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Late to this comment thread, but Amazon actually excels at this type of long term measurement, through methodologies internally called HVA/DSI and DSE (to name just a couple).

- High Value Action / Downstream Impact == using a "twins" comparison, estimate the 12-month impact of a customer taking a particular action (e.g. sign up for Prime, watch their first Prime Video, etc.), compared to one who doesn't. HVAs are basically those "A's" which turn out to have a high numeric DSI value.

- Downstream Expectation == similar but very different - instead of quantifying the impact of a single action, DSE tries to estimate the combined downstream causal impact of a user taking an initial action... there's a sophisticated methodology there that tries to strip away confounding factors like "rich people who would've shopped more anyways, are also naturally more likely to sign up for Prime", because they truly want to measure the causal benefit of Prime itself, separate from the fact that richer customers generally spend more no matter what

These are both long-term methodologies, that were explicitly designed in response to the problems of: short term experiments that didn't capture long-term negative effects, and different parts of Amazon having vastly different methodologies for measuring business impact (e.g. page views vs search impressions vs downloads vs orders vs whatever... no, everyone should optimize for the same customer level financial metric which is a flavor of growth-adjusted composite contribution profit (GCCP) that's partly derived from DSE)

Ex-Amazonian here, and while I agree with the facts you present, I do think the "tall" vs "wide" debate is being misapplied here.

Amazon is extremely and perversely obsessed with, and good at, building decoupled systems at scale, which in essence means lots and lots of individual separate "tall" systems, instead of monolithic "wide" systems.

So IMO, Amazon subscribes to a "forest-of-'tall'-services" philosophy. And even at that meta level, I would say the forests are better off when they grow taller, rather than wider.

Well, we're halfway to proving this objectively. There's a pretty thorough database of lies told during the Trump presidency, captured here: https://www.washingtonpost.com/graphics/politics/trump-claim...

There is no identical dataset for other presidents (generally because they haven't lied enough at this level to warrant a dedicated database), but luckily, you can see that Trump's lie count goes up at a fairly steady rate week-to-week/month-to-month (i.e. there's no stretch of time where he stopped lying), so a little project for you would be:

- Go back to any other modern day president from the past 50 or so years, whoever you'd like to compare

- Pick 1 week or 1 month randomly, and pull up all their public statements or press coverage

- Then count the # of lies for that week or month, and extrapolate a comparison

I think you'll quickly find that it does not compare to Trump's overall average rate of 21 documented public facing lies per day (obviously we are not able to count private conversations).

I like this framing a lot, but it seems to conspicuously miss who makes "strategy"? Assuming policy != strategy

Unless you're making a subtle joke that, nobody makes strategy... Which if so, then kudos for a nice joke

Not saying I agree or even that the author is referring to these specific ideas, but there's plenty to anger people with: https://www.avclub.com/your-guide-to-the-wall-e-controversy-...

Well the article paints a pretty clear hypothesis, if you read it -- dowries are rooted in the idea that in a particular kind of society (not the ones where a household is losing out on a woman's labor and hence needs a bride price), the woman who is married off into her husband's household is seen as a burden, an outsider, one more mouth to feed.

Therefore a dowry is both compensation, and a demonstration of value (i.e. take my daughter because she has a high dowry, and is thus more desirable to marry than other girls with less dowry).

This is a wild time capsule of an article to reread now... Whatever happened to Facebook "M"? And why did anyone ever think this would survive as a business model?

https://techcrunch.com/2016/01/05/make-magics-assistants-do-...

How about, wildly dangerous relative to other commercial passenger jets? Second in fatal accidents per million miles flown only to the Concorde, which didn't fly very much. Why would you want to continue flying a plane that was statistically 5-10x more fatal than peers? Surely there's no good reason to subject passengers to that, even if it were still relatively safer than automobile fatalities or whatever else you might compare to, not when safer aircraft are generally available?

https://www.airsafe.com/events/models/rate_mod.htm

(Note this data is only up to 2019 for 737 models, but that's exactly the time period at which decisions had to be made, of whether to continue flying it or not).

Riffing on this: another aspect of how business-life is harder for Google is, who actually is the customer?

