dbs

Without knowing all the details, there are a few reasons that explain the outperformance:

- long bull market lower the probability of achieving results above market, especially if the performance is concentrated in a select group of stocks/industries

- not knowing the factors in detail, but you might not have a period long to assess the performance. Factor performance tends to be attached to "performance regimes"

- the factors you considered in the past were underpriced, and in your evaluation window they are no longer in such state, so expected returns are lower

- market has catched up on the factors that you are using (your "free lunch" has been eaten). I guess there's a reason why nowadays quants freely join podcasts while a few years ago we had to be very careful in interviews ("do they want to hire me or do they just want to know what I'm doing/not doing")

Thats not how it works.

LLMs get you to average.

LLMs are not good at decision making under uncertainty.

Everything old is new again. These new AI “funds” remind me the applied research that was done in the 2000s with strategy search through genetic programming, startups like Logical Information Machines, even Peter Thiel had a macro hedge fund.

Failed to grasp what collapse data this article applies to. There is for sure a certain amount on individuals that will be able to sense structural changes if they happen to be in the right place at the right time and they have access to the right data and a set of mental models to do so. However there is a random factor at play for all the things that need to be right. There are no seers, only lucky seers.

LLMs are awesome bullshit generators. Cant see why some CEOs would not be impressed by them.

We had vibe coding. Now we have vibe engineering. Next stop, vibe faking.

No need for evidence of net benefits to get mass adoption. We have mass adoption of digital touchpads in cars despite evidence they are not safe. We have widespread adoption of open spaces despite evidence of them not increasing productivity..

This is very very similar to my recent personal experience with a XC60. Everyday there was something that was not working.

Unpredictable, unreliable and especially unsafe (built like a tank but prone to accidents due to all the random electronic failures and malfunctions)

You must be 18 in the UK for getting a tattoo, buying alcohol, watching porn, purchasing cigarettes, using a sunbed or being tried as an adult. Why would they lower the voting age to 16?

There was some talk a few years ago in the country where I live to lower the voting age. That talk was mostly driven by the parties that would benefit the most from a younger electorate. It had nothing to do with “democracy”.

Hal 9000 mimicry

This makes for an alternative take - in Portuguese

https://sicnoticias.pt/pais/2023-07-29-A-explicacao-completa...

What a tragedy. Another expat living in Portugal while complaining all the time how much better things are in his/her own country.

Investing niches.

I worked many years as a quant. Strategies based in unpublished findings tend to be very profitable. But once that info becomes widely available it is just a matter of time until getting diminished returns. Thus there is a high incentive to not publicise.

Moats per se are not useful. The direction is - are they strengthening or fading.

Also moats should be seen as a consequence of multiple dynamics - industry, management, etc - not as a cause.

A business that is growing and by growing is reinforcing their moat is a powerful multiplier.

We went from CLIs to GUIs to touchscreens and we will see more and more of MCBs, multimodal chatbot, where the interaction is done by text, voice, maybe even other on the future. AI is just the “software” that enables those interfaces, not the interface per se.

Neat. Can you add a filter for international buyers? Non-us

What does McKinsey has to do with it?

In what extent you see the output being incredible?

At appearance the output _seems_ incredible but once one starts pushing for more or requiring consistency for production, it requires a tremendous effort to put in place or it is simply not possible.

I have also a few decades in the field, especially regarding automation of knowledge processes, so genuinely interesting in getting other viewpoints.

No matter how you minimize drawdown from a backward perspective, there is a reasonable chance there will be an event in the future where you will have a 50pct drawdown.

Many of these strategies stopped working in 2008 because the markets became too crowded with players exploring them. Especially the ones that have low drawdowns attract a lot of competition. The writing was already in the wall with the quant bloodbath of 2007.

How certain can we be that these rankings reflect an accurate picture? Rankers seem to be paid customers of Startup Genome, the organization that is providing the rankings.

* GOOG might have search compromised by AI, so they need to invest. * Both MSFT and GOOG see AI as a way of growing cloud and building stickiness * MSFT is investing on AI on Windows and O365 to milk further their existing enterprise customers on O365

Where is AAPL competitive position being compromised? * Android vs iPhone: they just need to continue executing * Windows vs iOS: MSFT is actually telling them how to position

Given their technical moat on integrating hardware & software there is an hidden opportunity for them in being the best supporting local/on-device LLMs

Out of the major players they have less disruption risk, grow depends less on AI meeting expectations and they have hidden optionality. How is this falling behind?

Show me the use cases you have supported in production. Then I might read all the 30 pages praising the dozens (soon to be hundreds?) of “best practices” to build LLMs.

It’s largely the metals and chemicals that get into contact with our food. But what do I know.

Interesting how an entire discussion can be hostage of an association to a single analogy. Instead of astrology, alchemy is a better analogy for what is commonly called technical analysis.

Source?

I was quite surprised to see Sequoia getting involved in crypto fiascos.

My data sample is very small but I have a pretty good track record of shifting career focus in the last 20 years. In particular, 2000 and 2008 were two HUGE shifts for me as the writing was on the wall before crisis hit. Common theme to drive change: too much competition. I jumped out from areas where there was still tremendous growth to be seen but no serious money to be done.

I’m calling a third.

Some of the few buildings that resisted the Lisbon’s 1755 earthquake had a wooden cage structure.

https://en.wikipedia.org/wiki/Gaiola_(construction)

Reconstruction immediately after the earthquake saw a generalised adoption of this type of architecture.

Demon haunted world by Carl Sagan

Why: skeptical/scientific thinking

Keep trying, you can do it. Need a constant pool of patsies we can arb against.

Any method that requires you to trade frequently is not good to get you and keep you rich.

Also you don't get rich by paying for liquidity (which is not to say you will always get rich providing liquidity)