent logic rooted in geography and history, not only revolutionary ideology. A corrective to caricatures of the regime as either purely ideological or purely opportunistic. Useful context for anyone trying to make sense of the current moment. https://www.economist.com/culture/2026/03/05/six-books-to-read-about-iran

- Pedro

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The Cost of Skipping the Thinking

The Cost of Skipping the Thinking A lesson from a math problem that keeps showing up at work Read this Medium article on how to simplify a mathematical expression (see image below). I found it interesting not only for how you solve it, but for how it applies to so many areas of your personal and professional life. From a mathematical standpoint, the author's point is this: "…This solution isn't about algebraic muscle. It's about structural awareness. Most learners approach problems like this asking: 'What operation do I apply next?' Experts ask a different question: 'What shape is this expression trying to be?'…" In a nutshell: don't try to compute immediately. Try to recognize a pattern, apply it, and everything simplifies by itself. In this specific case, you should spot the square of a difference and let it do the work. Generalizing, this habit of rushing to solve a problem without taking the time to think it through is something I see over and over again in my professional life, to the point of it feeling like a plague. We jump straight into solving mode without identifying the variables at stake, understanding what output is actually required, and asking how the problem can be simplified. Sometimes we reach the same result with significantly more effort than needed; other times we don't get the right answer at all, or worse, we answer the wrong question. As Einstein reportedly put it: "If I had an hour to solve a problem, I'd spend 55 minutes thinking about the problem and 5 minutes thinking about solutions." https://medium.com/think-art/the-harvard-entry-exam-a-test-of-thinking-not-memory-46c8969939ea

- Pedro

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B2B Pricing in the Age of Agentic AI

Since October 2024, when I was first introduced to Agentic AI via a training I did with “crewai,” I have been trying to create an overall mental architecture for how we can integrate this new and game-changing technology/overarching framework into 2 key domains:

1- Finance – Valuation

2- Pricing – B2B Pricing

Regarding Valuation, I’m already more advanced and currently developing a pilot project to assist me in my personal investments. However, in the pricing domain my vision is still being built, developed, and shaped.

In both domains the key question is: “How can we materialize/harness all the potential exponential value that Gen and Agentic AI can bring into the domain?”

It is within this context that the article from McKinsey — B2B Pricing: Navigating the Next Phase of the AI Revolution (attached) — provided me with significant insights and food for thought in the definition of my vision and mental framework for the pricing domain.

What I fully agree with in the presented thesis — which strengthens my prior beliefs and allows me to start laying down my conceptual structure — is the following:

· Initial claim – Gen and Agentic AI have an enormous and latent value on how B2B prices will be defined, deployed and communicated. The value will impact on both fronts – from an effectiveness (do the right things) and efficiency (do the things right) perspective.

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· A paradigm shift – the use of Gen and Agentic AI will not just generate incremental value and change, but it will create a new order, where the winners will take the full potential value and the laggards will be cast aside with significant and detrimental impacts to the pricing function and the organization (creative destruction will be observed).

“…Pricing is moving from human-led processes supported by analytics to AI-orchestrated systems capable of using analytical insights at greater scale and consistency with human oversight….”

· The key areas where it will generate the most impact:

o cleaning and repairing data;

o ingesting customer requests for proposals;

o clarifying customer requests and developing response options;

o synthesizing market signals, customer feedback, and deal data;

o guiding pricing strategy;

o updating prices dynamically;

o notifying sales and customers; and

o even negotiating terms with customers, with clear escalation rules, human oversight, and audit trails

What I miss and that for me is a make it or break it variable: Change Management.

I wholeheartedly believe that you can only promote this type of change if you keep front and center the change management that will be required in all impacted areas. For each €1 invested in the technology, you should at a minimum invest €2 in change management.

Only with that laser focus on change management will you be able to significantly increase the probabilities of success from a pricing standpoint — and consequently, given its relevance, for the organization overall.

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Conclusion:

I’m a firm believer that Pricing is one of the few key levers any organization has at its disposal to impact the value it can generate; however, it is hugely and surprisingly undermanaged and overlooked in most of the companies.

Only the companies that embrace this paradigm shift (macro impact) in the organizational structure, but also and especially in Pricing, will be the ones that will thrive in their industry.

As happened in evolution, it is not the biggest or the strongest that will prevail and be successful, but the one that can adapt/react quickly and harness the value that this new technology/organizational framework will unleash for those willing to change.

Finally, ask yourself an honest and direct question:

Where are you in this “silent” arms-race for your company’s survival and value-accretive change?

