This is a fantastic post, immediately leaping into the top 25 of my favorite LessWrong posts all-time, at least. 

I have a concrete suggestion for this issue:

They end up spending quite a lot of effort and attention on loudly reiterating why it was impossible, and ~0 effort on figuring how they could have solved it anyway.

I propose switching gears at this point to make "Why is the problem impossible?" the actual focus of their efforts for the remainder of the time period. I predict this will consistently yield partial progress among at least a chunk of the participants.

I suggest thinking about the question of why it is impossible deliberately because I experienced great progress on an idea I had through exactly that mechanism, in a similar condition of not having the relevant physics knowledge. The short version of the story is that I had the idea, almost immediately hit upon a problem that seemed impossible, and then concluded it would never work. Walking down the stairs after right after having concluded it was impossible, I thought to myself "But why is it impossible?" and spent a lot of time following up on that thread. The whole investigation was iterations of that theme - an impossible blocker would appear, I would insist on understanding the impossibility, and every time it would eventually yield (in the sense of a new path forward at least; rarely was it just directly possible instead). As it stands I now have definite concrete angles of attack to make it work, which is the current phase.

My core intuition for why this worked:

• Impossibility requires grappling with fundamentals; there is no alternative.
• It naturally distinguishes between the problem the approach to the problem.
• I gesture in the direction of things like the speed of light, the 2nd law of thermodynamics, and the halting problem to make the claim that fundamental limits are good practice to think about.

I think this post is quite important because it is about Skin in the Game. Normally we love it, but here is the doubly-interesting case of wanting to reduce the financial version in order to allow the space for better thinking.

The content of the question is good by itself as a moment in time of thinking about the problem. The answers to the question are good both for what they contain, and also for what they do not contain, by which I mean what we want to see come up in questions of this kind to answer them better.

As a follow-up, I would like to see a more concrete exploration of options for how to deal with this kind of situation. Sort of like an inverse of the question "how does one bet on AI profitably?" In this case it might be the question "how does one neutralize implicit bets on AI?" By options I mean specific financial vehicles and legal maneuvers.

But if you introduce AI into the mix, you don’t only get to duplicate exactly the ‘AI shaped holes’ in the previous efforts.

I have decided I like the AI shaped holes phraseology, because it highlights the degree to which this is basically a failure in the perception of human managers. There aren't any AI shaped holes because the entire pitch with AI is we have to tell the AI what shape to take. Even if we constrain ourselves to LLMs, the AI docs literally and exactly describe how to tell it what role to fill.

Let’s say Company A can make AGIs that are drop-in replacements for highly-skilled humans at any existing remote job (including e.g. “company founder”), and no other company can. And Company C is a cloud provider. Then Company A will be able to outbid every other company for Company C’s cloud compute, since Company A is able to turn cloud compute directly into massive revenue. It can just buy more and more cloud compute from C and every other company, funding itself with rapid exponential growth, until the whole world is saturated.

I think this is outside the timeline under consideration. Transforming compute into massive revenue is still gated by the ability of non-AGI enabled customers to decide to spend with Company A; regardless of price the ability of Company C to make more compute available to sell depends quite a bit on the timelines of their contracts with other companies, etc. The ability to outbid the whole rest of the world for commercial compute already crosses the transformational threshold, I claim. This remains true regardless of whether it is a single dominant bidder or several.

I think the timeline we are looking at is from initial launch through the first round of compute-buy. This still leaves all normal customers of compute as bidders, so I would expect the amount of additional compute going to AGI to be a small fraction of the total.

Though let the record reflect based on the other details in the estimate this could still be an enormous increase in the population.

I endorse this movie unironically. It is a classic film for tracking what information you have and don't have, how many possibilities there are, etc.

Also the filmmaker maintains to this day that they left the truth of the matter in the final scene undefined on purpose, so we are spared the logic being hideously hacked-off to suit the narrative and have to live with the uncertainty instead.

