MCP-Atlas: The Opus 4.6 score has been updated to reflect revised grading methodology from Scale AI.

I don't understand the concern here

https://artificialanalysis.ai/evaluations/omniscience

On the other hand, it is their own verified benchmark, which is telling.

swe-bench seems really hard once you are above 80%

And had all sorts of negative outcomes for the kids: https://www.edweek.org/teaching-learning/long-term-study-of-...

Exponential would be at 3.6 hours

Not massively off -- manifold yesterday implied odds this low were ~35%. 30% before Claude Opus 4.1 came out which updated expected agentic coding abilities downward.

These benchmark gains aren't that high, so I doubt it is that obvious.

The prior is the market. It isn't sane to use your own prior experience. (Works both ways -- if your last startup did great, shouldn't assume next will).

> 4% of YC companies become unicorns. How many startups do you need to work for before you become part of the 4%? That number is not a feasible number of jobs for one lifetime.

The bar (and what the model is calculating) is Series A from top VC, not YC Seed funding. That significantly increases odds. Specifically, ~45% YC companies get Series A, so it's more like 10% chance of a YC Series A funded company becoming a unicorn (https://www.lennysnewsletter.com/p/pulling-back-the-curtain-...).

Model is change jobs every 18 months if not booming. A 1 in 10 chance is quite reasonable over a career.

I agree there is an issue with the event being too rare, but you can't just look only at modal returns. 2/3 chance of $0 (the modal return) and 1/3 chance of $10 million profit is still pretty good odds to work with.

> this argument reads to me like "the returns on a Powerball win are so much higher than your projected lifetime earnings that playing the lottery is a smart financial move".

That's stronger claim than it is making, but yes in a sense it is saying the lottery can be a good move because the expectation is large - that's what VCs do after all.

Note that all the model aims to do is value the equity package. If a public company is offering more than what this model values the startup equity package as (and this often is the case!), it isn't worth it financially to work at that startup.

To sum up the arguments:

* Employment packages allow things a shareholder cannot do (functionally recall their investment), so the high volatility leads to higher package returns.

* FAANG equity grants (RSUs) are taxed at much higher rates

* Expected return is in fact higher on startup equity than FAANG equity (and you generally have no way to invest in the good startups directly aside from working for them).

Comments URL: https://news.ycombinator.com/item?id=44781179

Points: 2

# Comments: 9

This is not obvious at all to me. Instagram (bought for $1B) is probably worth ~700 B of Meta's market cap.