This issue starts something new. Until now I’ve been testing other people’s releases: Anthropic’s agents, Claude Fable. Today we start building your skill stack, level by level.

Level 1 is skills.

In the next 10 minutes you’ll learn what a skill actually is, how to build financial skills that survive contact with a real close, and you’ll walk away with the master file: the skill.md that builds all my other financial skills.

Let’s go.

First, what a skill actually is

You’ve used Claude in chat. Maybe Claude in Excel, Cowork, or Claude Code. And I can bet money on this: you’ve had the same prompt produce three different outputs on three different days.

That inconsistency is the tax you pay for working from prompts.

A skill is how you stop paying it.

In The Big Short, Margot Robbie explained subprime mortgages from a bubble bath in 30 seconds. I’m not Margot Robbie. And I don’t have a bath either. But I’ll try to be just as precise.

A skill is an SOP (Skope of Work) your model follows. It tells Claude exactly how to produce a specific output: the steps, the format, what to check, what to refuse. Every time. In your format. That’s it. That’s the whole concept.

Or even shorter: a prompt is you explaining the task again every morning. A skill is the task explained once, permanently, by someone who was paying attention.

This is the first real game-changer for your time and your mental health. And instead of handing you a library of someone else’s skills you’d still have to customize, I’m going to teach you to build your own.

Then give you the file that does it with you. How about that?

The problem nobody warns you about

When you start building financial skills, the first one is fun. The fifth is fine. By the fifteenth, you end up with a mess.

I know because I opened our skills folder last month and audited it. Every skill structured differently. Half of them with dollar thresholds hardcoded into the prompt. One file says “company profile,” another says “business context,” a third says “client info.” Same file. Three names. And when something breaks during close, you can’t tell if it’s the data or the skill, because there’s no standard to check against.

So here’s the rule I now live by, and I’ll say it the blunt way: a skill stuffed with hardcoded values is not a skill. It’s a script.

A script works for one project, one month, one threshold. A skill survives a new month, a new policy, a new client, without anyone touching the skill body. If your “skill” breaks when the threshold changes, it was never a skill.

The fix: a meta-skill that builds skills

We built a meta-skill. One md file, dropped into your Claude skills folder. From then on it runs in two modes.

BUILD mode. You say “build a skill,” and it interviews you before writing a single line. What’s the one responsibility? (Exactly one. Not two.) What feeds data into it, and what does it feed into next? Which context files does it read? What can’t be automated yet? Only after the interview does it produce the full SKILL.md, in a fixed 10-section template: role, scope, context, tools, input, process, escalation rules, output schema, what it does NOT do, and critical rules.

VALIDATE mode. You paste any existing SKILL.md and it audits the thing line by line. Hardcoded values, missing sections, banned terminology, weak escalation logic. You get a pass/fail report with exact quotes of every violation and the fix for each.

Build with it, then validate its own output. The skill checks its own homework.

3 rules worth stealing even if you never download my skill file (below)

1. The FM approval gate is a hard block. Every skill has a step where the agent stops and waits for explicit human confirmation. Not “human review recommended.” The agent literally cannot continue without it. In finance, this is the difference between an agent you can deploy and an agent you can demo.

2. Confidence scoring with deduction logic. The agent starts at 1.0 and deducts for specific issues: 0.1 per missing data field, 0.1 per unresolved source discrepancy, 0.1 per unclassified vendor. Below 0.7? It escalates instead of deciding. No vibes-based confidence. Remember: every downstream skill is betting on this one’s output being right, so the errors don’t add up, they multiply.

3. Never let the LLM “just calculate.” Every calculation follows the same pattern: find the formula, show it, build the logic, calculate, return formula plus inputs plus result. If the formula is missing, the agent escalates. It never estimates. This one rule kills 90% of the hallucinated-numbers problem.

If you take nothing else from this issue, take these three. Paste them into every skill you ever write. I have.

How to use it

1. Download the SKILL.md below

2. Add it to your Claude skills (Settings → Capabilities → Skills, or drop it into your Claude Code skills folder)

3. Say “build a skill”. Or paste your messiest existing skill and say “validate this”

Start with validate. Watching it tear apart a skill you thought was solid is the fastest way to internalize the standard.

