In other more heavily regulated industries, whistleblowers are fortunately compensated and protected for raising such ethical issues. I wonder how far tech can go before we start to see similar government agencies and rules put in place to do the same.
This whole comment is a bit too sensational for me, however I want to highlight one fragment:
> When you have so many people alive, who can do nothing in exchange for food [...]
AI, for now, only exists in the digital space and that's what it will primarily disrupt (at least initially). You're still going to need real people to mow the grass, maintain homes/buildings, ship goods and perform other basic services that underpin society. None of that changes drastically with AI in the picture.
Some people will be out of jobs or replaced, but fundamentally they will just have to do something more tangible to provide value in order to pay the bills.
The material I covered here has been well established in classic literature for quite some time (some of which is hundreds of years old), so none of this should come off as sensational.
Feel free to verify yourself through independent means if you have the capability to do so, its a lot of time investment, which is well worth it considering the subject matter represents an existential threat to survival.
Knowledge of existential dangers typically fall into a category where a person would be glad to spend any amount of time or money (assuming they were capable of it), to continue surviving past that danger.
You can find the relevant parts under social contract theory, Malthus/Catton on ecological overshoot, Mises & Menger, and others on the economic foundations and dynamics of fiat/money printing currency, History (Federalist Papers, and many others). If you've had a classical education (trivium/quadrivium curricula) none of this should surprise you.
Being deadly serious here, there is a mountain of vetted material that supports what I said, its not an original thought, it builds on the foundations that have largely been left with the shoulder's of giants by what is called education today. Public education doesn't really teach critical thought, and there are ongoing fights between administrators and the philosophy departments where they are trying to remove those optional classes entirely. You can't teach a lot of philosophy without having taken a course in critical thinking.
> AI for now only exists in the digital space.
You are mistaken about that. AI is also being used in the industrial automation space, as well as other domains such as building architecture and manufacturing (i.e. there are already 3d-printed buildings, CAD machining, AI based QA, etc).
I'm sure you've heard of Siemens, there are many other global companies that have already bridged the digital divide, some started this about 15-20 years ago, and technology has only improved since then.
Integration is now happening at an exponential pace because it has short term profits involved seeing as most business people see labor as the most expensive cost. It is fueled by non-reserve debt financing (money printing).
The magnitude between the peaks of the boom bust cycle is dependent on the shortfall of demand (growth/production) to capital(+profit baked in) lent over the entire cycle as an aggregate.
The continuance of the cycle depends on money retaining its fundamental properties, and those properties fail under money printing, which has been the status quo since 1970s to avoid deflation. Its a narrowing cliff-side that ends at a point no one can see ahead of time.
The failure of Coolidge to both initially regulate the rural bank loans, as well as failing to bail them out later; prior to the great depression led to the wholesale collapse of seasonal lending facility that largely caused the great depression as a chaotic whipsaw. This wasn't corrected until the US entered WW2.
These mechanics have repeated many times if you look into the detailed histories from primary sources. Penn Station bankruptcy collapse (1970s), Savings and Loan (1980s), Atari (1980s), dotcom bust (1990s), 2008 (CDOs), and the sharp increase in money printing with the Fed setting reserve rates to 0% (non-fractional reserve system) and changing to Basel3, the latter of which is based in objective value that fails given that value subjectivity has been long proven by economists and experts.
The issue is a core issue with understanding the mechanics of capital formation.
People are also generally extremely bad at recognizing exponential and logarithmic changes, and we have cognitive biases towards survivorship bias that confound towards an incomplete understanding which is punished when reality punches back (and it doesn't pull its punches).
You neglect that at the end of the cycle, like with any ponzi scheme the outflows exceed inflows, and value collapses to 0. There is nothing that can be exchanged.
Initially in such structured systems (debt and money printing) the value and benefits are front-loaded, but someone always has to pick up the tab with these types of systems when the investment is bad, if no one does or even has the ability to, then the same thing that happened under Coolidge happens at a global socio-economic level.
Distortions are generally not visible except in retrospect, far long after you can do anything to change the outcome.
That is why it is so critically important to have the discussions and conversation of these dangers occur before integration actually takes place.
The process of integration creates a burnt bridge, preventing anyone from going backward, it sails right into a maelstrom, risking the existence of all members both alive and those yet unborn by its members.
