Thomas Laffont
Does his own confidence mean anything?
the same scored calls, split by how hard he was pushing at the mention that set his final stance. If credit rises with strength, his table-pounding carries information and you should weight it. If it's flat or inverted, his confidence is noise — treat a ×3 like a ×1. Cells under 5 calls are provisional and get no weight in the replay.
| he was | calls | credit | right/half/wrong | avg α | read |
|---|---|---|---|---|---|
| ×3 table-pounding | 6 | 0.5 | 3/0/3 | -16% | coin flip |
| ×2 stated with reasoning | 11 | 0.59 | 6/1/4 | -16% | better than coin flip |
| ×1 offhand aside | 1 | 0.0 | 0/0/1 | -51% | provisional (n<5) |
Which kind of claim to trust
by the tier of the mention that set his final stance — a dated prediction is a different animal from a passing lean, and they don't have to score alike
| tier | calls | credit | right/half/wrong | avg α |
|---|---|---|---|---|
| explicit_prediction | 11 | 0.73 | 8/0/3 | -2% |
| positioning | 1 | 0.0 prov. | 0/0/1 | -23% |
| sentiment | 6 | 0.25 | 1/1/4 | -45% |
What he's actually good at
his row of the scoreboard — these are the exact cells rescore.py uses to weight his mentions. w = clamp(2 × credit, 0.3, 1.5), applied only at n ≥ 5.
Live book — 1 position
his latest stance on every ACTIVE idea. Expression = what he is effectively long or short: supporting a bearish idea is a SHORT, opposing one is a LONG.
| idea | his stance | expression | conviction | flag | eval in |
|---|---|---|---|---|---|
| 🤖 Own AWS, Azure and GCP and nothing else — the clouds capture the AI application dollars | support ×3 | ▲ LONG MSFT | 20.6 | — | 650d |
Where his book points
net push per primary instrument across his live stances — conviction × his agreement × the idea's direction. This is his implied book, not a position he disclosed.
Best and worst calls
How he argues
Latest from him
What if actually AI can increase productivity and regrow GDP faster than expectations, right? And perhaps that's one of the reasons why interest rates might not be quite as high as you might expect, given some of the trends that you guys have talked about.
Onavo was a small data service provider, but what it did is it had a panel of phones, and we as investors could see which apps people were using, and the data was incredibly valuable because it was the only service that gave you true engagement data. ... Eventually, it sold to Facebook, and Facebook used it internally and didn't allow anybody else to use it.
if we look at the winners, in models of the past 12 months, anthropic the same, right? They've been very deliberate and have explained how TPUs, right? They've been a big user of them, how it's helped define their training models. ... If we look at the models that have really performed, it's ones that have that quote secret, as you mentioned.
I'm wondering if this is the year where we've seen the greatest divergence amongst the Mac 7, right? ... now the market is saying, wait, hold on, we might start to see diverging performance.
Look, I think to me, number one, I still think NVIDIA, right? I don't see the GPU kind of getting displaced. I see additional architectures kind of coming on board, right? And growing the market. But at the end of the day, all roads still lead to the GPU for all of these models.
I think Sacks' work on the diffusion rule, just generally, I don't think has gone enough attention in the rescinding of the diffusion rule, which essentially handicapped our ability to even arm our allies with our semiconductor technology.
So I think the good news for them is, look, they still have a monopoly on users, they have three trillion of market cap to kind of play with. So I think it's way too early to count them out.
I do think that we're starting to see a healthier market where we know a lot of dollars have gone in, but now we're starting to see some dollars coming out. So I think that's both in M&A, by the way, and it's also in IPOs.
I think, and this is order of magnitude correct, that Anthropic in Q1 added 70% of the net new AR in the SaaS industry, right? Defined by public SaaS companies, right? So let's just think that the company in AI that is most powering, the disruption of SaaS, added three quarters of the net new of the entire industry
Then wait till we see the flurry of S1s that have already been filed. Figma is a generational potential company that's going to be coming. I think we're going to see fantastic assets coming out. I think the market is saying we're open for business.
I think knowledge workers are incredibly flexible. They can take their tools from, you know, one particular skill set to another. So I think this is going to unleash incredible opportunities for the economy. I think it is going to make us more productive and wealthier. So I'm definitely on the more optimistic side of the scenario.
I think all four of us would agree that if we could synthetically own AWS, Azure and GCP, if I could somehow automagically create an index of all three of those businesses, right, over the next five years, you wouldn't need to own anything else.
But I think you put it all together, you know, is 100 billion a reasonable scenario? ... And then could it be a trillion dollar asset, the way Trump has mentioned? I mean, in a world where Facebook's one and a half, right? And the penetration of TikTok in the US is 50% of what Metta is. I don't think it's unrealistic.
my way to put back to Chamath on that would be, I just think it's a dangerous game for the government to start picking winners. You know, I wouldn't, for example, if it owns 50% of a TikTok, does it disadvantage a meta, as an example, right?
I think to me, the real question, guys, is, do we ultimately believe that you can get an ROI on that 500 billion? Because if you can, there will be people that will want to fund it, right? You can do a data center by data center, you can do it actually at the GPU level, right? So the financing is there. To me, the 500 billion doesn't scare me from an absolute number.