Huzzah for assembling conversations! With this proof of concept, I wonder how easy it will be to deploy inside of LessWrong here.

Strong upvote for conversation summarizing!

I think the best arguments are those about the costs to the AI of being nice. I don't believe the AI will be nice at all because neglect is so much more profitable computation-wise.

This is because even processing the question of how much sunlight to spare humanity probably costs more in expectation than the potential benefit of that sunlight to the AI.

First and least significant, consider that niceness is an ongoing cost. It is not a one-time negotiation to spare humanity 1% of the sun; more compute will have to be spent on us in the future. That compute will have to be modeled and accounted for, but we can expect that the better humanity does, the more compute will have to be dedicated to us.

Second and more significant, what about time discounting? The proportion of compute that would have to be dedicated to niceness is highest right in the beginning, when humanity is largest relative to the AI. Since the cost is highest right at first, this suggests the AI is unlikely to engage in it at all.

Third and most significant, why should we believe this to be true? Because it seems to me to already be true of basically everything:

• Polynomial equations get harder as you add terms.
• The curse of dimensionality.
• A pendulum is easy, but a double pendulum is hard.
• More levels in multi-level modeling is harder.
• Game theory outcomes are harder to solve with multiple players.
• Making decisions with a large group is famously hard, and the first rule of fast decisions is to keep the group small.

Even within the boundaries of regular human-level paper computations it feels like the hit here is huge on aggregate. The presence of humans makes a bunch of places where: zeroes or infinities can't be used to simplify; matrices can no longer be diagonalized; fewer symmetries will be available, etc. In short, I expect niceness to result in a systematic-though-not-complete loss of compute-saving moves through all layers of abstraction.

This isn't reserved for planning or world-modeling style computation either; these constraints and optimizations are already bedrock assumptions that go into the hardware design, system software design, and neural net/whatever other design the AI will launch with; in other words these considerations are baked into the entire history of any prospective AI.

In sum, we die by Occam's Razor.

I'm not familiar with the details of Robin's beliefs in the past, but it sure seems lately he is entertaining the opposite idea. He's spending a lot of words on cultural drift recently, mostly characterizing it negatively. His most recent on the subject is Betrayed By Culture.

I happened to read a Quanta article about equivalence earlier, and one of the threads is the difficulty of a field applying a big new concept without the expository and distillation work of putting stuff into textbooks/lectures/etc.

That problem pattern-matches with the replication example, but well-motivated at the front end instead of badly-motivated at the back end. It still feels like exposition and distillation are key tasks that govern the memes-in-the-field passed among median researchers.

I strongly suspect the crux of the replication crisis example is that while there are piles of exposition and distillation for probability/statistics, they are from outside whatever field experiences the problem, and each field needs its own internal completion of these steps for them to stick.

I think this should be a third specialization for every scientific field. We have theorists, we have experimentalists, and to this we should add analysts. Their work would specialize in establishing the statistical properties of experimental designs and equipment in the field on the one side, and the statistical requirements to advance various theories on the other side.

To me memetic normally reads something like "has a high propensity to become a meme" or "is meme-like" I had no trouble interpreting the post from this basis.

I push back against trying to hew closely to usages from the field of genetics. Fundamentally I feel like that is not what talking about memes is for; it was an analogy from the start, not meant for the same level of rigor. Further, memes and how meme-like things are is much more widely talked about than genetics, so insofar as we privilege usage considerations I claim switching to one matching genetics would require more inferential work from readers overall because the population of readers conversant with genetics is smaller.

I also feel like the value of speaking in terms of memes in the post is that the replication crises is largely the fault of non-rigorous treatment; that is to say in many fields the statistical analysis parts really were/are more of a meme inside the field rather than a rigorous practice. People just read other people's published papers analysis sections, and write something shaped like that, replicability be damned.

I am an American who knows what Estonia is, and I found the joke hilarious.

Welcome!

