r/VisualStockResearch

I’ve done a full 180 on Meta Muse. Been using it, and it’s pretty nuts.

It reminded me about bills I needed to pay and makes it clear what needs my attention and what it can handle. I don’t have to keep asking how it can help.

But the bigger point is Meta keeps finding ways to get people using its products, then turning that attention into money.

Automating online shopping feels like another opportunity. If Meta takes a small cut of purchases, it could make money by handling things people already need to do.

People question Meta’s spending, but figuring out adoption and monetization is something I’m increasingly reluctant to bet against.

Every AI release comes with another chart showing how it beats the competition. A little better here, slightly worse there. How much of that actually changes the user experience?

That said, I think Google is moving in the right direction. Based on its published results, the new model looks particularly strong at research and complex business tasks. The launch seems aimed heavily at enterprise customers willing to pay for that work.

Coding looks more mixed, and getting developers to switch from Claude or Codex could be a challenge. A better benchmark score alone probably won’t do it.

For a while, I thought Anthropic and OpenAI could become the Visa and Mastercard of AI. Now I’m less convinced this ends up being a two-company race.

My bet is that Google comes out ahead commercially. It may never have the best model in every category, but I think it’s positioned to turn AI into revenue and improve its existing business better than almost anyone. Meta is my other favorite on that front.

Google already has the chips, infrastructure, research teams, and products to put AI in front of enormous numbers of users. It also has an established business to fund that investment.

That doesn’t guarantee it wins. Distribution only helps if the products are useful, and being an established company doesn’t mean you’ll execute well.

I think Anthropic and OpenAI can build profitable businesses too. But Google doesn’t need everyone to switch to Gemini for its AI investment to pay off. Better Search, better advertising, and more valuable business software could be enough.

Are we putting too much weight on who wins the benchmarks and too little on who can turn the technology into profits?

I pulled data on ~200 large companies and used AI to test which 10-year business fundamentals had the strongest correlation with 10-year total stock returns.

I used Pearson correlation, which ranges from -1 to +1:

• +1 = strong positive relationship
• 0 = little to no linear relationship
• -1 = strong negative relationship

Results:

• Operating Profit Growth: 0.62
• Operating Earnings Growth: 0.61
• EBITDA Growth: 0.60
• Gross Profit Growth: 0.59
• Adjusted Net Income Growth: 0.58
• EPS Growth: 0.55
• Revenue Growth: 0.52
• Operating Cash Flow Growth: 0.52
• Unlevered Free Cash Flow Growth: 0.43
• Average ROIC: 0.16

The biggest takeaway for me was how consistently growth metrics had a stronger relationship with long-term returns than return-on-capital metrics

Operating profit growth had the strongest correlation, but gross profit, EPS, revenue and cash flow growth were all much stronger than ROIC.

That doesn’t mean ROIC doesn’t matter. High ROIC can be a sign of a very good business. But high returns on capital without growth don’t necessarily translate into great stock returns.

Correlation also doesn’t mean causation. Valuation, margins, dilution, dividends, acquisitions and starting point all matter

But this makes me think the long-term formula may be simpler than people make it:

Find companies that can sustainably grow earnings and cash flow, then figure out whether you’re paying a reasonable price for that growth