Adam Foroughi

speaker

1,682 appearances 2 recordings 2 series first heard Apr 2026 last heard 27 Apr

Adam Foroughi’s voice in public audio — every appearance, attributed to the second.

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recordings per month · last 12 months

Recordings per month over the last 12 months — 2 in all, peaking in Apr 2026 with 2.

Appearances

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The Twenty Minute VC (20VC): Venture Capital | Startup Funding | The Pitch · 20VC: Applovin: $160BN Market Cap, $5.48BN Revenue, $10M EBITDA Per Head | Why the Best Do Not Need Mentorship | Why Founders Should Not Angel Invest | Why Kindness in Business Will Slow You Down with Adam Foroughi · 27 Apr 2026 podcast

The challenges there are when everyone is telling you,

that the stock is going down every single day, investors are not buying your shares, it's very easy to go, am I doing something wrong?

Is the business fucked?

Is something here that I don't understand that everyone else in the world is smarter than me on and understands?

And so it can make you lose confidence and second guess yourself.

In the face of that, I think what's important is if you believe in your business, maintaining conviction.

And so we did a couple of things and really that allowed us to turn the business around.

As an advertising business, there's an advertising model that drives a lot of the success that we have on platform.

Everything we do is on a performance basis.

So advertisers plug in, they aim to get a certain amount of revenue that's more than the ad dollars that they spend on the platform.

Now what delivers that equation for them is how potent our advertising model is.

And these models are recommendation system models and

That's one of the earlier forms of machine learning that existed, and it's really gotten supercharged with what we see today in AI and the research advancements in LLMs.

Well, in 22, at the very bottom, we said, we're on an older version of machine learning.

We're going to completely throw out our technology, rebuild it, and go to what is really cutting edge and current in the field of recommendation systems.

To do that was a big internal change.

One, we had to slow down basically all research and development on the current system because we said we're going to throw it out.

It's now outdated.

It's not going to carry us forward where we got to go.

We had to turn over some people.

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