Watch it work: building a revenue reconciliation skill in 8 steps

I ran the full loop so you can see exactly what the interview feels like. The task: a skill that reconciles Stripe/Chargebee revenue against QBO GL for the close month.

The skill asks you eight questions before it writes a single line. These eight questions ARE the method. Even if you never download the file, run your next skill through them by hand and it will come out better. Here’s each one, and why it’s there.

Step 1. What is the one responsibility of this skill? Exactly one. Not two. The moment a skill does two things, you can’t tell which one broke. Mine: reconcile Stripe/Chargebee revenue against QBO GL for the close month, flag mismatches.

Step 2. Which orchestrator does it belong to? Every skill lives inside a bigger workflow: Month-End Close, Controlling, Forecasting. Naming the orchestrator up front means you’re building a system, not a pile of disconnected tools. Mine: Month-End Close.

Step 3. What feeds data into it, and what does it feed into next? The data chain. Mine receives from data-ingestion-agent and passes to revenue-recognition-agent. If you can’t answer this question, you don’t have a skill yet, you have a wish.

Step 4. Which context files does it read? This is where hardcoded values go to die. The thresholds, the CoA mapping, the business specifics all live in named context files.

Step 5. Which connectors does it need? Only what’s actually required, with read/write spelled out. Mine: QBO (read), Chargebee (read). A skill that asks for write access it doesn’t need is a skill your future self will regret.

Step 6. Which steps can’t be automated yet? Anything still manual becomes an explicit [MANUAL STEP] block instead of a silent assumption. Mine: the FM confirms the Chargebee export covers the full month before the skill runs.

Step 7. Who owns it? Every skill has a named owner. A name. When it breaks during close, you know exactly whose Slack lights up.

Step 8. Has it been tested on a real close month? This sets the status field, and I love this part: Idea → Testing (1 month) → Validated (2+ months) → Standardized (team standard). A skill that’s never touched real data is an Idea, and the file makes you admit it.

Eight answers later, it produced the full SKILL.md: the role, the scope, the critical rule (”Never modify accounting records without a human approval. Never guess missing financial data. If confidence < 0.7 or risk is high → escalate.”).

Then I did the part I recommend you do first: I validated it. And to make it interesting, I planted two bugs: a hardcoded $500/1% threshold and a missing connector.

The validator caught both. 28 of 30 checks passed, and the two failures came back with exact quotes and exact fixes. The missing kpi_context surfaced as four separate violations, which is what happens with a real gap in real life: it doesn’t fail once, it leaks into everything downstream.

That’s the loop. Interview, build, validate, fix, deploy. No blank page anywhere in it. Your next skill takes twenty minutes, and it’s better than the ones you spent hours on.

My meta-skill file (built and tested by ~20 finance managers)

Here it is, the most-used file in our entire skill library. The skill that builds all the other skills.

Read more

Then on Tuesday, Anthropic released Claude Fable. Within an hour, our team chat at Fuel turned into a room full of CFOs asking the same question: who’s tested it, and on what?

So I tested it the same night. Both models, side by side. By 1am I had my answer, and the title of this issue.

Fable found 3 issues. One of them is worth about $30M. One of them wasn’t Opus’s mistake. It was mine. Let me show you everything.

First, what Fable actually is

Back in April, Anthropic built a model called Mythos. And then they refused to release it. Too powerful, they said. Too dangerous. They locked it inside a restricted program called Project Glasswing, partners like AWS, Microsoft, Apple, and CrowdStrike only, plus a few biology researchers. The rest of us got to read rumors about it from the outside.

This week’s Claude Fable is that same Mythos model. What changed is the supervision. Fable ships with safeguards: push it into genuinely high-risk territory (serious cybersecurity work, biology, chemistry, attempts to clone the model itself) and it won’t answer. It reroutes you to Opus 4.8. Anthropic says this triggers in under 5% of sessions. Karpathy says the safeguards are “a little too trigger happy” at launch. Both can be true.