The short and informal version is that epistemics covers all the stuff surrounding the direct claims. Things like credence levels, confidence intervals, probability estimates, etc are the clearest indicators. It also includes questions like where the information came from, how it is combined with other information, what other information we would like to have but don't, etc.

The most popular way you'll see this expressed on LessWrong is through Bayesian probability estimates and a description of the model (which is to say the writer's beliefs about what causes what).

The epistemic status statement you see at the top of a lot of posts is for setting the expectation. This lets the OP write complete thoughts without the expectation that they demonstrate full epistemic rigor, or even that they endorse the thought per se.

May I throw geometry's hat into the ring? If you consider things like complex numbers and quarternions, or even vectors, what we have are two-or-more dimensional numbers.

I propose that units are a generalization of dimension beyond spatial dimensions, and therefore geometry is their progenitor. 

It's a mathematical Maury Povich situation.

I feel like this is mostly an artifact of notation. The thing that is not allowed with addition or subtraction is simplifying to a single term; otherwise it is fine. Consider:

10x + 5y -5x -10y = 10x - 5x + 5y -10y = 5x - 5y

So, everyone reasons to themselves, what we have here is two numbers. But hark, with just a little more information, we can see more clearly we are looking at a two-dimensional number:

5x - 5y = 5

5x = 5y +5

5x - 5 = 5y

x - 1 = y

y = x - 1

Such as a line.

This is what is happening with vectors, and complex numbers, quarternions, etc.

The post anchors on the Christiano vs Eliezer models of takeoff, but am I right that the goal more generally is to disentangle the shape of progress from the timeline for progress? I strongly support disentangling dimensions of the problem. I have spoken against using p(doom) for similar reasons.

Because that method rejects everything about prices. People consume more of something the lower the price is, even more so when it is free: consider the meme about all the games that have never been played in people's Steam libraries because they buy them in bundles or on sale days. There are ~zero branches of history where they sell as many units at retail as are pirated.

A better-but-still-generous method would be to do a projection of the increased sales in the future under the lower price curve, and then claim all of that as damages, reasoning that all of this excess supply deprived the company of the opportunity to get those sales in the future.

This is not an answer, but I register a guess: the number relies on claims about piracy, which is to say illegal downloads of music, movies, videogames, and so on. The problem is that the conventional numbers for this are utter bunk, because the way it gets calculated by default is they take the number of downloads, multiply it by the retail price, and call that the cost.

This would be how they get the cost of cybercrime to significantly exceed the value of the software industry: they can do something like take the whole value of the cybersecurity industry, better-measured losses like from finance and crypto, and then add bunk numbers for piracy losses from the entertainment industry on top of it.

This feels like a bigger setback than the generic case of good laws failing to pass.

What I am thinking about currently is momentum, which is surprisingly important to the legislative process. There are two dimensions that make me sad here:

1. There might not be another try. It is extremely common for bills to disappear or get stuck in limbo after being rejected in this way. The kind of bills which keep appearing repeatedly until they succeed are those with a dedicated and influential special interest behind them, which I don't think AI safety qualifies for.
2. There won't be any mimicry. If SB 1047 had passed, it would have been a model for future regulation. Now it won't be, except where that regulation is being driven by the same people and orgs behind SB 1047.

I worry that the failure of the bill will go as far as to discredit the approaches it used, and will leave more space for more traditional laws which are burdensome, overly specific, and designed with winners and losers in mind.

We'll have to see how the people behind SB 1047 respond to the setback.

As for OpenAI dropping the mask: I devoted essentially zero effort to predicting this, though my complete lack of surprise implies it is consistent with the information I already had. Even so:

Shit.

I wonder how the consequences to reputation will play out after the fact.

• If there is a first launch, will the general who triggered it be downvoted to oblivion whenever they post afterward for a period of time?
• What if it looks like they were ultimately deceived by a sensor error, and believed themselves to be retaliating?
• If there is mutual destruction, will the general who triggered the retaliatory launch also be heavily downvoted?
• Less than, more than, or about the same as the first strike general?
• Would citizens who gained karma in a successful first strike condemn their 'victorious' generals at the same rate as everyone else?
• Should we call this pattern of behavior, however it turns out, the Judgment of History?