For Amazon (at least in the classic retail sense), the customer is clear and obvious: the person buying a thing from your website, who wants good-selection, cheap and fast/delivered to their door. It's relatively easy to orient your entire company around the question of: "but is this actually good for The Customer?" That is, until you feel you've established your business model well enough, that you no longer need to focus on creating customer value, and instead turn towards growing your business value (i.e. the shift of 3p sellers going from "gee this is a cool way to expand selection for customers beyond what Amazon 1p offers by itself", into "wow this is just a cesspool for fakes and fraud, but who cares since they all pay fees to Amazon").

For Google, with the nature of the search business and the fact that it is their primary cash cow, the incentives are "mixed" to put it mildly. The true customer of Google is not the users, but the advertisers. The users are simply an ingredient to be fed into the advertising engine -- any decisions you make to benefit the user, are only from the perspective of, not pissing them off so much that they leave your platform (and even that, is a sliding scale depending on the viability of alternatives -- Bing along wasn't very viable, but Bing + ChatGPT might be...).

In a twisted way, maybe comparing Google to Amazon is more like this:

- G's search users = A's warehouse workers

- G's advertisers = A's shoppers

How well does Amazon treat its warehouse workers? Only as well as needed to achieve 2 goals: not break employment laws too egregiously, and not churn through the entire employable labor pool too quickly.

This is actually a niche industry, that provides "deposit solutions". A family member used to work at this one for example, who just put a PR release based on the recent news cycle: https://www.reichandtang.com/2023/03/12/statement-on-protect...

They provide up to $50 million worth of combined coverage, by splitting across dozens of partner banks into $250k chunks. Yes there are fees associated with purchasing such a service, but I'm not really sure why it's not more popular or more commonly adopted as SOP for small to medium enterprises to do this.

Maybe there is a convenience or liquidity factor that I'm missing, or maybe people really didn't think the cost was worth it (but I'd guess many are probably reevaluating now, just how safe or unlikely they think a bank failure really is)?

I'm sorry, but the sports betting analogy is hilariously wrongheaded. In order to set any prices at all, books have reasonably good models for the sports they offer, including NFL. But these models are not and do not need to be perfect, in order for a profitable business model to exist. There are in fact, plenty of "sharps" who can beat the books on certain sports or certain types of markets (e.g. maybe you can model O/U markets very well, but money line not so much).

The book does not "stop taking bets" on the entire event in question. The book simply has to limit the "sharp" individually. Of course then we get into the whole cat and mouse game of chasing sharp action through straw bettors and playing games of information theory (i.e. how much money can a sharp win while still plausibly not outing themselves as an obvious sharp).

The book only would have a serious problem, if the entire world became sharp and every bettor was betting sharply. But alas there you have a paradox: anyone who is currently a sharp, has a strong self interest to preserve the secrecy of their models and methods, because knowing something that nobody else knows is precisely what creates their edge. And so therefore, as long as sharps have this self-interest, then there is no problem for books to keep operating, and the problem is reduced to one of identifying and limiting sharps occasionally (basically a "cost of doing business" that books acknowledge).

The only way for the scenario you describe to happen, is for disinterested actors with no thought of profit for themselves, to not only "solve" a particular market, but also release it to the entire world and make it a commodity. I'm not saying it's impossible, but I'm saying that normal economic incentives work against this outcome.

Remember, the author of the article constructed a scenario where, it was expected that the "main" function ends up treating an empty "CONFIG_DIRS" input as an uncatchable IOError; in other words, an empty array was invalid/not-allowed, per the rules of this program. Depending on the context in which you are operating, you may or may not have similar rules or requirements to follow.

Empty lists are actually generally not a big deal - they are just lists of size 0, and they generally follow all the same things you can do with non-empty lists. The fact that a "head" function throws an error on an empty list, is really just a specific form of the more general observation that: any array would throw an index-out-of-bounds exception when given an index that's... out of of bounds. So any time you are dealing with arrays, you probably need to think about, "what happens if I try to index something that's out of bounds? is that possible?"

In this particular contrived example, all that mattered was the head of the array. But what if you wanted to pick out the 3rd argument in a list of command line arguments, and instead the user only gave you 2 inputs? If 3 arguments are required, then throw an IOError as early as possible after failing to parse out 3 arguments; but once you pass the point of parsing the input into a valid object/struct/whatever, from that point forward you no longer care about checking whether the 3rd input is empty or not.

So again, it depends on your scenario. Actually the more interesting variant of this issue (in OO languages at least) is probably handling nulls, as empty lists are valid lists, but nulls are not lists, and requires some different logic usually (and hence why NullPointerExceptions aka NPEs are such a common failure mode).