The practical part, because the practical part is the job:

→ The full-power Mythos also exists. Businesses that touch it accept mandatory 30-day data retention for safety monitoring.

→ For finance, the guardrails are a non-issue. The reroutes target cyber, bio, and chemistry. FP&A work essentially never trips them. Your forecast is not a bioweapon, no matter what your CEO says about your downside case.

→ Pricing: $10/$50 per million tokens. Double Opus. Ouch.

So that’s who walked in on Tuesday: the dangerous one, on supervised release, wearing a friendlier name. Karpathy called it a major-version-bump step change. I’ve seen one founder had it build a working product live during a customer call.

I was charmed too. But I’ve been a CFO long enough to know you don’t believe charm. You check the references. So I ran an audit.

The audit setup: same company, same prompts, both models

You know Numbr, my imaginary $93M SaaS from Issue 002. Officially our lab rat now.

I took the exact 3-statement model we built last issue and gave Opus 4.8 and Claude Fable two identical tasks. Same files. Same prompts, word for word.

Task one: build the interactive forecast dashboard (the exact prompt from Issue 002).

Task two: “make an analysis of this business using this forecast.”

Here’s what the audit found.

Finding 1: Opus misstated a number and never noticed

Both models built a working dashboard. Both looked good.

Then I checked the numbers. Opus displayed the wrong total revenue. The correct number was in the file I gave it. It showed a different one and never flagged it.

Fable showed the correct revenue. It also added a save-scenario feature and surfaced the metrics that matter for SaaS: NRR, revenue per FTE, Rule of 40, headcount efficiency. Opus gave me the generic revenue-costs-margins set.

Finding 2: a $30M omission

Same analysis prompt to both. They agreed on the thesis: expansion-driven growth, heroic operating leverage, no real bear case. Skim both and you’d call it a tie. It’s not a tie.

Fable caught the tax problem. Numbr’s model runs an 8% effective tax rate flat for five years while pretax income passes $150M. Fable flagged it, quantified $25-35M of overstated cumulative net income and cash, and gave the fix: NOL shields run out at this profitability, model toward 21% plus state by FY28-29.

Opus didn’t mention tax once. Neither has any Claude model I’ve tested.

Two more catches came with it. The SBC add-back: “Adjusted EBITDA” flatters the picture by 3-7 points, net income (44% margin) is the more honest line.

And capital allocation: $744M of cash piling up with no deployment plan. M&A, buybacks, reinvestment, why is this money sleeping? Opus called the cash pile good news and moved on. Board members do not move on.

Uncomfortable part: the tax assumption wasn’t Opus’s error. It was in MY model. Fable didn’t just audit Opus. It audited me. That’s the moment I knew this release was different.

Finding 3: communication issues

My favorite finding, and it’s not a number.

I can spot a Claude-written document from across the room.

Fable writes straight to business. It called the marketplace line “essentially dead.” It said the downside case “would get laughed at” by a board. It ended with marching orders: build an NRR stress case, fix the tax ramp, decouple headcount, kill or fund the marketplace.

Opus ended with a summary. For documents that go in front of CEOs and boards, that’s the difference between one you forward and one you rewrite at 11pm with your name on it.

Fable 5 vs Opus 4.8

So, to wrap up, here’s the full Opus error log vs Fable:

→ Wrong total revenue on the dashboard (the correct number was in the file)

→ A growth multiple anchored to the wrong baseline year

→ Missed the tax problem entirely (~$25-35M of overstated net income and cash)

→ Missed the SBC distortion (Adjusted EBITDA flattered by 3-7 points)

→ Missed the capital allocation question ($744M of cash, no deployment plan)

To be fair to Opus

Here’s what Opus did better. It did do some things better.

→ It produced a proper board-ready scenario table: ARR, revenue, margins, cash, Rule of 40 across all three scenarios. Fable’s document didn’t have one. For a board deck, I’d want that table.

→ It made one genuinely sharp catch: the headcount slider exists in the playground, but none of the preset scenarios ever flex it. That’s a more precise diagnosis than Fable’s flatter “headcount is held identical.”