B2B pricing: Navigating the next phase of the AI revolution

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Six Books for the Gilded Age

Six Books for the Gilded Age Six books recommended by The Economist to understand the Gilded Age (1865 to 1914). All added to my wishlist. Hope you can find some that might catch your attention. 1. The Age of Innocence — Edith Wharton Wharton's Pulitzer-winning novel of 1870s New York high society, where the rules of dinner-party seating can break a life. The most elegant indictment of old-money America ever written, and still, somehow, very funny. 2. Death in the Haymarket — James Green A narrative history of the 1886 Chicago bombing, the trial of the labor anarchists and the birth of the modern American labor movement. The moment when class conflict became a permanent fixture of US politics. 3. The Republic for Which It Stands — Richard White The Oxford History volume covering 1865 to 1896. White's central claim is that Reconstruction and the Gilded Age were one continuous period — and one of spectacular failure as much as spectacular growth. The best single-volume overview on the list. 4. Titan: The Life of John D. Rockefeller Sr. — Ron Chernow Chernow's definitive biography of the devout Baptist who built Standard Oil and, along the way, more or less invented the modern corporation. Ruthless, pious and contradictory — a one-man case study of American capitalism. 5. How the Other Half Lives — Jacob Riis The 1890 photo-reportage that put the tenements of New York's Lower East Side in front of middle-class readers for the first time. Crude by today's standards, but it genuinely helped launch housing reform and the Progressive movement. 6. The Jungle — Upton Sinclair Sinclair set out to write a socialist exposé of the Chicago meatpacking industry and ended up, as he later put it, aiming at the public's heart and hitting it in the stomach instead. The book that gave America its food-safety laws, almost by accident. https://www.economist.com/culture/2026/03/06/six-books-to-understand-the-gilded-age

- Pedro

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The Agents Don't Do the Math

How a three-agent system turns SEC filings into a structured valuation config — and why the boundary between judgment and calculation is hard-coded.

When I published The First Pillar Stands last week, I mentioned that AI agents were the next step. Pillar 2 is now taking shape and the architecture is cleaner than I expected

The core principle is simple: agents author configuration. The model does the math. Never the other way around. Here is what that means in practice. When the system onboards a new company, three agents run in sequence.

1. An Analyst reads the 10-K looking at business description, MD&A outlook, risk factors, segment notes and proposes a structured configuration: drift priors per segment, FX exposures, peer multiples, capital structure thresholds. Every numerical assumption must be backed by a filing citation. No citation, no override.

2. A Reviewer then challenges each proposed field independently, cross-checking segment growth priors against historical CAGRs, verifying FX exposures are cost-ratio effects rather than translation noise, and flagging anything that falls outside acceptable bounds.

3. A Manager reconciles the two, picks the better value for every contested field with documented reasoning, and writes the final configuration file.

4. The human reviews and promotes. Agents never go to production directly.

What makes this architecture interesting is not just what the agents do, it is what they are explicitly prohibited from touching. Variables like calibrated volatility parameters, working capital days, cost of debt, beta are all computed from data, never authored by an agent. The boundary between judgment and calculation is hard-coded into the schema.

The honest limitations are in the deck too. Peer ticker hallucination is real. The reviewer can be over-cautious when management is genuinely right about forward outlook. And the self-healing XBRL loop has not yet been stress-tested on a genuinely broken filer.

Pillar 3, multi-company screening, is next, after the additional development of pillar 2 to a pilot phase.

The methodology deck is attached.

The First Pillar Stands: Building an AI-Powered Equity Valuation Engine from Scratch

 

From blank Python file to institutional-grade model — and the bigger ambition behind it.

Four weeks ago, I set out to answer a simple question: can a rigorous, institutional-quality equity valuation be built entirely from scratch, using AI as a co-pilot, without compromising on methodology?

The answer, it turns out, is yes. Here is what I built, why it matters, and where it goes next.

The problem I was trying to solve

This project started with an ambition, not a gap. I wanted to build something sophisticated and scientifically sound — a model where every methodological choice reflected my own knowledge and judgment, not a template someone else had designed. Something that was genuinely mine: in its architecture, its assumptions, and its intellectual foundations.

The objective was also strategic. I wanted a tool rigorous enough to anchor a broader investment platform, something I could leverage across future projects rather than rebuild from scratch each time. A first pillar, in the most literal sense.