It does, if anything, seem almost backwards - getting nuked means losing everything, and successfully nuking means gaining much but not all.

However, that makes the game theory super easy to solve, and doesn't capture the opposing team dynamics very well for gaming purposes.

I think this is actually wrong, because of synthetic data letting us control what the AI learns and what they value, and in particular we can place honeypots that are practically indistinguishable from the real world

This sounds less like the notion of the first critical try is wrong, and more like you think synthetic data will allow us to confidently resolve the alignment problem beforehand. Does that scan?

Or is the position stronger, more like we don't need to solve the alignment problem in general, due to our ability to run simulations and use synthetic data?

Following on this:

Moreover, even when that dataset does exist, there often won’t be even the most basic built-in tools to analyze it. In an unusually modern manufacturing startup, the M.O. might be “export the dataset as .csv and use Excel to run basic statistics on it.”

I wonder how feasible it would be to build a manufacturing/parts/etc company whose value proposition is solving this problem from the jump. That is to say, redesigning parts with the sensors built in, with accompanying analysis tools, preferably as drop-in replacements where possible. In this way companies could undergo a "digital transformation" gradually, at pretty much their regular operations speed.

It occurs to me we can approach the problem from a completely data-centric perspective: if we want tool AIs to be able to control manufacturing more closely, we can steal a page from the data-center-as-computer people and think of the job of the machines themselves as being production sensors in a production center.

Wrangling the trade-offs would be tricky though. How much better will things be with all this additional data as compared to, say, a fixed throughput or efficiency increase? If we shift further and think in terms of how the additional data can add additional value, are we talking about redesigning machines such that they have more degrees of freedom, ie every measurement corresponds to an adjustable variable on the machine?

Also think he's wrong in the particulars, but I can't quite square it back to his perspective once the particulars are changed.

The bluntest thing that is wrong is that you can specify as precise a choice as you care to in the prompt, and the models usually respond. The only hitch is that you have to know those choices beforehand, whereas it would be reasonable to claim that someone like a photographer is being compelled to make choices they did not know about a priori. If that winds up being important then it would be more like the artist has to make and execute the choices they make, even if they are very simple like picking shading in photoshop or pushing the camera button.

I could see an alternative framework where even the most sophisticated prompt is more like a customer giving instructions to an artist than an artist using a tool to make art, but that seems to push further in the direction of AI makes art.

Lastly, if we take his claims at face value, someone should write an opinion piece with the claim that AI is in fact rescuing art, because once all the commercial gigs are absorbed by the machine then true artists will be spared the temptation of selling out. I mean I won't write it, but I would chuckle to read it.

Great job writing an oops post, with a short and effective explanation. Strong upvote for you!

The Ted Chiang piece, on closer reading, seems to be about denying the identity of the AI prompter as an artist rather than speaking to the particular limitations of the tool. For those who did not read, his claim is:

• Being an artist is about making interesting choices in your medium (paintings, novels, photography, digital).
• AI tools make all the choices for you; therefore you cannot be an artist when using AI tools.
• Further, the way the AI makes choices precludes them from being interesting, because they are a kind of arbitrary average and therefore cannot be invested with meaning.

To set himself apart from luddites and the usual naysayers, he uses Adobe Photoshop as an example of a tool where you can be an artist: it is a computer tool; it used to be derided by photographers as not being real art; but now it is accepted and the reason is that people learned to make interesting choices with it.

He appears to go as far as to say two people could generate an identical digital picture, one via photoshop and one via AI, and the former gets to be an artist and the latter does not.

I strongly endorse you writing that post!

Detailed histories of field development in math or science are case studies in deconfusion. I feel like we have very little of this in our conversation on the site relative to the individual researcher perspective (like Hamming’s You & Your Research) or an institutional focus (like Bell Labs).

