4 min read

🤱Mom says🗣️ it's my😤 turn to kill☠️ 👿Chaos!😈

“I’m😡 here to kill💀 Chaos.😈 That’s my😡 mission.😤” [STOCK BAT🦇 SOUND FX AND STRUGGLING🥵 NOISES] “Looks👀 like Chaos😈 has been ☃️waiting⏰ for us🤼.” “You’re gonna😱 make us🤼 go in there and find you😈?😭” “Guess❓ we’ll just show🪄 ourselves👥 in.🚪” “I only know🧠 one1️⃣ thing🪨: I😡 want😤 to 🔪kill☠️… 😈Chaos😱… I need🥵 to.” [TIME⏰ TO DIE☠️] “It’s not🙅 a hope🥺, or a dream🤯… it’s like a hunger😋, a thirst🥵…” [MOVE ASIDE🏃] [YOU’RE👉 DUST💨] [🤣🤣UUOOOGGHH🤣🤣] “You’re😡 sure Chaos👿 is here?🏚️” “Yeah👍, we can only squash👢 🧛monsters🧟 for so long➡️➡️! I hate🤬 doing pest🦟🐛 control🎮.” “This👇 is the ☠️shrine⛪ of 👿chaos😈. He’s here.😤 We👥 just have to hunt🏹 him down👇.” [FEEL🤱 THE PAIN🤕] “The darkness🕶️ is so thick🍑 I can taste🥵🍑😋 it… this is it🤡. No🙅 doubt about it🤡.” [🔪DIE☠️ ALREADY] “There👉 was a knight🤺 who 👈left👈 on the same 🏃journey🏃 as you👉… but🍑 never returned 😭. His name💳 was 🌿Garland🌿. If you could🙇, I would be grateful🙇🏻‍♀️ if you looked for him.🙋” “😈CHAOS.😈” “I’LL🤬 CRUSH👢 YOU👉 NGGGH🥵” “KNOW💭 WHEN YOU’RE BEATEN🤜!” “And who🤔 are you?😳” “We’re🫂 here to ☠️kill🔫 😈Chaos.👿” “The prophecies🔮 very own Warriors🤺 of Light.🕯️” “🤔Really?😯…” “He😠 always wore👔 such splendid😋 armor, with a 🪖helmet⛑️ that was so 😱terrifying😱 to 👀behold👀…” “It’s 🌿Garland🌿!😯😯😯” “No…🙅 I am become😤 😈CHAOS👿! MMMMH😋 GHAAA😫”

In Michael Lewis' book Flash Boys, it tells the story of the "flash crash" and how high-frequency trading (HFT) or "flash trading" could become the market events HFT algorithms were being trained to respond to. Brad Katsuyama, who would go on to create IBX--a market that was not as susceptible to the kind of market forces of HFT that he saw while at the Royal Bank of Canada that drives much of this story-, is the banker in this scenario, and there are many, many technologists involved who, objectively, do their job very well. The problem is that what makes technologists good at their job was problematic to why they were being asked to do it, and in the end, as always, Marxian alienation from their labor would will-out, and one would wind up in prison (something I won't discuss here– I encourage you to read the book, it just kind of drives home the meta-narrative on its own), not because they committed a crime, but because the industry they serviced was ill-equipped to understand the demands they were making.

At the core of the book is the technical achievement: IBX's soon-to-be CTO installing a datacenter with the lowest possible latency to the exchanges, while also complying rigidly with regulations about proximity, etc. Algorithms that investment institutions can employ and use to power entire HFT operations. All of this efficiency results in a cascade of micro-crashes that occur and resolve, and because there's the cushion of massive amounts of capital (much of it not real money, to begin with), this barely registers systemically, but does for investors that are not institutions. It begs an ethical question of the technologists, sure, but I think it begs an even bigger ethical question for the financial culture in this country; if our banking and exchange system is ill-equipped to handle true, algorithmically defined efficiency, so when faced with it, the system basically ceases to function, who is really wrong here? The person making the demand, or the person fulfilling it whose compensation isn't based on their unrealized profit from the endeavor the way the person making the demand is? Arguably, only the former, but objectively, as an enabling factor the latter– one could, however, argue the latter had no reason to know or care, because that's how alienation from one's labor works.

Historically, this is a condition where revolution becomes more and more likely as tolerance erodes, and expectations rise; however, because of this efficiency, the impact and recovery of these tolerance-eroding events cycles with increasingly frequency to the point of reaction by humans simply becoming impossible, outside of a vague, inaccessible aggregate. This is where the engineers come into it– they're not part of the trade, they just build the system to reflect the laid-down guidelines, but because of the behavior of traders and volume of trades, the system simply can't keep up, and algorithms meant to train on market data begin to react to events the algorithm, itself, anticipated and began to act on, which meant other algorithms anticipated and began to act on, and so forth, right up to where Brad Katsuyama at RBC queueing up a trade begins to see a stock price fluctuate because of his pending order before it's even executed.

I don't mean this as a defense of the engineers, but that their circumstance is one that people in all manner of industry experience every day– does an Amazon warehouse worker contribute to the net-badness of Amazon as a corporate entity, even though they are perhaps the most impacted by this net-badness? This is an extreme comparison (given that software engineers generally have better, if not differently challenged, workplace conditions), but one that drives home the point that, irrespective of compensation, the dynamics of Marxian labor relations between employee and employer still apply.

I don't have a real point here other than to say, within these compelling narratives, of which there is already so much value that anyone, without the benefit of theory, could arrive at some great conclusions about our economic culture, there is room to apply theory and take this messaging and framing further and apply to so much other work that is considered "apart" from labor or working class socioeconomic analysis.

I said I wouldn't link-dump resources in this blog, but here is some theory to lather on this as liberally (or not) as you'd like.

See you in orbit.