I don't understand how you can compare the price of gas (which you convert into an equivalent $/kWh price, no issues so far), with the price of electricity, without trying to account for how long or how often either heating system has to run for... it's almost certainly not the same amount of time.

For example, let's imagine an experiment where 2 heating systems are tasked with doing the same thing -- let's say, to keep a 2000 sq ft home at an even 70F degrees, when the outside air temperature is 0F while the ground temp is whatever-it-is:

- How much gas would the gas system consume, over how many hours, to maintain that 70F temp? Maybe it uses X1 amount of gas, over Y1 hours, for Z1 price of gas, so you can multiply that out.

- How much electricity would a heat pump consume, over how many hours? Maybe it uses X2 amount of electricity, over Y2 hours, for Z2 price.

You're only comparing Z1 and Z2, how could that possibly tell you the whole story? Heat pumps can be >100% efficient (I've seen claims of up to 300% efficiency in ideal conditions), while gas maxes out at about 95%, so I don't know why you wouldn't try to measure/estimate the X and Y variables too, and why you'd only focus on the Z.

As another former Amazonian (but not near payments stuff by any means), judging from the outside, this doesn't sound like it equates to the death of Amazon Payments at all.

The PR statements are very carefully worded to say, strategic partner that will be "processing a significant portion of Amazon’s total payments volume." If significant portion was going to mean anywhere near 100%, I'm sure Stripe would have shouted that fact from the rooftops even louder (what a huge win that would be!).

For my money, this really just reads like mostly a quid-pro-quo deal, you give me X% of payments volume, I'll give you Y level of commitment to AWS. With no insider knowledge of any kind, I would wildly speculate that "significant portion" could mean a value of X as low as 5-10%.

That makes sense under a certain set of assumptions I suppose, namely that you might think AWS's problem arises mostly from normal customers not realizing that someone has hijacked their account to run GPU crypto mining without their knowledge, because GPU instances were not their intended use case. So "people that don't know what they are hiring" is the failure mode you're concerned with.

But I wonder more about, say a fraudster using stolen credit cards or gift card abuse, to open an account, or abuse a free trial, and if Lambda Labs makes it super easy and low friction to start up a GPU, then the fraudster can open an account, mine crypto immediately, rack up as much crypto as they can for days or a month until billing fails or trial expires, and just try to squeeze Lambda for as much computing power as they can subject to Lambda’s ability to detect fraud.

Low friction for customers, usually translates directly to higher risk of abuse. AWS adds its limits and friction for a reason, not because they enjoy making life harder (esp. since, easier for customers == more business/revenue).

I get your point about small companies occupying a niche as their distinct competitive advantage, but in this case, one has to ask the obvious question: why doesn't Lambda Labs have a problem with fraudulent crypto mining, if the bar for entry is so low and it's so much easier to be up and running in minutes?

And whatever the answer is, one hopes that there is some particular reason why Lambda Labs doesn't think AWS could replicate it... otherwise, it wouldn't be a durable enough moat to defend their business model long-term.

Two counterpoints I'd raise, in case they can help broaden your perspective:

- Industry wide layoffs != Mistake. By analogy, think about naturally occurring forest fires. Vast majority of ecologists would agree that such fires are healthy and necessary for the long term health of forest biomes. It's not a "mistake" for the trees or brush to become dense when energy or water is plentiful, and it's not wrong for a fire to destroy much of existing forest (allowing new and different growth in the future). So it is with business cycles and investment hypotheses.

- There are many accidental products or inventions, that proved more popular and durable than anyone could reasonably expect. Have you heard the story of how champagne was invented? Dom Perignon (yes the actual historical figure) was a French monk in the 17th century, tasked by the Catholic Church to solve the problem of wine fermentation that resulted in too much carbonation in the Champagne region, since the local climate was such that winter was too cold and the fermentation process was naturally interrupted by winter there in such a way as to introduce unwanted carbon dioxide. Wine with carbonation was considered undesirable, but Dom Perignon tweaked the process (inventing the "French method") in such a way as to invent a drink that consumers surprisingly found preferable (remember, nobody originally wanted carbonated wine at the time). The point of this story is that, there's no way to truly know what becomes a lasting trend or a change in consumer tastes; maybe people really would continue to shop and consume media online at higher rates even post-pandemic because they truly changed their tastes and liked it more, after being forced to do it by pandemic? How could anyone know that carbonated wine would stick around, when nobody had ever shown any interest in it before?

