R
RESEARCH HUB
Intelligence Brief
Finance AI Tools Playbook

10 Claude Prompts That Replace a Bloomberg Terminal

A Bloomberg Terminal costs $2,000/month. Claude costs $20. Here are the 10 prompts that close the gap — plus the bigger picture on where the real capital opportunities are right now.

The Bloomberg Terminal charges $2,000/month for financial data, analytics, and research tools. For 40 years that price was justified — because the data and the analysis lived in one place and nobody else had it.

That moat is collapsing. Not because the data is free — it's not. But because the analysis layer, which used to require a trained analyst with expensive tools, can now be replicated by anyone who knows how to ask the right questions.

Here are the 10 prompts that do the actual work.

💡 How to use these: paste a company's annual report (10-K), earnings transcript, or SEC filing directly into Claude — or give it a ticker and ask it to reason from public knowledge. For real-time data, combine with free sources: SEC EDGAR, Yahoo Finance, company investor relations pages.
1

The Fundamental Dissection

What a senior analyst does first: find where the money actually comes from and whether it's growing.
Analyse [COMPANY] as a senior buy-side analyst. Cover:
1. Revenue breakdown by segment and geography (last 3 years)
2. Gross margin trend and what drives compression or expansion
3. Free cash flow vs. reported earnings — where do they diverge?
4. Capital allocation: buybacks, dividends, acquisitions, capex
5. One metric management buries but that matters most

Flag any red flags you find. Be blunt.
2

The Moat Stress Test

Buffett's most important question: is the competitive advantage real, and is it widening or narrowing?
Assess [COMPANY]'s economic moat. For each potential source of moat —
switching costs, network effects, cost advantages, intangibles, efficient scale —
rate it: Strong / Weak / Illusory. Explain why.

Then: who is most likely to erode this moat in the next 5 years?
What would be the first sign it's happening?
3

The Management Quality Scan

Capital allocation decisions compound over time. Bad managers destroy value quietly.
Based on [COMPANY]'s last 4 earnings calls and annual letters,
evaluate management quality:

1. Do their words match their actions over 3+ years?
2. How do they talk about failures vs. successes?
3. What metrics do they emphasise — and are those metrics convenient?
4. How do they allocate capital in good years vs. bad?
5. Owner-operators vs. hired managers: who is running this?

Score them 1-10 on capital allocation discipline. Justify it.
4

The Bear Case Builder

The best investors stress-test their own thesis before the market does it for them.
I own [COMPANY] stock. Construct the most compelling bear case.
Don't steelman weak objections — find the actual structural risks.

Cover: competitive dynamics, regulatory exposure, balance sheet risk,
technology displacement, key-person risk, and valuation.

Then tell me: what single piece of news would make you sell immediately?
5

The Valuation Reality Check

Most retail investors pay too much. This prompt forces you to look at what you're actually buying.
Value [COMPANY] using three methods:
1. DCF: what growth rate and terminal multiple does the current price imply?
2. Comparable companies: how does it trade vs. peers on EV/EBITDA, P/FCF, P/S?
3. Private market value: what would a strategic acquirer pay?

What's the implied upside/downside at current price?
What assumptions have to be true for the bull case to work?
6

The Earnings Call Decoder

Executives don't lie outright. They redirect. This prompt finds what they don't want you to notice.
[Paste earnings call transcript]

Analyse this earnings call as a seasoned short-seller would:
1. What questions were answered vaguely or deflected?
2. What metrics did they stop reporting vs. previous quarters?
3. What language changed compared to the prior quarter's call?
4. What did analysts push back on hardest?
5. What's missing that you'd expect them to mention if things were good?
7

The Industry Map

No company exists in isolation. The best investments ride a rising tide in their sector.
Map the [INDUSTRY] landscape:
1. Who are the top 5 players and what share does each hold?
2. What are the key value chain steps, and where is margin concentrated?
3. What macro/regulatory tailwinds or headwinds are building?
4. Which part of this value chain is AI most likely to disrupt in 3 years?
5. If I had to pick one company positioned best for the next decade, who and why?
8