The context that made it necessary is well known. Professional equity valuation is expensive and largely inaccessible. The models used by investment banks and asset managers are built over months, maintained by teams of analysts, and guarded behind paywalls. Retail investors and smaller funds are left making decisions with far less rigor, often relying on a P/E ratio and a gut feeling. If the platform I am building is to have any credibility, it needs a valuation engine that can hold up against professional standards. That is what I set out to build.

What I built

Over four weeks, working session by session with Claude, I built a fully automated equity valuation engine in Python. Here is what it does, in plain language.

It reads ten years of official data. The model connects directly to SEC EDGAR — the US government's official financial filing database, and parses 40 quarterly reports and 10 annual reports for the company being analyzed. No manual data entry. No spreadsheet copy-paste. Audited numbers, machine-read.

It forecasts revenues using Geometric Brownian Motion, calibrated separately for each business segment on ten years of actual quarterly data. To prevent the model from over-reacting to recent quarters, Bayesian shrinkage blends short-term momentum with long-run sector drift. A Markov regime-switching layer adds economic realism: the model alternates between expansion and recession states, with recession volatility set 30% higher than in normal periods. Terminal revenue growth of 0.50% per year is derived from the GBM drift itself, not assumed arbitrarily.

It models costs through an Ornstein-Uhlenbeck mean-reverting process, anchored to the historical average cost ratio using an exponentially weighted moving average. This is an important design choice: it prevents the model from projecting runaway margin compression or perpetual expansion, keeping cost trajectories grounded in economic reality while still allowing for uncertainty. EUR/USD currency sensitivity is applied directly to the relevant segment's cost ratio throughout the simulation.

It applies distress guard-rails at multiple levels. CapEx is governed by a three-layer dynamic framework: a phase-gate structure tied to free cash flow thresholds, overlaid with a D&A-parity health floor that activates when net debt falls below 2.5 times EBITDA. The cost of equity carries an explicit distress premium , calculated as the spread between the cost of debt and the risk-free rate, scaled by a 60% loss-given-default assumption, reflecting elevated near-term financial risk. The conditional terminal value rule is a further guard-rail: if the company is forecast to earn below its cost of capital at the end of the horizon, the Gordon Growth model is suppressed and replaced entirely with a market multiple approach.

It runs 10,001 simulated futures simultaneously. This is the Monte Carlo engine. Rather than producing a single "correct" number, the model runs 10,001 different scenarios using Sobol quasi-Monte Carlo sequences, a low-discrepancy sampling technique that converges faster than standard random methods. Eight correlated risk drivers, revenue per segment, cost ratios, currency, and economic regime, are jointly simulated through a Cholesky decomposition, ensuring the correlations between them are realistic, not assumed to be independent. Every single path applies the conditional terminal value rule independently. With an odd number of paths, the median outcome is always a single, exact path, not an average.

It checks itself. Four automated sanity checks run after every execution: return on capital vs cost of capital, terminal value anchor, gap between the simulation median and the discounted cash flow estimate, and regime drift. If something looks wrong, it flags it.

It grades its own methodology. Across ten analytical dimensions: data quality, revenue modeling, cost structure, cost of capital, cash flow construction, terminal value, simulation design, stress testing, earnings quality, and internal consistency; the model scores its own rigor. The current score: 9.1 out of 10, grade A.

What makes this sound valuation, not just a number

Anyone can build a spreadsheet that spits out a price target. What makes this different is the methodology underneath.

The cost of capital updates live each run — fetching the current equity risk premium directly from Professor Damodaran's database, re-levering beta annually as the company's debt changes, and letting the risk-free rate evolve along a mean-reverting path rather than staying frozen at today's level.

The terminal value, the single most impactful assumption in any DCF, applies a conditional rule: if the company is expected to earn below its cost of capital at year seven, the Gordon Growth model is suppressed and replaced with a market multiple anchor. This is the correct thing to do financially, and most models simply don't do it.

The earnings quality checks are independent of the valuation itself. The Beneish M-Score, an academic model with an 85%+ accuracy rate at detecting earnings manipulation, runs on the same official filing data and produces a manipulation probability score for every year in the sample.

The model was built to be right and to allow your critical thinking, not just to produce a number.

What Claude brought to this

I want to be direct about this: I could not have built this in four weeks without Claude.

Not because the ideas were Claude’s, the financial methodology, the architecture decisions, the analytical judgments were all mine. But because building approximately 12,000 lines of Python, across a dozen interconnected modules, while simultaneously reasoning about financial theory, statistical methodology, and implementation correctness, is simply beyond what one person can hold in their head at once.