But not all types of music are equally in demand. If consumer tastes change, it would obviously make sense to layoff musicians trained to make music for a genre that nobody wants to buy anymore.

It's interesting to compare/contrast that, with thoughts like these: https://www.lesswrong.com/posts/CKpByWmsZ8WmpHtYa/competent-...

I think both authors can be correct, and both sets of experiences are valid. But I think it's a sign that politics selects for political-competency, which has nearly nothing to do with general intelligence or other forms of competency/expertise.

No one fills that niche because finishing out shows that were otherwise cancelled, is unlikely to be a growth market. At best, you attract a dwindling set of diehard fans, but the reason the show was cancelled is probably because the audience for it wasn't growing, or the economics of producing the show could not be justified against the size of audience attracted.

Might there be some diamonds in the rough? Sure. The Expanse seemed to do well enough with its second chance on Prime Video after Syfy cancelled it after 3 seasons. But that seems to be an exception rather than the rule.

Not trying to pile on you individually but this is an interesting line of thought in general, so continuing the thread here...

Let's say there are 10 nearby cities with drug addicts. Let's say 3 of them start running needle exchanges or other "drug friendly" policies.

By this logic, if those policies don't increase drug use, but do attract users, then maybe we just made 7 cities drug-free, and 3 cities into concentrated zones where social programs as well as law enforcement can be more targeted and more effective? If so, then very likely this will be a net-positive for the 10 cities as a whole, with neutral to slightly-worse outcomes for the target neighborhoods in the 3 cities running the programs.

Many programs/policies are not operated from a macro-enough perspective, or not optimizing for macro-level outcomes, or humans just aren't capable of macro-enough greater-good decision-making. If the 10-20 homeowners living directly next to the proposed needle exchange site can veto it, that might be better for them individually, but worse as a whole for the city/state/nation/world. See exhibit: NYC and proposals for new shelter sites.

On the other hand, seems like these sorts of debates always just end up becoming examples to prove out Churchill's "democracy is the worst form of government – except for all the others that have been tried" claim.

This type of advice does work, but only for some people. I would hazard that "some" isn't even a majority of alcoholics or other kinds of addicts. So, it's an interesting perspective to share, but please don't assume that what worked for you is universally good advice for everyone.

You may as well be a patient in a drug trial, one of the let's say 30% that responded well to the drug, but afterwards you go out and claim that it's a cure for everyone... well no, it didn't work for 70% of people.

I agree with the advice to be your authentic self, and someone who's on the spectrum would probably find it very inauthentic to try and turn themselves into an overnight charmer or "life of the party" type.

That being said, I don't think abrasiveness correlates as strongly to authenticity as you seem to imply. You do not need to be obsequious or to grovel for your coworkers' approval or to follow all of their norms, but you should treat them with respect in as much as you would want them to treat you with respect.

It's also an incredibly common and lazy copout, for downright mean/dickish/asshole people to claim their abrasiveness is "just who they are" and others need to "get used to them" or "grow a thicker skin". These people are generally toxic, and make it impossible to build a healthy workplace or team culture.

I don't believe you are one of those people, and I certainly hope you don't become one.

Some truths to keep in mind:

- Every company is navigating the marketplace, and making decisions with imperfect information

- Not every decision will be perfect (or even, good)

- Not every decision-maker will be perfect (or even, good)

- Even a collection of individually smart/reasonable people, can end up collectively making pretty awful/illogical decisions

- "Good" decisions don't guarantee market success; conversely, "bad" decisions can still result in good outcomes

- Judging the quality of decisions and decision-makers based on outcomes, is an imperfect measure of the actual "quality" of those things/people

In your first example, you were presumably an entry-level engineer, but you either mistakenly took on too much burden (emotional or practical) in terms of decision-making yourself, or you misunderstood what types of expectations you should have for the actual decision-makers.

Decision-makers are allowed / expected to make such bets: "how many and which corners can we cut as a company, to get a product out to market, that clients will want to purchase, in a sensible time-frame?" This is not unusual, this happens all the time, at every single company, all around the world. The companies who do this more successfully, are the ones who find a sweet spot between cost-cutting, efficiency, time-to-market, and customer demands/satisfaction/delight. This is a very difficult thing to juggle, and really really smart business leaders consistently fail to find the right balance, or make the wrong calls. Hopefully the mistakes aren't fatal to a company, but unavoidably sometimes they will be. So yes, your company leaders made a bad call based on the outcome, but that on its own is not enough to indict the decision or the decision-makers as being fundamentally wrong.

