Philip Atkinson @UnrationalPhil

It's not to everyone's taste (indeed some aspects are a study in gaming's propensity to go way beyond acceptable boundaries), but it does make sense. Kalshi and Polymarket were primarily outsider startups that grew out of the tech scenes (crypto for Poly, SF for Kalshi). They were therefore always adjacent to the growth hacking discourse where visibility is valued above almost anything. They then hired people/content providers for 'growth' (aka marketing) who instinctively aligned with that. Those people were highly social media native, they were comfortable with memes/UGC style videos etc. You can still see it now in how the key people interact on here and their suite of Meta ads (if you look at the ad library). It clearly works for them - it's just an formalisation of what the grey market crypto casinos and successful drop shippers do, and it also allows them to present an edgier brand to an audience they understood. Contrast that with FanDuel and DK, who evolved a sort of slicker, more consistent, and more professional commercial approach. That works for them, especially when they have concerns over advertising/targeting regulations and breaking any rules around the content (which is a massive thing for those entities). x.com/byajperez/stat…

A.J. Perez @byajperez

2 weeks ago

Polymarket and Kalshi’s social media approach has never made any sense. From partnering with antisemitic accounts to @WSJ’s amazing report today on creators paid for deceptive Polymarket videos, does this make anyone want to risk money on these sites? wsj.com/business/media…

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Detailed article: x.com/UnrationalPhil…

Philip Atkinson @UnrationalPhil

2 weeks ago

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Used @laminalabs's Simi to create a video summary of my article/essay on company brains (link in first reply). Required a bit of iteration to get right (and there are still some rough edges), but generally reasonably happy. Interesting product, @sudiprokaya!

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@kwansfull @Lelepokerr The solvers etc are just the latest manifestation of a two decade trend. There’s no going back, and LLMs (and more) mean it only downhill from here.

Philip Atkinson @UnrationalPhil

a month ago

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I wrote the final article in a series about the decline of (online) poker. This one is about the boom that never was. Have a read here 👇 x.com/UnrationalPhil…

Philip Atkinson @UnrationalPhil

a month ago

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I wrote the third article in a series about the decline of (online) poker. This one is about the perception of unfairness. Have a read here 👇 x.com/UnrationalPhil…

Philip Atkinson @UnrationalPhil

a month ago

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@astraffon I feel like this is a gap between what people who work inside the industry understand and what those outside do. No structure, whether sports book/exchange/etc is neutral, and the overall balance is hard to find. Wrote about it here: x.com/UnrationalPhil…

Philip Atkinson @UnrationalPhil

a month ago

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I wrote the second article in a series about the decline of (online) poker. This one covers the development of the surrounding tool/learning ecosystem and the impact it had on new players. Have a read here 👇x.com/UnrationalPhil…

Philip Atkinson @UnrationalPhil

a month ago

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The issue is that many plans for making a product better, especially in a muture product, are often team efforts. You can often end up blocked/waiting on other aspects from other people. This previously lead to a sort of inefficiency of unusable resources (which AI helps resolve, but not in the most optimal sense).

I suspect that what you are seeing here is a function of how coding agents have entered product mature organisations (basically engineering outwards). In that scenario, developers, who now have the bandwidth to handle the nice to have refactors/resilience improvements which they could never quite get round to, have simply pushed onwards. The problem is that, in many cases, such work was marginally useful, or impossible to link to relevant end outcomes (if you improve a set of tests to handle an edge case, but that edge case never got reached in real world use, is the system any more stable?). The bull case is that such work is finite, the bear case is that it is not (and that down/upstream coding changes driven by AI effectively create ever more marginal work). For most of the last 14 months, @steipete was my AI productivity North Star (in so far as it is possible to adopt Peter's approach, which is basically sui generis). I think I am actually more aligned with @badlogicgames and @mitsuhiko now - it's about a more slowed down, thoughtful application of these powerful tools. I think pretty soon you will see a different type of organisational structure appear, one where the whole framework is a sort of agentic entity that wraps human activity. @jack somewhat spoke about this recently (and Peter is sort of a forerunner of that as well in a way).

Ed Zitron @edzitron

a month ago

Uber’s COO has said that it’s getting “harder to justify” its AI costs because there was no way to show a link between AI spend and any meaningful increase in useful features. This is the first time I’ve seen a company say this directly. businessinsider.com/uber-coo-andre…

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This is the first (of a few) articles, that I have been planning to write for a long time, on what caused the demise of online poker (in this case how it relates to prediction markets now). Going to give credit to @mattkalish - the recent prediction market kerfuffles inspired me to get writing. I would note also that @SteveRuddock has been saying essentially the same thing for a long time - strongly recommend you read his newsletters. x.com/UnrationalPhil…

Philip Atkinson @UnrationalPhil

a month ago

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Sharps and fish are relative concepts. A person might be a sharp relative to 98% of the players on DK, but they could easily be a fish to the top 10% on a PM. Forced to trade with that group, they would eventually lose their money. Note that even in a scenario where you have a specific market advantage against the best, one of the defining features of 'sharp' play is the ability to very quickly realise when you are at an informational/skill disadvantage and adjust your behaviour accordingly. This is why you see specific behaviours in poker rooms / some of the more established betting exchanges which are detrimental to the overall ecosystem etc.

It’s one of the fundamental problems poker had as well - the product that caters to your most valuable customers is not the one that is optimal for the players they want to play against. Inside @PokerStars (which was teaming with serious poker people) I used to describe it as ‘building for the person you actually sit beside rather than the one you want to sit beside’. Very difficult to square that circle.

It’s one of the fundamental problems poker had as well - the product that caters to your most valuable customers is not the one that is optimal for the players they want to play against. Inside @PokerStars (which was teaming with serious poker people) I used to describe it as ‘building for the person you actually sit beside rather than the one you want to sit beside’. Very difficult to square that circle.

One of the interesting things about the evolution of online poker (having seen it from the inside and now outside) is that, in general, the love for the game itself never died. Ultimately, many players just became cynical and jaded about the product. Sports betting, especially on exchanges/PMs, has a much shorter skill level feedback loop and less core product engagement than poker, so you have to work extra hard to keep the retail flow.