Claude served as a tireless co-pilot: catching logical errors before they propagated, translating financial intuition into working code, identifying edge cases I hadn't considered, and — critically — pushing back when my assumptions were financially unsound.

The result is a model I can genuinely defend. Every number has a traceable source. Every assumption has a documented rationale. That combination — human financial judgment, AI execution rigor — is where the real value lies.

The output on a real company

The model has been running live on a major publicly listed industrial company. The key outputs from the April 2026 live run:

• DCF implied price: conservative single-path intrinsic value estimate
• Monte Carlo median: the central outcome across 10,001 simulated futures — x% above the current market price
• Downside scenario: a severe recession path implies −y% from current levels
• Earnings quality: Beneish M-Score of −3.28 — manipulation unlikely, probability 3.6%
• Model rating: A across 9 of 10 dimensions

The point is not the specific numbers. The point is that they emerge from a methodology that would hold up in a professional investment committee.

Where this goes next

This is the part that genuinely excites me.

Step one: multi-company. The engine is already company-agnostic, it reads from SEC EDGAR using any ticker. The next build extends it to run across an entire watchlist simultaneously: e.g screen 50 pre-selected companies, flag the ones where the Monte Carlo median diverges most significantly from the current market price, and surface the most mispriced opportunities automatically.

Step two: AI agents on top. This is the bigger ambition. Rather than a human analyst manually tweaking assumptions and re-running the model, I want a layer of autonomous agents that can monitor live earnings releases and update model inputs in real time, identify which assumptions are most sensitive and run targeted scenario analyses, compare a company's valuation across different economic regimes, and generate natural-language investment memos from model outputs.

The vision: give any investor — institutional or retail — the ability to ask "what is this company worth, and why?" about any publicly listed company in the world, and get a rigorous, methodology-grounded answer in minutes.

Why this matters

Equity research today is a privilege. It is expensive to produce and expensive to access. The analysis that moves capital is concentrated in a small number of institutions.

AI changes that equation. Not by replacing financial judgment, but by making it scalable. The same methodology that takes an analyst team weeks to build can, with the right architecture, run overnight on every company in an index.

I am not suggesting algorithms should replace human investment decisions. I am suggesting that the quality of information available to make those decisions should not depend on the size of your research budget.

That is what this project is about.

Follow along

I will be publishing the methodology in more detail, the valuation framework, the stress testing approach, the earnings quality models, on my Substack, blog and here on LinkedIn via a post.

If you are interested in quantitative equity research, AI-assisted financial modeling, or the intersection of the two, follow along. And if you are building something in the same space — or thinking about it, I would genuinely love to connect.

A presentation providing additional detail on the project, methodology, architecture, and outputs, is attached to this post.

Substack: https://substack.com/@pedrosantospinto

Blog: https://premioderisco.blogspot.pt

Personal page: https://pedrosantospinto.netlify.app

The Currency Graveyard

Importance of History

Sometimes all we need is a good, meaningful look back to understand some basic economic concepts — no elaborate theorizing required.

If we want to identify the pillars that a currency must rest on to earn the status of world reserve currency, with all the privileges that entails, we need only study those that have played that role throughout the last few centuries. History, as usual, does the heavy lifting.

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So what are those pillars? There appear to be three constant building blocks that the supporting country or economy must possess:

1. The ability to run a sustained trade deficit. The economy behind the reserve currency must consistently buy more than it sells. Anchored by a strong domestic market, it generates enough surplus capital to purchase goods and services from the rest of the world — and crucially, to pay for them in its own currency. This is how that currency becomes widely available beyond its borders, gains perceived value, and ends up being used in transactions between third parties who have nothing to do with the issuing country.

2. Military and economic hegemony. Hard and soft power work together to spread the currency’s reach, demand, and usage across other economies. Without this, trust is hard to project at scale.

3. Strong and independent institutions. These are the guardians of the currency’s value, capable of shielding it from short-term political temptations by controlling monetary flows and, with them, inflation. Their independence from the executive and legislative branches is what prevents those in power from debasing the currency for electoral convenience.

Apply this simple framework to the current holder of reserve currency status, the dollar, and one might reasonably conclude that the United States is working rather hard to disqualify itself. Two of the three pillars are under relentless and, at times, breathtaking attack, all under a strategy grandly branded as Make America Great Again.

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People often say that ignorance can sometimes be a blessing. Unfortunately, in most cases, it is a curse — and one whose consequences fall not just on the ignorant, but on the lives of countless others who never asked to be part of the experiment.

Based on: “The decline and fall of the Roman currency empire”, The Economist, March 26, 2026.

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