→ It noticed that every cost line is modeled as a fixed % of revenue, meaning no infra-scaling or support-load risk anywhere in the model. True and useful.

But the pattern of the evening was consistent: Opus produced cleaner-looking structure with more mistakes inside it (it also misquoted a growth multiple, its second numerical slip of the night, stated with full confidence). Fable produced blunter documents with numbers that held.

So, are we really over?

Mostly.

Fable costs double. The high-volume plumbing, categorization, tagging, short summaries, stays with cheaper models. Opus and I are still friends. It keeps the routine tasks. We’re being very adult about it.

But the judgment work, forecast analysis, anomaly investigation, anything where a model has to hold a long chain of reasoning across an entire P&L, that’s Fable’s now.

It’s the first model that performed at a level I’d call senior on my tests. It caught what a good controller catches, including my own mistake.

One evening isn’t a verdict though. It’s a strong signal, the kind you take seriously and verify anyway. This week I’m moving my heavy analysis to Fable and putting it through more experiments. More results soon.

Tell me what you think about Fable in the subscriber chat. I read every message.

And as is tradition in good marketing around here: this issue was brought to you by our founding partners, Hampton, FIF Collective, Light, and Spendesk. Grateful to them for backing this newsletter before model releases started writing my content calendar.

— Alyona

Look at what they pulled off. On May 5th they dropped ten finance agent templates and the whole internet lost its mind on cue. Every AI influencer reposting within the hour. Half my LinkedIn feed, honestly.

And Anthropic’s marketing deserves its own newsletter issue. Their creative team (who, I promise, are not agents, and not Claude) keep coming up with the most human way to sell AI. Go look at their Instagram. It’s art, illustration, in-person Code with Claude meetups, real people making real things.

They recently ran a roundtable in Tokyo where they put painters, bakers, and engineers in the same room, with one of Japan’s last three living sento mural painters working on the wall behind them. Look at that. Crazy that this is what AI marketing looks like now.

Anyway. Away from the marketing for a second: financial services is now Anthropic’s second-biggest source of enterprise revenue, right behind tech.

But as a CFO-turned-founder, I know the single most costly habit in finance is believing the marketing.

So I closed Slack. Opened a fresh Claude window. And spent the rest of the weekend testing Anthropic’s agents on an imaginary company called Numbr. (Sorry, Numbr, if there’s a real one out there. I didn’t mean you.)

First, let’s agree on what a word “agent” means

Before we go further, we need to talk about a word.

I truly hate how everyone is calling everything an “agent” right now. Email automation? Agent. Macro in Excel? Agent. N8N flow with a ChatGPT call bolted on the end? Bro, that’s still just a N8N flow.

We’re all smart finance people here. We do critical thinking for a living. So let’s actually agree on what each word means before someone sells us a product that doesn’t do what we think.

→ Automation. A rule-based workflow. You write the rules, it follows them, forever. A lot of the “AI-native” stuff you see marketed in finance is simply rule-based workflows.

→ Skill. A Claude feature. A reusable instruction set you hand the model: how to do a thing, the steps, the format, what to check, what to refuse. Find them under Customize → Skills (toggle any on or off). Build your own with the built-in Skill Creator, which interviews you and writes the file. You’ll see a lot of my skills on the pages of this newsletter.

→ Plugin. A bundle of skills, connectors, and helpers packaged together. Basically an app inside Claude. Add one under Customize → Plugins → “+” → Add marketplace, then paste a GitHub URL, which is exactly how we’ll install the finance ones in a minute. You can build your own too.

→ Routine. A scheduled workflow you set once and let run. Smarter than automation, not as independent as an agent. In Cowork these are Scheduled Tasks: describe the recurring job, it fires on a schedule (as long as your laptop’s awake and Claude’s open).

→ Memory. LLM remembering your context, preferences, and files across sessions, so you’re not re-explaining your business every morning. Turn it on in settings. Tip: you can also move your memory from ChatGPT to Claude. Open Claude, go to Settings > Capabilities > Memory, and click Start Import. Copy the extraction prompt provided and follow the instructions.