That’s very interesting - could you talk a bit more about that? I have a guess about why, but would rather hear it straight than risk poisoning the context.

Could you talk a bit about how much time and effort you have invested into writing the wikipedia articles?

I think it would be helpful by making it easier for other people to judge whether they can have an impact this way, and whether it would be worth their time.

The claim that zoning restrictions are not a taking also goes against the expert consensus among economists about the massive costs that zoning imposes on landowners.

I would like to know more about how the law views opportunity costs. For most things, such as liability, it seems to only accept costs in the normal sense of literally had to pay out of pocket X amount; for other things like worker's comp it is a defined calculation of lost future gains, but only from pre-existing arrangements like the job a person already had. It feels like the only time I see opportunity costs is lumped in with other intangibles like pain and suffering.

Independently of the other parts, I like this notion of poverty. I head-chunk the idea as any external thing a person lacks, of which they are struggling to keep the minimum; that is poverty.

This seems very flexible, because it isn't a fixed bar like an income level. It also seems very actionable, because it is asking questions of the object-level reality instead of hand-wavily abstracting everything into money.

Over at Astral Codex Ten is a book review of Progress and Poverty with three follow-up blog posts by Lars Doucet. The link goes to the first blog post because it has links to the rest right up front.

I think it is relevant because Progress and Poverty is the book about:

...strange and immense and terrible forces behind the Poverty Equilibrium.

The pitch of the book is that the fundamental problem is economic rents deriving from private ownership over natural resources, which in the book means land. As a practical matter the focus on land rents in the book heavily overlaps the modern discussion around housing. The canonical example of the problem is what a landlord charges to rent an apartment.

One interesting point is that while UBI is suggested (here called a Citizen's Dividend), it is for justice reasons, and is not proposed as a solution to poverty. I expect Henry George would agree that a UBI would not eliminate poverty, and would predict it mostly gets gobbled up by the immense and terrible forces behind the Poverty Equilibrium.

I feel like the absence of large effects is to be expected during a short-term experiment. It would be deeply shocking to me if there was a meaningful shift in stuff like employment or housing in any experiment that doesn't run for a significant fraction of the duration of a job or lease/rental agreement. For a really significant study you'd want to target the average in an area, I expect.

This is why San Francisco was chosen as the example - at least over the last decade or so it has been one of the most inelastic housing supplies in the U.S.

You are therefore exactly correct: it does not comply with basic supply and demand. This is because basic supply and demand usually do not apply for housing in American cities due to legal constraints on supply, and subsidies for demand.

I acknowledge the bow-out intention, and I'll just answer what look like the cruxy bits and then leave it.

There's no actual price signal or ground truth for that portion of the value.

Fortunately we have solved this problem! Slightly simplified: what a vacant lot sells for is the land value, and how much more a developed lot next to it sells for is the value of the improvements. Using the prices from properties recently sold is how they usually calculate this.

If it's NOT just using a land-value justification to raise the dollar amounts greatly, please educate me.

Let the record reflect that I totally expect someone to do this. But as for doing it in a non-insane fashion, we take what used to be a property tax and turn it into a sales tax, which is levied on sales of property.

There's a good guest blog post over at AstralCodexTen which digs into the value assessment problem which I think you would like. That whole series of guest blog posts was fascinating.

And with that, we'll call it!

My objection is to the core of the proposal that it's taxed at extremely high levels, based on theoretical calculations rather than actual use value.

I'm a little confused by what the theoretical calculations are in your description. The way I understand it - which is scarcely authoritative but does not confuse me - is that we have several steps:

1. Theory: a lot of the value of a piece of property is not because of work done by the owner, but instead because of other people being nearby.
2. Theory: this is bad. We should remove all the value provided by other people who aren't the owner.
3. Practical: we need to calculate what fraction of the value is provided just by other people.
4. Practical: we tax that fraction of the price, and of the income from the property.
5. Practical: we adjust the fraction to allow for measurement errors or whatever other consideration.