There are systems where there are points where you simply cannot sail the ship to safety because the dynamics involved overpower any human action cumulative or individual, and anyone on a ship like that will have its remnants crushed by overpowering forces in the vortex. The visibility might be poor, but knowing about the danger ahead of time may allow safe passage in its vicinity. An analogy sure, but there are many systems like this where without the proper frame of reference, you sail blind, and destructive outcomes await.
Whether its a maelstrom, a dam breaking, a avalanche, or tsunami, people survive by recognizing small but critical details and taking action well beforehand.
If using python with type annotations, linters like ruff and mypy do a great job at identifying issues. It's no substitute for tests and nor will it give you the same guarantees that rust will at compile time. But I think it improves the base quality of the code.
Yeah I see what you mean, you can always disable specific features, but I think that's a habit mypy tries to enforce. They consider redefining a variable bad practice.
Even in rust you have to be explicit about doing the same thing with an extra "let" statement.
Yeah that's fair enough, and I've lived with it because of how good MyPy is. But I always end with weird variable names like uuids_as_list as a result!
Adding to this, stochastic calculus matters more for modeling volatility/interest rates/derivatives. As you mention, Python/ML are more than suitable for many other areas within quant finance like optimization, algo development, signal research, etc.
This is a really exciting development. Can anyone currently using this chime in on working with pyodide/dependency management in this setting?
I imagine that this reduces the iteration time for developing excel integrations. It's unfortunate that direct db queries aren't supported, but I guess that's a wasm/pyodide issue.
I am not a random user, but the creator. The dependencies are managed via standard requirements.txt file, which is stored in the Excel workbook itself. When you open xlwings Lite with the workbook, the dependencies are installed from either PyPI or Pyodide's own repository (after the first download from the browser's cache).
Direct db queries are indeed a restriction of Wasm/Pyodide, but there are more and more databases offering a HTTP layer. For example, Supabase has this built-in via PostgREST. For Oracle, there is Oracle REST Data Services (ORDS). Ultimately, you can also build your own little proxy server, although that's a little bit more work, but might still be worth it for company-internal use.
HFTs/market makers are competing for pennies on the dollar. They do not hold risk (long term), they exist to readily provide liquidity to your buy/sell orders. HFTs can be quite profitable, but that doesn't make them inherently evil.
Can you elaborate why those bans will improve the market and what the issues are with HFTs in your eyes?
These dark pools and private rooms appear to be a response to HFT. To get away from HFT. So HFT appears to be fragmenting the market which is probably not a net good. If that is not the purpose of dark pools and private rooms, then what other purposes do they have?
One of the unique aspects of dark pools (and this can vary by venue) is that orders are segmented according to the source type (e.g. retail, client of the operator of the venue, institutional trader, etc) and the quality/grading of their order flow. As an institutional trader trying to leg into/out of a large position, you'll want many different execution options to help minimize price impact + information leakage + execution time. Dark pools are a great option in which you may have the option to specifically trade against non-toxic order flow (e.g. retail) to achieve better execution.
There are other reasons as well, but these venues essentially exist to solve some very niche problems for institutional trading. Public filings are available for review in case you're interested in the rules of each dark pool. [0]
Its a spectrum rather than a binary thing, however you are asking the right questions!
One of the things that is most powerful about K8s is that it gives you a lot of primitives to build things with. This is also its biggest drawback.
If you are running real physical infrastructure and want to run several hundreds of "services" (as in software, not k8s services) then kubernetes is probably a good fit, but you have a storage and secrets problem to solve as well.
On the cloud, unless you're using a managed service, its almost certainly easier to either use lambdas (for low traffic services) or one of the many managed docker hosting services they have.
Some of them are even K8s API compatible.
but _why_?
At its heart, k8s is a "run this thing here with these resources" system. AWS also does this, so duplicating it costs time and money. For most people the benefit of running ~20 services < 5 dbs and storage on k8s is negative. Its a steep learning curve, very large attack surface (You need ot secure the instance and then k8s permissions) and its an extra layer of things to maintain. For example, running a DB on k8s is perfoectly possible, and there are bunch of patterns you can follow. But you're on the hook for persistence, backup and recovery. managed DBs are more expensive to run, but they cost 0 engineer hours to implement.
BUT
You do get access to helm, which means that you can copypasta mostly working systems into your cluster. (but again like running untrusted docker images, thats not a great thing to do.)
The other thing to note is the networking scheme is badshit crazy and working with ipv6 is still tricky.
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