* This brings us to a larger question - why did we build Soros?
First, let's address the elephant in the room: we were inspired by George Soros' theory of reflexivity and how human tendencies affect markets more prominently than expected. Yes, there's a corny backronym [0]. No, this is not a political statement or endorsement of his views.
Coming back to the main point, we (the founding team at Lookback Labs) have both spent a long time at the intersection of financial markets, technology, and machine learning. During that time, one key thing that kept bothering us [1] was simply this: when a geopolitical crisis breaks, an investor's actual problem is not really to find out "what is happening now" — it's more of "which scenario plays out, how likely is each one, and what do I buy, sell, or hedge under each? For how long?"
There are a ton of existing tools and services that seek to answer the first question reasonably well (newsletters such as StratFor, publications such as Foreign Affairs and Foreign Policy, Bloomberg terminals for breaking news, etc.).
None of these answer the other questions particularly deftly. Sure, one can engage with ChatGPT (or Claude if one prefers), and play through multiple scenarios. You will, of course, miss out on the grounded structural model that powers Soros' analysis, along with the simulations that serve up the relative probability estimates.
Also, one of the worst things purely LLM-based ad hoc frameworks do is assume that countries are monolithic decision-making units from a game-theoretic perspective. This is hardly the case - "Iran" doesn't make choices, Mojtaba and the IRGC faction does. "China" doesn't decide, the Politburo Committee does. And so on.
There are of course formal analytical frameworks that dig deeper, studying groups, factions, organizations that are jostling to gain control (Bruce Bueno de Mesquita's Expected Utility Model and selectorate theory [2] is the most academically serious and is a prime inspiration for our system design), but they are extraordinarily hard to operationalize in real time, and produce no market implications.
To sum up, the choices are stark: ask AI and hope for the best, or build out your own systematic framework to organize evidence, assumptions, and implications. We chose the latter path.
Zooming out, our mission at Lookback Labs (https://www.lookbacklabs.com/) is to build "the intelligence layer for AI-native investing"; accordingly, Soros is the first of several agentic systems that we are designing across the systematic and discretionary spaces, that are both usable and useful from the get go, and not merely demo eye candy.
* Some minor details:
(1) We are currently in private beta for Soros and are onboarding selectively.
(2) The static demo is not completely static; you can still chat with the analysis (up to 20 messages a day per IP).
(3) We are still working on pricing: something that captures the value Soros provides.
(4) We want this to work for individual investors as well, not just institutional desks, and would love to price accordingly.
We're curious to hear what the HN community thinks about our approach. AUA!
Feel free to reach out offline if you'd like! We are, sadly enough, on LinkedIn, but are also available via email (anshuman/karen@lookbacklabs.com)
PS: As is probably obvious to the diligent reader :), every token in this post has been lovingly handcrafted by the Lookback Labs team.
[0] Scenario-Oriented Reasoner for Opportunity Synthesis. Lol.
[1] Many things bothered us. Buy us drinks, get stories.
[2] We heartily recommend two of BdM's books: "Predicting Politics" and "The Dictator's Handbook"
Hmmm, good question. I think one interesting incident for us was when we saw scenario probabilities being updated near last Friday EOD for the US-Iran conflict, biased towards further kinetic action by the US around Kharg island (?). This was basically captured from changes in odds for Polymarket events that the system was tracking.
The news came in a few minutes later, post equity market closing.
> So someone monitoring Polymarket could have reached the same conclusion?
Maybe? If they are professionally trading prediction markets, I'm pretty sure that would be the case. Polymarket especially is a great source of insider traded information, as you pointed out.
We do near realtime tracking of most major markets, plus X accounts that Soros identifies as being important. The system also composes search queries per analysis, along with frequency of scanning, and that's run as requested. (We use a mix of Perplexity and other smaller search providers, along with Exa via OpenRouter's integration.)
Other inputs might be direct statements from leaders involved in the conflict, especially Trump. Also maybe bond market and oil market price movements?
Then you would want to generate an alert when you an actionable prediction. You don't want the user to have to prompt the AI. It needs to be running in the background, having been prompted on the scenarios to monitor?
* This brings us to a larger question - why did we build Soros?
First, let's address the elephant in the room: we were inspired by George Soros' theory of reflexivity and how human tendencies affect markets more prominently than expected. Yes, there's a corny backronym [0]. No, this is not a political statement or endorsement of his views.
