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One of the biggest changes in investing was the internet.
Before that, access to information was a real advantage. Getting filings, annual reports, transcripts, analyst commentary, competitor data, and market news was much harder. If you had better access to information, you could sometimes have a real edge just by doing work other people could not easily do.
Then the internet changed that.
Suddenly, individual investors could read 10-Ks, compare companies, find old annual reports, listen to earnings calls, follow industry experts, use screeners, download data, and learn from other investors around the world.
That did not make investing easy.
But it changed the game from "who can get the information?" to "who can interpret the information better?"
I think AI may be another version of that shift.
Not because AI can magically tell you what stock to buy. I actually think that is probably one of the weakest uses of it.
But because it can help investors move through the boring but necessary parts of analysis much faster.
For example, I have been experimenting with using AI to:
* Summarize long filings before reading the important sections myself
* Build tools where the same analysis is done just by changing the ticker
* Build agents to keep track of my watchlist and relevant news events
* Compare risk factors from one year to the next
* Pull out management commentary on margins, capital allocation, competition, and demand
* Compare several companies in the same industry
* Build valuation scenarios
* Stress test an investment thesis
* Help build spreadsheet formulas or small scripts for analysis
More recently, I have also been interested in MCPs and connected tools, where the AI is not just answering from memory, but can connect to outside data sources, filings, spreadsheets, or other tools.
Where instead of asking an AI:
"Is Apple stock undervalued?"
I ask:
"Compare Apple’s revenue growth, margins, buybacks, share count, free cash flow, and valuation over the last 10 years, then show me which assumptions matter most in a valuation"
This has been much more helpful.
The key thing that I have found is that: **AI should not replace the investment judgment but it should reduce the friction of getting to the judgment.**
It can help organize information, retrieve data, compare companies, find inconsistencies, and challenge a thesis.
But it still does not know what matters unless you ask the right questions.
And there are still a lot of pain points.
The biggest ones I have run into are:
**1. Hallucinated or unreliable numbers**
This is probably the most obvious issue. If the financial data is wrong, the analysis is useless no matter how good the explanation sounds.
**2. Weak sourcing**
I do not just want an answer. I want to know where the number came from, what period it refers to, and whether it came from a filing, a data provider, a transcript, or somewhere else.
**3. Mixing definitions**
Free cash flow, owner earnings, adjusted EBITDA, net income, operating cash flow, ROIC, and margins can all be calculated in different ways. AI often sounds confident even when the definition is unclear.
**4. Losing context**
A good stock analysis process is cumulative. You build a thesis, test assumptions, update your view, and track what changed. Most AI tools still feel too conversation-by-conversation.
**5. Too much summary, not enough judgment**
A lot of AI output reads like a polished summary of facts. That is useful, but the real value comes from identifying what changed, what matters, and what could break the thesis.
**6. Hard to trust without checking everything**
If I have to verify every number, citation, and conclusion manually, then the time savings can disappear quickly.
That said, I still think this is one of the most interesting changes for individual investors in a long time.
The internet made information accessible.
Spreadsheets made analysis more personal and flexible.
AI may make research more interactive.
The investor still has to make the hard calls: whether the business is durable, whether management is trustworthy, whether the valuation is reasonable, and whether the market is missing something important.
But the workflow around getting to those answers seems like it is changing very quickly.
I am curious how people here are actually using AI tools, ChatGPT, Claude, Gemini, Perplexity, MCPs, custom agents, APIs, or spreadsheets in their stock analysis.
What has genuinely saved you time?
And what is the biggest pain point that still keeps you from trusting these tools more?