→ Cowork. A Claude feature. The desktop workspace where Claude works with your files, tools, and skills in one place instead of you copy-pasting between tabs. The finance version of the setup engineers have always had.

→ Agent. A model that decides what to do next on its own. It plans, picks its tools, checks its own work, tries again when it’s wrong. Calls you never scripted.

Most things being marketed as “agents” right now are actually plugins, skills, or automations. That’s not bad! It’s just not what the word means.

So the ten “finance agents” everyone lost their mind over?

They’re plugins. Packaged skills with a defined workflow, called from inside Cowork, with mandatory human review on every output. That’s not an agent in the strict sense. That’s a really well-engineered plugin.

Cool. Now we can talk about them honestly.

They’re not even inside Claude. They’re on GitHub.

This was the first thing that surprised me. The whole thing lives on GitHub. As code.

At github.com/anthropics/financial-services.

For anyone not from the engineering side, GitHub is where code lives on the internet. It’s the version-controlled filing cabinet of the entire software industry. Anthropic publishing finance agents there means: here’s the open-source recipe, you go install it.

And once you’re on GitHub, you start noticing things.

There are other finance agents up there. From other people. From other companies. Some of them, honestly, better than what Anthropic shipped. The marketing made it sound like Anthropic invented finance-flavored AI on Tuesday. The repo tells a different story.

My first impression: the marketing was louder than what’s inside.

That’s not me being a hater. Anthropic is one of the companies I bet on hardest. I’d call myself a Claude psycho on a public post, and I have. But part of building your finance engineering skillset is being able to read marketing carefully - not just receive it.

Okay. Let’s install them.

How to add them (5 minutes, no engineering required)

The install is the easy part.

You need three things: Claude desktop (the app, not the browser), a paid plan, Cowork enabled.

Then:

1. Open Claude desktop

2. Settings → Plugins → Add plugin → Add marketplace

3. Paste: https://github.com/anthropics/financial-services

4. Hit Sync

Done. The marketplace shows up in your plugin directory.

When you open it, you’ll see the full list:

Built for corporate finance (us):

• Model builder - DCF, LBO, 3-statement, comps, live in Excel

• Financial analysis - same core, broader scope

• GL reconciler - finds breaks, traces root cause, routes for sign-off

• Month end closer - accruals, roll-forwards, variance commentary

• Earnings reviewer - earnings call + filings → draft update to note

Built for investment banking / PE / wealth management (not us):

• Equity research

• Investment banking

• Fund admin

• KYC screener

Other:

• Finance (general), Marketing, Small business

A lot of these are calibrated for investment banking, private equity, and wealth management workflows. Not for the FP&A manager, controller, or head of finance at a SaaS company.

For corporate finance (the people this newsletter is actually for) three of them are worth your time today:

→ Model builder (with caveats, see below)

→ GL reconciler

→ Month end closer

I’m going to walk through Model Builder in detail right now.

Inside the Model Builder

When you open Model Builder, it offers four model types:

→ DCF - discounted cash flow

→ LBO - leveraged buyout

→ 3-statement - IS / BS / CF, integrated

→ Comps -trading multiples

A thing I noticed immediately and I think it tells you exactly who Anthropic built this for.

There’s no model for recurring revenue forecasting. No unit economics. No MRR waterfall. No cohort retention. No usage-based pricing model.

If you run finance at a SaaS company - that list is your whole job. And it’s missing.

DCF and LBO are pretty standard banker-side instruments. Most corporate finance teams don’t live in those models day-to-day. The only one from the four that genuinely maps onto operational corporate finance is the 3-statement.

So that’s where we’ll go deep.

What’s good inside the 3-statement skill

I went into the repo and read the 3-statement skill file line by line. (Reading skill files is now part of my actual job. I did not expect to ever type that sentence, and yet here we are.)

Three things Anthropic got right.

1. Formulas over hardcodes. Every projected cell is a formula. Every assumption is labeled, cited, and traceable to an input. No typed numbers in calculation cells. This is the right discipline — every cell can be audited, every driver flexed, and the model behaves like a model instead of a printout.