Coming back to the main point, we (the founding team at Lookback Labs) have both spent a long time at the intersection of financial markets, technology, and machine learning. During that time, one key thing that kept bothering us [1] was simply this: when a geopolitical crisis breaks, an investor's actual problem is not really to find out "what is happening now" — it's more of "which scenario plays out, how likely is each one, and what do I buy, sell, or hedge under each? For how long?"
There are a ton of existing tools and services that seek to answer the first question reasonably well (newsletters such as StratFor, publications such as Foreign Affairs and Foreign Policy, Bloomberg terminals for breaking news, etc.).
None of these answer the other questions particularly deftly. Sure, one can engage with ChatGPT (or Claude if one prefers), and play through multiple scenarios. You will, of course, miss out on the grounded structural model that powers Soros' analysis, along with the simulations that serve up the relative probability estimates.
Also, one of the worst things purely LLM-based ad hoc frameworks do is assume that countries are monolithic decision-making units from a game-theoretic perspective. This is hardly the case - "Iran" doesn't make choices, Mojtaba and the IRGC faction does. "China" doesn't decide, the Politburo Committee does. And so on.
There are of course formal analytical frameworks that dig deeper, studying groups, factions, organizations that are jostling to gain control (Bruce Bueno de Mesquita's Expected Utility Model and selectorate theory [2] is the most academically serious and is a prime inspiration for our system design), but they are extraordinarily hard to operationalize in real time, and produce no market implications.
To sum up, the choices are stark: ask AI and hope for the best, or build out your own systematic framework to organize evidence, assumptions, and implications. We chose the latter path.
Zooming out, our mission at Lookback Labs (https://www.lookbacklabs.com/) is to build "the intelligence layer for AI-native investing"; accordingly, Soros is the first of several agentic systems that we are designing across the systematic and discretionary spaces, that are both usable and useful from the get go, and not merely demo eye candy.
* Some minor details:
(1) We are currently in private beta for Soros and are onboarding selectively.
(2) The static demo is not completely static; you can still chat with the analysis (up to 20 messages a day per IP).
(3) We are still working on pricing: something that captures the value Soros provides.
(4) We want this to work for individual investors as well, not just institutional desks, and would love to price accordingly.
We're curious to hear what the HN community thinks about our approach. AUA!
Feel free to reach out offline if you'd like! We are, sadly enough, on LinkedIn, but are also available via email (anshuman/karen@lookbacklabs.com)
PS: As is probably obvious to the diligent reader :), every token in this post has been lovingly handcrafted by the Lookback Labs team.
[0] Scenario-Oriented Reasoner for Opportunity Synthesis. Lol.
[1] Many things bothered us. Buy us drinks, get stories.
[2] We heartily recommend two of BdM's books: "Predicting Politics" and "The Dictator's Handbook"
Ok, do you have any interesting, unusual insights generated by Soros?
Hmmm, good question. I think one interesting incident for us was when we saw scenario probabilities being updated near last Friday EOD for the US-Iran conflict, biased towards further kinetic action by the US around Kharg island (?). This was basically captured from changes in odds for Polymarket events that the system was tracking. The news came in a few minutes later, post equity market closing.
So someone monitoring Polymarket could have reached the same conclusion?
But Soros can process many more inputs than a human analyst?
Polymarket seems like a very good input, because of the probable insider trading. What other inputs do you use?
> So someone monitoring Polymarket could have reached the same conclusion?
Maybe? If they are professionally trading prediction markets, I'm pretty sure that would be the case. Polymarket especially is a great source of insider traded information, as you pointed out.
We do near realtime tracking of most major markets, plus X accounts that Soros identifies as being important. The system also composes search queries per analysis, along with frequency of scanning, and that's run as requested. (We use a mix of Perplexity and other smaller search providers, along with Exa via OpenRouter's integration.)
Hope this helps! Thanks for your questions!
Other inputs might be direct statements from leaders involved in the conflict, especially Trump. Also maybe bond market and oil market price movements?
Then you would want to generate an alert when you an actionable prediction. You don't want the user to have to prompt the AI. It needs to be running in the background, having been prompted on the scenarios to monitor?
Exactly! That's how it works - the static demo is just that, static.
Would love to onboard you for the full thing if you'd like! Just LMK (team@lookbacklabs.com) or add your info on the site