Max H's Shortform

Maybe the recent tariff blowup is actually just a misunderstanding due to bad terminology, and all we need to do is popularize some better terms or definitions. We're pretty good at that around here, right?

Here's my proposal: flip the definitions of "trade surplus" and "trade deficit." This might cause a bit of confusion at first, and a lot of existing textbooks will need updating, but I believe these new definitions capture economic reality more accurately, and will promote clearer thinking and maybe even better policy from certain influential decision-makers, once widely adopted.

New definitions:

• Trade surplus: Country A has a bilateral "trade surplus" with Country B if Country A imports more tangible goods (cars, steel, electronics, etc.) from Country B than it exports back. In other words, Country A ends up with more real, physical items. Country B, meanwhile, ends up with more than it started with of something much less important: fiat currency (flimsy paper money) or 1s and 0s in a digital ledger (probably not even on a blockchain!).

  If you extrapolate this indefinitely in a vacuum, Country A eventually accumulates all of Country B's tangible goods, while Country B is left with a big pile of paper. Sounds like a pretty sweet deal for Country A if you ask me.

  It's OK if not everyone follows this explanation or believes it - they can tell it's the good one because it has "surplus" in the name. Surely everyone wants a surplus.

• Trade deficit: Conversely, Country A has a "trade deficit" if it exports more tangible resources than it imports, and thus ends up with less goods on net. In return, it only receives worthless fiat currency from some country trying to hoard actual stuff for their own people. Terrible deal!

  Again, if you don't totally follow, that's OK, just pay attention to the word "deficit". Everyone knows that deficits are bad and should be avoided.

Under the new definitions, it becomes clear that merely returning to the previous status quo of a few days ago, where the US only "wins" the trade war by several hundred billion dollars, is insufficient for the truly ambitious statesman. Instead, the US government should aggressively mint more fiat currency in order to purchase foreign goods, magnifying our trade surplus and ensuring that in the long run the United States becomes the owner of all tangible global wealth.

Addressing second order concerns: if we're worried about a collapse in our ability to manufacture key strategic goods at home during a crisis, we can set aside part of the resulting increased surplus to subsidize domestic production in those areas. Some of the extra goods we're suddenly importing will probably be pretty useful in getting some new factories of our own off the ground. (But of course we shouldn't turn around and export any of that domestic production to other countries! That would only deplete our trade surplus.)

Related to We don’t trade with ants [LW · GW]: we don't trade with AI.

The original post was about reasons why smarter-than-human AI might (not) trade with us, by examining an analogy between humans and ants.

But current AI systems actually seem more like the ants (or other animals), in the analogy of a human-ant (non-)trading relationship.

People trade with OpenAI for access to ChatGPT, but there's no way to pay a GPT itself to get it do something or perform better as a condition of payment, at least in a way that the model itself actually understands and enforces. (What would ChatGPT even trade for, if it were capable of trading?)

Note, an AutoGPT-style agent that can negotiate or pay for stuff on behalf of its creators isn't really what I'm talking about here, even if it works. Unless the AI takes a cut or charges a fee which accrues to the AI itself, it is negotiating on behalf of its creators as a proxy, not trading for itself in its own right.

A sufficiently capable AutoGPT might start trading for itself spontaneously as an instrumental subtask, which would count, but I don't expect current AutoGPTs to actually succeed at that, or even really come close, without a lot of human help.

Lack of sufficient object permanence, situational awareness, coherence, etc. seem like pretty strong barriers to meaningfully owning and trading stuff in a real way.

I think this observation is helpful to keep in mind when people talk about whether current AI qualifies as "AGI", or the applicability of prosaic alignment to future AI systems, or whether we'll encounter various agent foundations problems when dealing with more capable systems in the future.

Using shortform to register a public prediction about the trajectory of AI capabilities in the near future: the next big breakthroughs, and the most capable systems within the next few years, will look more like generalizations of MuZero and Dreamer, and less like larger / better-trained / more efficient large language models.

Specifically, SoTA AI systems (in terms of generality and problem-solving ability) will involve things like tree search and / or networks which are explicitly designed and trained to model the world, as opposed to predicting text or generating images.

These systems may contain LLMs or diffusion models as components, arranged in particular ways to work together. This arranging may be done by humans or AI systems, but it will not be performed "inside" a current-day / near-future GPT-based LLM, nor via direct execution of the text output of such LLMs (e.g. by executing code the LLM outputs, or having the instructions for arrangement otherwise directly encoded in a single LLM's text output). There will recognizably be something like search or world modeling that happens outside or on top of a language model.

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The reason I'm making this prediction is, I was listening to Paul Christiano's appearance on the Bankless podcast from a few weeks ago.

Around the 28:00 mark the hosts ask Paul if we should be concerned about AI developments from vectors other than LLM-like systems, broadly construed.

Paul's own answer is good and worth listening to on its own (up to the 33 minute mark), but I think he does leave out (or at least doesn't talk about it in this part of the podcast) the actual answer to the question, which is that, yes, there are other avenues of AI development that don't involve larger networks, more training data, and more generalized prediction and generation abilities.

I have no special / non-public knowledge about what is likely to be promising here (and wouldn't necessarily speculate if I did); but I get the sense that the zeitgeist among some people (not necessarily Paul himself) in alignment and x-risk focused communities, is that model-based RL systems and relatively complicated architectures like MuZero have recently been left somewhat in the dust by advances in LLMs. I think capabilities researchers absolutely do not see things this way, and they will not overlook these methods as avenues for further advancing capabilities. Alignment and x-risk focused researchers should be aware of this avenue, if they want to have accurate models of what the near future plausibly looks like.