2. Verify step-by-step with the user. This part I love. The skill is explicitly told NOT to build the entire model end-to-end and present it complete. It’s told to stop at each statement. Show the work. Wait for confirmation. Catch errors early.

The exact language from the skill:

→ After mapping the template → show the user the sections, confirm before touching any cells

→ After populating historicals → show the historical block, confirm values/periods match source

→ After building IS projections → run subtotal checks, confirm before moving to BS

→ After building BS → show the balance check (Assets = L+E) for every period

→ After building CF → show the cash tie-out (CF ending cash = BS cash)

→ Do NOT populate the entire model end-to-end and present it complete. Break at each statement. Show the work. Catch errors early.

This is human-in-the-loop done right. After every wrong number I’ve watched Claude confidently produce, this is exactly the discipline I want.

Steal this pattern. Paste it into every skill you ever build. I have.

3. Real integrity checks. Master Status reads PASS across all periods. Every reconciliation gets its own tab. Every guardrail shows up visibly on the Cover and Checks tabs. That’s good engineering, full stop.

What to keep in mind before we start

The Model Builder agent ships as a general template. No business context. No industry assumptions. No idea what your revenue model is.

To get a forecast that actually reflects your company, you have to customize the underlying skill file first. With business-specific context, drivers, assumptions, metrics.

You can do this inside Claude Chat. No engineering required. But you do need to do it. Out of the box, the model is generic.

Let me show you what “out of the box” actually looks like, and what happens after you customize.

Meet Numbr, my imaginary company

To test this honestly, I needed real-looking data. So I generated a fictional company with Claude and tested on that.

Meet Numbr, Inc. – a $93M ARR fintech SaaS selling financial intelligence tools to banks, hedge funds, and asset managers.

→ Three legal entities (US, Netherlands, Singapore)

→ Two revenue streams (recurring subscriptions + usage-based API billing)

→ Freshly cash-flow positive in 2025

→ Complex enough to be interesting, clean enough to follow along

The inputs are three Excel files exported straight from their systems:

→ QBO export — Cash-basis P&L with $3M intercompany, Balance Sheet that doesn’t tie

→ NetSuite export — proper accrual books, $89.5M revenue, $12.6M operating income

→ HubSpot export — pipeline, customer data, deal stages

This is what real corporate finance actually looks like.

I’m dropping the Numbr files here. Took me ~3 hours to generate and clean. You can run the same test yourself.

Numbr Hubspot Export

78.9KB ∙ XLSX file

Download

Download

Numbr Netsuite Export

45.3KB ∙ XLSX file

Download

Download

Numbr Qbo Export

42.4KB ∙ XLSX file

Download

Download

Let’s see what we got out of the box

I switched on Model Builder, pointed it at the three files, and asked for a 3-statement model.

It worked. It ran the step-by-step verification exactly like the skill promised. Stopped at each statement. Showed the balance check. Tied out cash. As pure engineering, it did what it said on the tin.

It built a generic 3-statement model.

And it had no idea Numbr was a SaaS company. It skipped the recurring revenue build. It ignored churn. It never split subscriptions from usage-based API billing, even though that split is sitting right there in the HubSpot and NetSuite files.

ARR, net revenue retention, cohorts, all gone. It forecast revenue as one line growing at one rate, the way you’d model a company that sells a single widget to a single kind of customer.

I gave the Financial Analysis plugin a shot too. It was a little better. A few more ratios, a bit more commentary. But still average.

Lesson learned: Anthropic’s plugins are good, technically, but they don’t work out of the box.

So I went further, and customized the skill, which really is great, to fit my imaginary $93M SaaS.

Let’s see what happens when we customize the skill

I did it in Claude Chat.

I described the business model, the challenges, the revenue streams, the markets, and the exact financial model I wanted out the other end.

I kept Anthropic’s skill as the base, because most of the rules in it are good. No reason to start from scratch when the bones are right.

You can download my custom SaaS 3-statement skill file below (+ prompt I use to turn the agent's model into an interactive dashboard you can play with).