So my understanding is there are no calculations until you get to the practical considerations of applying the tax.

Your suspected answer is how current implementations of the system work, but they are also regular property taxes in the sense of being a very tiny fraction of the value which means the payments are manageable under normal circumstances, and don't take aim at eliminating the economic rent.

Just scaling up property taxes the way they work now with the new values would be an epically bad move, in the same vein as the taxing unrealized capital gains at a high rate is.

The way I understand it, once we agree the focus is on the economic rent problem, we shift to taxing the sale price and the income from the property because these are how economic rents are captured, rather than an impossibly high annual payment. A homeowner doesn't capture economic rent just by living on the property, as I see it.

So for myself I would not vote for a measure that just scaled up property taxes, but would vote for one that did the sale/revenue taxation.

That isn't how the taxes are assessed, as a practical matter. The value of the land and the value of buildings are assessed, mostly using market data, and then the applied tax is the ratio of the land value to the property value, so for example in an apartment building that fraction is taxed out of the rent payments, and when a property is sold that fraction is taxed from the sale price.

I do notice that we don't have any recent examples of the realistically-full land tax interacting with individual home ownership; everywhere we see it is treated the same as a regular property tax. While it seems reasonable to me that non-income-producing properties should not require regular payments and instead only be taxed at point of sale, this is an adaptation rather than a strict application of the theory.

They can only make decisions that generate enough income to pay the taxes

Out of curiosity, how is this different from current property taxes, or from mortgages for that matter?

“Government will make better resource decisions than profit-motivated private entities”

I think you landed on the crux of it - under the Georgist model, individuals (or firms) still make the decisions about what to do with the resources. What the government does is set a singular huge incentive, strongly in the direction of "add value to the land."

I don't have an answer to this question, but I would register a prediction:

• Georgism believers < communism believers
• Georgism popularity > communism popularity

The latter is mostly because there are a bunch of people who really hate communism and will prefer almost literally anything else in a survey.

I feel like suburban homeowners are the key group here. They have the most votes among landowning groups, and the strongest motivation to oppose anything that reduces property values because their home represents all of their wealth. There is also the way-of-life question, because the economics of the suburbs seem really difficult under Georgism.

Something like an exemption for the first sale after the tax is passed would be a simple solution to taking some of the sting out, and makes sure no one has to unilaterally lose their investment to a tax.

Huzzah for enriched!

Things to consider:

• Pays out faster than regular real estate development on a within-sector basis.
• Here I envision industrial/infrastructure uses, but for areas that are sufficiently difficult (New York or California, for example) it could be viable for things like housing in combination with traditional development (permit company -> development company -> homebuyer).
• You almost definitely cannot get "general construction" permits whereby you can build anything, which on the one hand is limiting and requires strategic choices in permitting.
• On the other hand it allows some strategic influence over the development of useful sites.

Special Purpose Permit Companies

How many kinds of environmental and/or building permits could be gotten by company A, and then used for company B's project, after company B buys company A? For example, could we form a special purpose company to pursue navigating all the NEPA stuff for building a power plant at a particular site, and then sell that company to a power company, who could then begin construction immediately?

Consider real estate developers: one company buys the land and develops it, which means do all the construction, and then sells it to other companies who use the buildings.

I propose an extension of this model, where one company will buy the land and do the legal/regulatory work, and then sell the site+permits to another company who will do the construction of the facility.

The advantage here is that rather than one company taking the whole risk of the land and permitting process through to completion of the needed facility, the land and permitting can be handled by a special purpose company which can be sold, construction-ready, to a market of potential buyers. Because there are multiple potential buyers, the risks are lower than they are for one company doing the whole process alone, and normally you can't sell permits/licenses/etc once they are granted so if the project falls through for some reason the work done on the regulatory stuff is wasted.

