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Patrick O'Shaughnessy Podcast
Etched - Building AI Hardware to Make Inference Faster and Cheaper - [Invest Like the Best, EP.480]
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20 September
19 September
18 September
17 September
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korrents.com
As AI inference costs fall, rising customer expectations will keep service margins from converging toward 100%.Their words
Margins will not climb to 100%. As inference gets cheaper, buyers will ask more of their agents, shifting the equilibrium over time in response to competition.
16 September
15 September
From one piece @paulg on X 2 beliefs · x.com
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Their words
Someone needs to define the unit, perhaps using a chain of increasingly hard problems, each pair of which can be solved by a single model.
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korrents.com
Tokens are not the true unit of AI inference, because better models yield more problem-solving per token.Their words
Although you pay for AI by the token, that's not the unit of inference, because you get more problem solving per token as models improve.
14 September
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korrents.com
Migrations are part and parcel of software engineering and a skill to invest in, not special one-offs to avoid.Their words
migrations (e.g. replacing one service with a new one, or switching database engines, or whatever) are part and parcel of software engineering and are a skill that you should invest in and get good at, not avoid or treat as special one-offs.
13 September
12 September
11 September
10 September
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Their words
I don’t think it’s responsible for high long-term rates, any more than Bill Clinton was responsible for high rates in the late 1990s. This looks like the natural market response to the rush to invest in AI.
9 September
8 September
5 September
3 September
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Their words
it's someone who gets what they want in any situation. And so, if you invest in them and you have stock in their company and they have stock in this company, your interests are aligned. If they get what they want, you get what you want, so that's why investors want people who are formidable.
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open sourceAnthropicOpenAIChina
Their words
in this whole USA vs China thing OpenAI and Anthropic aren't relevant because they're positioned differently them building better models doesn't hurt china at all the competitor has to be - american - open source - enough compute to do inference at scale that can shift things
2 September
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korrents.com
Investing in strong fundamentals and invisible supports makes good outcomes follow naturallyTheir words
Invest in building strong fundamentals, invisible supports, and outcomes flow like water.
1 September
29 August
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Their words
I read every DM people send me, even if I don’t reply to all. I’m constantly learning about new ideas, ways to improve our product, where we’re falling short, who could join our company next, where to invest… very grateful for the @x platform.
25 August
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Their words
This is a computer system made for pros, and I mean that in the sense of professional computer lovers. People who are willing to invest time and energy into learning how their system works, and reap the benefits that come from that investment.
24 August
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korrents.com
Planning a day or a week in advance works because it pays the brain's decision cost once rather than dozens of times.Their words
Every decision has a cost in terms as we've seen the brain having to put in invest fuel to make that decision. So if you're having to do that every time you make a decision, that can seem very burdensome. Whereas if you did it once in a big way where you planned it out for the day or the week, maybe the costs in terms of the activation energy required for the brain is much lower for you if you do that.
23 August
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korrents.com
Demand for intelligence is highly elastic — every fall in inference cost is met with rapidly growing usage.Their words
the demand for intelligence is highly elastic: as inference costs fall, usage grows rapidly.
21 August
20 August
18 August
30 July
From one piece Jeff Dean: The 1% Rule for Building in AI 3 beliefs, in the piece's order there
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Their words
And um if you build a specialized chip for low precision dense linear algebra and can't do anything else that turns out to be really useful for machine learning inference uh even though it can't run Chrome or Word or whatever.
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Their words
Um, and that's a very very useful general technique is you know inference time compute to perform search over plausible ways of solving the problem that can get much much higher performance or much more reliability in longunning agent flows.
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Their words
Yeah, I mean it's a little different, but I think uh you're going to see more and more uh uh high performance and um low energy uh inference hardware systems because I think everyone is now realizing that inference is the key to making you know these agent-based systems be available to more and more people and that latency is really important and that specialization of the hardware is a really key way you can make uh things that are more energy efficient and lower latency than more general purpose uh computational devices like say GPUs or TPUs
29 July
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Their words
the other piece of advice I would have is try to identify what is the what is the exponential in the world that has both the steepest curve and will go the longest. And you know many decades ago this curve was was Moore's law and that probably was you know that was at the time like clearly the right thing to invest on. I think right now it's AI progress
26 July
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Their words
You don't invest in companies, you invest in people.
20 July
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Their words
And what they found is there's kind of this um bell-shaped curve between energy availability and brain volume. And in general, brain volume, right, the more the better, right, we think. And so if you're chronically calorically restricted, your brain is smaller um because you just don't have the resources to invest in, you know, the structure um and to maintain it. At the other end, you see the same thing. So if you're chronically in a chronic state of chloric excess, you also tend to have a smaller brain on average.
