In 2025, blogs will be the last bastion of the Good Internet
Erik Hoel writes
a work of maximalism succeeds when it triggers this property in the experiencer. You know that the whole is there, but you can’t see it all at once. You can only take it in sequentially. It is the awareness of an emergent form you, as a limited being with only a periscope for perception, cannot actually understand in full.
And this same reaction of “I don’t even know where to look, or where to begin” in the viewer (here, reader) is what I think the very best of blogging should strive for.
This was the exact reaction I had over 15 years ago when I came across my first real blog, ribbonfarm.com. Around this time blogging came into it’s heyday and many other blogs were found and devoured. The blogs all had this quality defined by the quantity, it was like making a friend without ever meeting the person.
As AI slop (that’s the official term now) takes over and corrodes what was left finding good thing even thorough social media will be difficult. I agree with Erik, blogs being high ownership media will be where we will go to have fun.
Simon Willison’s approach to running a link blog
Speaking of AI slop, Simon Willison, who coined the term, recently did this beautiful write up about how to post what’s perhaps the simplest kind of blog post, the link post. It is the same thing which you probably do with your friends in your private chats or your hundred channel discords post interesting things write a bit about them.
On the blog side, we can stand on the shoulders of these blogging giants and in the spirit of the internet copy and evolve their best practices. Simon has a longer list in the post that you should read, these bits about how to do it nicely stood out to me:
- I always include the names of the people who created the content I am linking to, if I can figure that out. Credit is really important, and it’s also useful for myself because I can later search for someone’s name and find other interesting things they have created that I linked to in the past. […]
- I try to add something extra. My goal with any link blog post is that if you read both my post and the source material you’ll have an enhanced experience over if you read just the source material itself.
- Ideally I’d like you to take something useful away even if you don’t follow the link itself. This can be a slightly tricky balance: I don’t want to steal attention from the authors and plagiarize their message. Generally I’ll try to find some key idea that’s worth emphasizing. […]
- I’m liberal with quotations. Finding and quoting a paragraph that captures the key theme of a post is a very quick and effective way to summarize it and help people decide if it’s worth reading the whole thing. […]
- If the original author reads my post, I want them to feel good about it. I know from my own experience that often when you publish something online the silence can be deafening. Knowing that someone else read, appreciated, understood and then shared your work can be very pleasant.
- A slightly self-involved concern I have is that I like to prove that I’ve read it. This is more for me than for anyone else: I don’t like to recommend something if I’ve not read that thing myself, and sticking in a detail that shows I read past the first paragraph helps keep me honest about that.
Yes, very meta of me to post this.
AI, Investment Decisions, and Inequality
[full-text] As a practitioner in the field, I’ve always maintained that AI is a tool on the lines of spreadsheets. It gives superpowers no matter who you are but if you are an expert in your domain the power is multiplicative. Seems actual research, by Eric So and others, is coming to similar conclusions:
We hypothesize that the widening performance gap across investor groups stems from an inherent trade-off–making AI summaries accessible to less sophisticated investors sacrifices technical precision. For example, whereas a simple summary might note “a reduction in profits,” the advanced version specifies “operating margins declined due to higher input costs caused by supply chain disruptions”—creating a gap in the precision of the signals that participants receive. Consistent with this hypothesis, we find that the performance gap between more versus less sophisticated participants widens when more technical information is omitted from the simplistic summaries, such as discussion of R&D spending, share repurchases, and gaps between EBITDA vs. net profits. Thus, the efforts to make financial information more accessible via AI come at the cost of reduced precision, potentially limiting AI’s ability to fully democratize financial decision-making.
While specialization may be for insects, context is for kings (Star Trek) and jargon compresses the progress to circumscribe the exact matter at hand. Those who can compress better not only can judge better but can narrow down the scope of the LLM to get better analysis done. It seems this will also lead to a widening gap rather than a democratization:
Our analysis yields two central results. First, there is a significant improvement in both information processing ability and the quality of investment decisions following AI adoption. However, this effect holds (for both sophisticated and unsophisticated groups of users) as long as AI output is aligned with user sophistication. Second, AI widens the knowledge-driven disparities between sophisticated and unsophisticated participants.
Why probability probably doesn’t exist (but it is useful to act like it does)
[Paywall version on nature], David Spiegelhalter takes his lifetime of experience to tell us of the church choir why this is a useful fiction.
numerical probability, I will argue — whether in a scientific paper, as part of weather forecasts, predicting the outcome of a sports competition or quantifying a health risk — is not an objective property of the world, but a construction based on personal or collective judgements and (often doubtful) assumptions. Furthermore, in most circumstances, it is not even estimating some underlying ‘true’ quantity. Probability, indeed, can only rarely be said to ‘exist’ at all.
[…]
In the natural world, we can throw in the workings of large collections of gas molecules which, even if following Newtonian physics, obey the laws of statistical mechanics; and genetics, in which the huge complexity of chromosomal selection and recombination gives rise to stable rates of inheritance. It might be reasonable in these limited circumstances to assume a pseudo-objective probability — ‘the’ probability, rather than ‘a’ (subjective) probability.In every other situation in which probabilities are used, however — from broad swathes of science to sports, economics, weather, climate, risk analysis, catastrophe models and so on — it does not make sense to think of our judgements as being estimates of ‘true’ probabilities. These are just situations in which we can attempt to express our personal or collective uncertainty in terms of probabilities, on the basis of our knowledge and judgement.
[…]
we perhaps don’t have to decide whether objective ‘chances’ really exist in the everyday non-quantum world. We can instead take a pragmatic approach. Rather ironically, de Finetti himself provided the most persuasive argument for this approach in his 1931 work on ‘exchangeability’, which resulted in a famous theorem that bears his name. A sequence of events is judged to be exchangeable if our subjective probability for each sequence is unaffected by the order of our observations. De Finetti brilliantly proved that this assumption is mathematically equivalent to acting as if the events are independent, each with some true underlying ‘chance’ of occurring, and that our uncertainty about that unknown chance is expressed by a subjective, epistemic probability distribution. This is remarkable: it shows that, starting from a specific, but purely subjective, expression of convictions, we should act as if events were driven by objective chances.
Aneesh Sathe 
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