If widely adopted as a practice, what I hope this would allow is a lot of potential building sites for needed infrastructure can be prepared for construction simultaneously. While this will do nothing to speed up projects starting now, it would potentially cut years off the construction time of the next generation of facilities as an abundance of sites would be waiting for them already.

The most frequent expression I see used for this is alpha, by which people mean the temporary advantage that comes from new things in finance. It has since moved into entrepreneurship lingo. However, I note that alpha speaks to a competitive environment, where you lose the advantage because other people are doing it; it isn't normally used to cover the situations you describe where a company shoots themselves in the foot, for example.

Even if their abstractions are different from ours, a key valuable thing is to predict us and thus model our abstractions, so even if you started out with alien abstractions you would then also want something closer to ours?

I am closer to John than Eliezer on this one so it is not a crux for me, but speaking just to this reasoning: it seems to me that by the time the AI is deciding via its alien concepts whether or not to simulate us, the issue is completely out of our hands and it doesn't matter whether we can interpret its simulations. Why wouldn't the concepts by which it would judge the simulation also be the concepts we want to govern in the first place?

While I do not use the platform myself, what do you think of people doing their thinking and writing offline, and then just using it as a method of transmission? I think this is made even easier by the express strategic decision to create an account for AI-specific engagement.

For example, when I look at tweets at all it is largely as links to completed threads or off-twitter blogs/articles/papers.

 

• An atom isn't a chair.
• Adding an atom to something that's not a chair doesn't make it a chair.
• Therefore, nothing is a chair.

I've decided to call this Zeno's System Paradox.

I wonder how feasible it would be to make something that would allow people to coordinate for consumption like unions for bargaining, parties for politics, or corporations for production. In particular I have in mind targeting entertainment and social media consumption because of the prevalence of recommendation algorithms here.

The intuition is that these algorithms are political computations: who gets the advertising traffic, what type of content is in demand, which consumers are catered to.

The thing being coordinated on is the consumer behavior; namely what people click on.

Examples:

• like a union, but instead of collective bargaining to sell labor, it is collective behavioring^[1] to consume content.
• like the old website Massdrop, but instead of coordinated buying it is coordinated clicking.
• like a political party, but instead of coordinating votes, it coordinates clicks.
• like a company, but instead of coordinating work, it coordinates consumption.

Concretely, I have in mind something like a browser widget that overlays indicators of the most impactful consumption choice to make to influence the algorithm in the direction you want, such as an icon or a color filter. This would run over things like youtube, instagram, tiktok, etc.

Another way to think of this: a value handshake between whatever algorithm the company is using and an algorithm you would choose instead.

1. ^{^}

   behavioring - by this I mean consciously conforming to a pattern of behavior so it shows up in behavior analysis, in this case consumer behavior tracking which is part of the function of recommendation algorithms.

I read a story once about a billionaire who managed to flout a zoning limitation by building himself an absurdly huge high-rise single apartment. Once that was done, he waited a bit, and either using a loophole that already existed or one he finagled into the law, he added a bunch of walls inside is personal mega-apartment to and then sold off all these new units as regular apartments.

Now I think of this in the context of the American dredging and shipping laws. There's a lot of rules surrounding dredges and shipping vessels, but there are a lot fewer surrounding luxury items like yachts. So imagine a group of investors building a big, state of the art shipyard that builds yachts, but which can be converted for little or no additional cost to build dredges or vessels suitable for river shipping or similar.

A simpler alternative scheme is to just comply with the laws but use yachts or similar less-regulated vessels as a lead product to build capacity for the more economic ones, analogous to how Tesla strategically built an electric sports car to generate interest and funds for eventually building electric sedans.

Reading this after some months it looks like the majority of the commenters interpreted the post as being "I found a bunch of things that increase IQ," but it feels like the point of the post is more like "Anyone can increase their IQ by trying a bunch of plausible things."

If I am right, for your purposes, a better experiment would be other people trying different batches of interventions at a similar intensity for a similar length of time. Does that sound right?