18 July
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Their words
a smaller model at a higher reasoning effort can sometimes reach a similar score as a larger model at a lower reasoning effort
Controlling Reasoning Effort in LLMsmagazine.sebastianraschka.com
15 July
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korrents.com
Under rising uncertainty, people should shorten their planning horizon rather than invest in a distant future.Their words
When uncertainty rises, your horizon should become shorter-term.
14 July
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Readaffiliate linkrcmnd.app
How NOT to InvestTheir words
Ritholtz does exactly that, running through such surefire ways to wreck your wealth as ignoring taxes, paying excessive fees, misinterpreting data, overreacting to short-term trends, and trading too much.
7 July
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Their words
it's more important to invest in trains that can get more riders than just the railfans; taking a line from 0% to 5% modal split is less important than taking it from 20% to 40%
Prioritizing Rail Expansion in New Englandpedestrianobservations.com
18 June
From one piece Re-engineering the Semiconductor Supply Chain with Intel CEO Lip Bu Tan 2 beliefs, in the piece's order there
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Their words
And then frankly speaking, I look back nine of the 10 company I invest halfway they change their business plan because market have changed.
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Their words
So I think power, thermal, those become the bottleneck. So I think I always look at from what is the problem we try to solve? Is it real? Is customer crying for it?
15 June
9 June
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Their words
the investor community writ large has slowly become aware of and believes it strongly in increasing returns and power laws. And so over time, if they all believe that, they're going to be more willing to invest on the come and take risk.
7 June
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korrents.com
Incumbents are unseated by a fundamentally different product, never by better features or better marketing.Their words
I invest in the deep technologies that are going to unseat the incumbents because it's going to change the market or the product in such a dramatic way that customers will choose this.
4 June
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Their words
So, what you need is that basically demand to be bounded, like a hard bound, not even like a soft sort of like diminishing sensitivity. You need for them to eventually say, "I've had enough. I don't want to spend any more money." And for that money to not enter as investment.
27 May
From one piece Building OpenCode with Dax Raad 3 beliefs, in the piece's order there
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Their words
cuz because we rent GPUs at scale to run the models and we still use middleman by the way. So we're not like going all the way down to the down to the floor. Even for us there are some models the sticker price and the cost to us there's like an 80% margin in there.
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Their words
There's always negative sentiment that exists for any business that's getting hyped. They have no incentive to correct it. Um so again it's complicated because I know the training costs are a big part of it. Uh the R&D department is is hugely expensive but long-term inference makes sense as a business and I think it it always will.
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Their words
The demand for inference is growing. So, like I don't think it's linearly growing. I think it might even be exponentially growing. But we haven't made our production of GPUs grow exponentially. That's like kind of a linear process. So as those lines intersect, there's going to be uh tightening.
25 May
22 May
13 May
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Their words
because if you were to force AI to write a type annotation on everything, then it would probably get it wrong more often because now it has to keep track of all these types and and it and it has to just repeat itself over and over and over, right? And so, types are important where there's no context.
23 March
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Their words
that was always illogical to me because inference is thinking, and I think thinking is hard. Thinking is way harder than reading.
14 March
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Their words
players who are also commentators give better commentary than people who are just commentators. Stock analysts who never invest have zero skin in the game.
13 March
From one piece Dylan Patel — The single biggest bottleneck to scaling AI compute 2 beliefs, in the piece's order there
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Their words
They could release claw slow mode and have an increase in tokens per dollar by a significant amount. Um they could probably like reduce the price of Opus 46 by you know 4x 5x and reduce the speed by another by maybe just like 2x like the curve on inference throughput versus speed is there already just on hm um and yet they don't um because no one actually wants to use a slow model
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Their words
So when you look at inference at let's say 100 tokens a second for deepseek and kimk 2.5 hopper versus blackwell the performance difference is on the order of 20x
22 February
13 February
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korrents.com
Longer context is an engineering and inference problem, not a research problem — nothing prevents it from working.Their words
There's There's nothing preventing longer context from working. You just have to train at longer context and then learn to to serve them at inference. And both of those are engineering problems that we are working on and that I would assume others are working on as well.
10 February
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Their words
The economics of orbital “datacenters” or essentially glorified Starlink satellites with a bunch of GPUs attached are likely to be even better than Starlink.
Space AI: I guess we’re doing Moon factories nowcaseyhandmer.wordpress.com
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Their words
The "homework problem" described above might be much worse in practice for end users who aren't motivated to invest in their email triage systems, even if it will save them time in the medium term.
24 January
30 December 2025
28 December 2025
10 December 2025
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Their words
With so many professional money managers afraid to act, with most of the public in the grip of fear and anger, you should put your cash and your courage to work.