The Catalyst Timeline

Knowing what's going to happen isn't enough. You need to know when.
For [COMPANY], list all known and likely catalysts in the next 12 months:
- Earnings dates and key metrics to watch
- Product launches, regulatory approvals, contract renewals
- Macro events that specifically affect this stock (rate decisions, elections, etc.)
- Insider lock-up expirations, index inclusions/exclusions

Which catalyst has the most asymmetric potential? Bull and bear scenario for each.
9

The Macro Sensitivity Map

In 2026, macro moves everything. You need to know how your positions behave before it happens.
How does [COMPANY] perform across these macro scenarios:
1. Rates rise 100bps
2. Rates cut 100bps
3. Recession (GDP -2%)
4. Dollar strengthens 10%
5. AI capex cycle slows
6. Regulatory crackdown on [their sector]

For each: what's the mechanism, and what's the estimated revenue/multiple impact?
10

The Full Portfolio Intelligence Brief

This is the Bloomberg Terminal replacement. One prompt, systematic output, weekly cadence.
My portfolio: [list your holdings and position sizes]

Give me a weekly intelligence brief covering:
1. Material news for each position this week
2. Any changes to the investment thesis
3. Sector rotation signals I should watch
4. What the smart money (institutional 13F filings) is doing in my sectors
5. One new idea I'm not holding that fits my style

Format as a briefing memo. Flag anything requiring urgent attention.

The Bigger Picture: Where the Real Opportunities Are

The prompts above are tactical. But the more important question is: where do you want to be positioned for the next 3 years?

🏗️ AI Infrastructure — The Picks & Shovels Play

Everyone's talking about AI models. The smarter money is in what the models run on. Power, cooling, networking, custom silicon. The AI buildout requires massive physical infrastructure — and unlike the models themselves, infrastructure has clear, defensible pricing power.

Watch: Data center REITs, power utilities in AI corridors, copper & cooling suppliers, custom ASIC designers.

⚛️ Quantum Computing — The 5-Year Horizon

The quantum encryption story from this week (100K qubits to crack RSA) is one signal. Quantum is moving from lab to commercial application faster than the consensus expects. The plays aren't the pure-quantum names trading at 100x revenue — it's the traditional hardware companies pivoting credibly into quantum-adjacent work.

Watch: IBM (most credible quantum roadmap), IonQ (pure play, volatile), companies building post-quantum cryptography software.

🧬 Longevity & Biotech — The Silent Wave

AI is accelerating drug discovery by orders of magnitude. AlphaFold solved protein folding. Clinical trial design is being compressed. The companies that combine proprietary biological data with AI pipelines are sitting on massive moats that the market hasn't fully priced.

Watch: Companies with drug discovery AI platforms + large proprietary biological datasets. Not generic AI-in-biotech hype — specific data moats.

💰 The Monetisation Play: Information Arbitrage

The most underpriced opportunity right now isn't a stock — it's a business model. The gap between what institutional investors pay for financial intelligence ($2,000+/month Bloomberg, $50K/year data services) and what's now possible with AI + public data is enormous. Newsletters, research products, and intelligence services built on AI-augmented analysis are the new media arbitrage. Very low cost base, very high willingness to pay.

The meta-point: The Bloomberg Terminal isn't expensive because the data is rare. It's expensive because the synthesis was expensive. Claude reduces the cost of synthesis to near-zero. What remains valuable: proprietary data, proprietary networks, and the judgment to ask the right questions. That last one is still scarce. Build it.

Inspired by @JasonL_Capital on X · 60.9K views · Expanded with original analysis by Research Hub · Mar 2026

Back to Intelligence Brief

Continue Reading

The Fuckening. 6 min → Stability Is the New Status. 6 min →