Read more

I was on a morning run with Haku, my husky, listening to My First Million. The episode with Amjad Masad, founder of Replit. Haku was sniffing every tree like usual. I was half-listening like usual.

Then Amjad said something I haven’t been able to shake.

He was talking about the singularity.

The concept comes from astrophysics. A singularity is what happens at the center of a black hole. Past the event horizon, the math breaks. The equations that describe everything else in the universe just stop working. Gravity, time, distance. All of it goes weird.

You can’t predict what happens next, because the rules you’ve been using no longer apply.

The future stops being uncertain and becomes literally undefined. There are no formulas for it. It’s the universe shrugging at you.

At this point, stay with me: this is a finance newsletter, I promise.

Vernor Vinge, an early computer scientist and sci-fi writer, borrowed this idea in the ‘90s and applied it to technology. His argument: at some point, AI advances fast enough that the pace of change itself becomes unpredictable. Fast, then faster, then accelerating. The second derivative goes exponential.

Once you’re past that point, you can’t project forward anymore. The planning tools we use to think about the future stop being useful. Five-year strategy decks become creative writing.

Amjad’s point was: we’re there. Right now.

Honestly, when you look around, it’s hard to argue.

In 2022, GPT-4 dropped and felt like a revolution. Before that, we got a major model every two years. Now we get one every few weeks. Sometimes every day. Sometimes I check X in the morning and someone has built a thing I didn’t know was possible by lunch.

Each new model unlocks capabilities that haven’t been productized yet. A pile of potential energy just sitting there, waiting for someone to figure out what to build with it.

I’ve been a CFO. I’m now a founder. I’ve pivoted our strategy three times in the last month. Three times. In one month. The ground just won’t stop moving.

Nobody knows where this goes. But the people who figure things out in undefined moments are the ones who win the next decade.

For finance, I want to call them Finance Engineers.

How we vibecoded so hard we accidentally became engineers

A few years ago, a company called Clay (yes, that Clay) had this problem. Their go-to-market team had people doing something that didn’t fit any existing title. They were too technical to be SDRs. Too sales-brained to be engineers. This weird hybrid - data, revenue, automation - all in one person.

So Clay made up a name: GTM Engineer.

Today, 100+ GTM Engineer roles get posted every month. Every growing startup needs one. A job title that literally didn’t exist became a career path - because one company said “this is a real thing” and built the infrastructure around it.

There’s something similar happening in finance.

In 2025, Andrej Karpathy - co-founder of OpenAI, former head of AI at Tesla, and now a part of Anthropic, probably one of the five smartest people working on AI right now - wrote a post that changed how an entire industry thinks about building software.

He described a new way of creating things. You don’t write code. You describe what you want - in plain English. The AI writes the code.

He called it vibe coding.

This blew up in engineering. And then (because apparently finance people can’t resist a good tool) it jumped the fence.

I’ve been vibe coding for months now. I built a cash flow forecasting tool. I spend my weekends testing the new finance agents from Anthropic (more on this + a full tutorial in the next issue). I went to one of Rillet’s vibe coding workshops and watched a CFO build a pricing calculator from scratch in under 20 minutes with Claude Code.

Aesha, a financial manager on my team at Fuelfinance, built an interactive dashboard that pulls live data and visualizes it dynamically. She’s not an engineer. She just opened the tool and started building.

This is happening everywhere. Finance people (FP&A managers, controllers, heads of finance, CFOs) are building software.

They didn’t plan to become engineers.

They vibecoded so hard they accidentally became one.

The skillset and the mindset of a finance engineer

So who are these people?

Let’s get specific. Take a Head of Finance at a $15M company. Series A, maybe early Series B. Small team - maybe it’s them and two analysts. Maybe it’s just them.

The core finance skills didn’t go away. AI doesn’t replace any of that, it amplifies it. Without the foundation, you’re just someone with Claude and a very, very dangerous amount of confidence (in finance, 99% right is still wrong).

But the layer on top is new. And it’s expanding every month.