4 December 2025
15 August 2025
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Their words
actually it turns out that you can significantly decrease power consumption with a very small reduction in overall compute. So if you if you've got like three really bad days in a row or something, you can actually just like you can dial back your power usage quite a lot without compromising your inference or or um or training.
13 August 2025
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Their words
I’ve rarely seen a more capitalist society than China, from the pure economic side. I’ve rarely seen companies that are as competitive as Chinese companies. People as ambitious and obsessed with making money as Chinese people. Kind of ruthless actually. And look, consumers shop, firms invest. If you invest well financially, you’ll get great returns. What is not capitalistic about the Chinese economy? At the same time, the social fabric is highly socialist.
4 August 2025
14 April 2025
From one piece Tariffs, saving, and investment 2 beliefs, in the piece's order there
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korrents.com
The binding constraint on building anything in America is permitting and litigation, not money.Their words
Endless lawsuits, cost-exploding contracting requirements, decades to get permits, and more bedevil any attempt to invest here.
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korrents.com
The American trade deficit is foreigners choosing to invest in America, not America being cheated.Their words
this story tells us how an increase in foreign demand to save in the US rather than at home will push up the dollar, and cause the trade deficit, which is in effect how foreigners send us factories which they would rather build here than in their own countries.
7 March 2025
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Their words
In a vibe coding future, companies will hopefully invest more in understanding user needs, refining the interface, and polishing details that delight users, because those are harder for AI to get right without guidance.
3 February 2025
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Their words
OpenAI has a fantastic margin. When they're doing inference, their gross margins are north of 75%. So that's a four to five X factor right there of the cost difference, is that OpenAI is just making crazy amounts of money because they're the only one with the capability.
30 January 2025
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Likedrcmnd.app
MacBook Pro M1 MaxTheir words
This allows me to run models locally on my MacBook Pro M1 Max. With the 64GB of RAM it has, it’s a pretty potent machine for basic inference despite it being three years old.
31 December 2024
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Recommendsrcmnd.app
Is AI progress slowing down?Their words
To understand more about inference scaling I recommend Is AI progress slowing down?
6 August 2024
19 June 2024
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Their words
I think if we can achieve that amount of inference compute, where it leads to a dramatically better answer as you apply more inference compute, I think that will be the beginning of real reasoning breakthroughs.
25 April 2024
20 March 2024
20 February 2024
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korrents.com
About a dozen holdings captures nearly all the benefit of diversification; going to fifty adds almost nothing.Their words
I even recommend for individual investors to invest in a dozen companies, you don't get that much more benefit of diversification going from a dozen to 25 or even 50.
22 March 2023
10 January 2023
31 December 2022
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Their words
As the forest grows darker, noisier, and less human, I expect to invest more time in in-person relationships and communities. And while I love meatspace, this still feels like a loss.
1 June 2020
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Recommendsaffiliate linkrcmnd.app
Information Theory, Inference and Learning AlgorithmsTheir words
Information theory has a way of shaping the way you view many things in math, computer science, physics, and beyond. It's one of those fields that takes ideas that you wouldn't think of as being quantifiable or rigorous and makes them so.
16 September 2019
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Recommendsrcmnd.app
Do Dice Play God?: The Mathematics of UncertaintyTheir words
The thing I'd say about Do Dice Play God? is that it is very rewarding, but you do have to invest in it.
20 August 2019
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Mixed onrcmnd.app
REAPERTheir words
It's basically the Vim of audio editing software, so I wouldn't recommend it if you don't have the time and energy to heavily invest in customizing it for your workflow, but I can move like lightning in this thing.
28 October 2018
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Lovedrcmnd.app
Invest Like the BestTheir words
Invest Like the Best with Patrick O’ Shaughnessy. This is the best business podcast out there.
9 March 2018
From one piece Frankle & Carbin, "The Lottery Ticket Hypothesis: Finding Sparse, Trainable Neural Networks" (arXiv) 2 beliefs, in the piece's order there
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Their words
However, contemporary experience is that the sparse architectures produced by pruning are difficult to train from the start, which would similarly improve training performance.
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Their words
Neural network pruning techniques can reduce the parameter counts of trained networks by over 90%, decreasing storage requirements and improving computational performance of inference without compromising accuracy.
From one piece Frankle & Carbin, "The Lottery Ticket Hypothesis: Finding Sparse, Trainable Neural Networks" (arXiv) 2 beliefs, in the piece's order there
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Their words
However, contemporary experience is that the sparse architectures produced by pruning are difficult to train from the start, which would similarly improve training performance.
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Their words
Neural network pruning techniques can reduce the parameter counts of trained networks by over 90%, decreasing storage requirements and improving computational performance of inference without compromising accuracy.
15 November 2017
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