Here’s what I’ve come to believe: finance engineering isn’t a job title. It’s a skill layer that’s being added to every finance role. The Controller still closes the books, but now they automate the close. The FP&A Manager still forecasts, but now they vibe code the dashboard. The Head of Finance still runs the function, but now they build the systems that run it. Same people. Same titles. Fundamentally new capabilities. And nobody’s teaching it, which is kind of why we’re all here.

And here’s the thing nobody’s saying (well, maybe only on reddit) - this transition isn’t always easy.

Sometimes your CEO is pushing AI on the team while you haven’t even gotten the data right yet. You’re being told to “implement AI in finance” while your chart of accounts is a mess and your ERP hasn’t been updated properly. Cool. Sure. Let me just connect Claude to our garbage data and see what hallucinations we get.

Sometimes you’re doing this out of pure fear. You think “how fu*ked are we?” when someone fed AI two budgets and got back a complete variance analysis that looked like a real analyst wrote it.

And you thought: wait. Is that my job? Can it do my job? Should I be scared right now??

And sometimes - honestly? Sometimes you open a vibe coding tool for the first time and build something in 30 minutes that would’ve taken you two weeks, and your brain just... short-circuits. You can’t have another reaction. It’s like seeing a magic trick except it’s real and you did it and now you don’t know what your job even is anymore.

All of these reactions are valid. All of them are happening simultaneously in finance teams everywhere.

Here’s what I think is true:

I don’t know exactly where this goes. I really don’t. Anyone who says they have a five-year roadmap for AI in corporate finance is selling you something.

But I’m pretty sure about one thing: if you don’t learn this new skillset - whatever we end up calling it - you can be replaced by someone who did.

So stay for the ride :)

Building your skill stack for the new era of finance

So here’s what this newsletter actually is.

My team and I are personally testing dozens of tools and workflows every week to figure out what actually works for the new era of finance.

My dear CFO and head-of-finance friends (the ones I’d genuinely call finance engineers) are testing too. And they wanna share.

Right now, the newsletter runs on two formats:

→ My guides on engineering stuff in finance. When I automate something, I’ll show you the prompts, the setup, the output, and what went wrong. When I test AI agents on our close, you’ll see the screenshots. This is a build log, not a thought piece.

→ My finance friends sharing what they built - step by step, tool by tool. Maybe we’ll be eating bagels or having a filter coffee on a lunch break while we prepare these.

You’ll also find thoughts beyond the finance role. Going from CFO to founder building AI taught me things I never would’ve learned inside the department. I’ll share those too - between all the practical stuff this newsletter is truly about.

The founding partners

The founding partners of The Finance Engineer are the people and communities who shaped me, both as a finance person and as a founder.

→ Hampton

Founded by Sam Parr, who built and sold The Hustle newsletter to HubSpot. A lot of what I know about writing on the internet, I learned from Sam directly.

Hampton is Sam’s founders community I’ve been a member of, and a huge part of why I’ve grown as a founder over the last couple of years. One day I hope The Finance Engineer becomes for finance leaders what Hampton is for founders. If you’re a founder doing $3M+ a year, Hampton is the room you want to be in.

→ FIF Collective

Run by Megan Curtin McKenna, FIF is the best women-in-finance community I’ve ever been part of, and one of the most generous, sharp networks of finance leaders I know.

Megan has built something rare. Whenever I’m testing something new or building content, FIF gets to see it first and give honest feedback. If you’re a woman in finance, I cannot recommend joining strongly enough.

→ Light ERP

Light is an AI accounting platform. AR, AP, expenses, bookkeeping, and reporting, running automatically across every entity. It’s all-in-one, built for fast-moving SaaS companies that never needed a heavy ERP. I’m watching this space closely, and they’re building it the way I’d want it built if I were starting an accounting platform today. They’re also the platform Lovable runs on. Worth a look if you’re rethinking your stack.

→ Spendesk

Spendesk is one of the most established spend management platforms in finance, and the team behind CFO Connect, one of the largest communities of finance leaders. What I’ve always respected about them is how seriously they take the finance community itself. The events, the content, the actual investment in helping CFOs become better operators. Not many vendors do this.

Grateful to all four of them for backing this early.

You’re not behind. You’re early.

Thank you for being here. Truly.

— Alyona