The fact that your company made a set of decisions that ultimately led to failure, doesn't necessarily prove that they were a bad company. And to be a devil's advocate for a second, even "mislabeling standard X" might be forgivable under certain circumstances, such as launching a product with an "X pending" label, even though you didn't finish certification process for X yet, or maybe you didn't even start (but hey not starting doesn't mean it can't say "pending").

As a manager, I actually actively filter-in for what Amazon would call "Have Backbone" as a value, when interviewing engineers, and I ask them to provide examples of times where they fundamentally disagreed with the product team, disagreed with what they were asked to build, disagreed with a proposed architecture, etc. I want engineers on my team who will speak up, who are opinionated, who care enough about their work to take pride in it and put forth effort to improve beyond the status quo.

That being said, your examples seem to indicate a rigidity of black/white thinking, all-or-nothing thinking, and an inability to collaborate towards finding a solution. These were probably the most extreme examples you had, so I'm not judging every interaction or your entire personality as being so rigid, but hopefully you have by now experienced other examples in your career, where collaborative problem-solving was possible, where you did more than point out fatal flaws but also helped formulate a path to mitigate or solve them. The companies where that was more encouraged or made possible, are the ones you probably want to work for.

Set aside work/career for now. What do you do in your personal life? Have you had any experience with celebrating small (even tiny) milestones or "achievements?

Recent example: I recently moved house. Many of my books and lower-urgency work resources are still in moving boxes. I think I need a new bookshelf, so that I can unbox some stuff. I take pleasure in the act of putting together the bookshelf. It is a 30-60min task, it is trivial in the grand scheme of things, but I feel accomplished about doing this proactive step to improve my working environment. Plus I like the new shelf I picked, I like the way it looks, the design itself gives me some small amount of pleasure.

Rinse and repeat. Maybe not every day, but every week, there will be some small/tiny/insignificant victory like this. Learning to appreciate them, will not necessarily bring you lifelong joy or self-actualization on its own, but it will do wonders for improving your relationship and emotional balance with respect to work. Sometimes a big project goes wrong, everything is on fire, my boss is asking me to do some crazy unreasonable thing (but at least, a good boss doesn't ask this unnecessarily, and only very rarely), whatever... but still there are always some minor victories to celebrate, or at least partial progress:

- hey I stopped the bleeding on that production outage -- at least other teams know where to look for deeper fix

- hey I sketched out an outline for that documentation that everyone keeps asking about -- even if the content isn't there, at least people know this is where new content should go

- hey I wrote down all my tasks for tomorrow -- so even though I didn't finish things today, I can still log off and forget about them, then pick them up again in the morning

EDIT: Or, to put it another way:

- don't think about the problem as, "how do I learn to accept tedium or chores, and not get bored/frustrated/angry about them?"

- instead the problem should be, "how do I find contentment in the small details of life, that I previously overlooked as simply being tedious or boring?"

Well, this isn't prima facie unreasonable without more info (or knowledge of the implicit assumptions you are making but not publishing).

- Are cases 1,2,3 named that way, because they are the top priority cases (i.e. the #1, #2, and #3 most important product features that customers care about)? Even if they are, what is the cost of a new architecture? Will it take you 3 years and 20 engineers, to rebuild Y or to make Y.v2, just so you can support X "properly"? By then the market may have moved on, the feature may be worthless, so it may make perfect sense to deliver a bad version of X that relies on Y.

- Or, are cases 1,2,3 legitimately either rare, or low-impact, or do have viable manual workarounds? If so, then it's entirely reasonable to defer/punt on doing new architecture right now, because either you know these cases are unimportant, or at least you don't have positive proof that these cases are important enough to justify new architecture. With more data, or clear customer demand, you can make a better case for rebuilding Y "properly". The real problem comes later: what happens if you do get strong signals of customer demand, you can prove the current solution is not scalable or extensible, and yet the business still decides that Y is good enough to never touch... well that's a business that doesn't want to stay in business.

Agile is about practicality/pragmatism, over adherence to dogma or preconceived notions. Just because Y is the wrong architecture to deliver X, does not mean it is the wrong decision to ship partial feature X. Don't be dogmatic about "correct architecture", if you care about for-profit software engineering as a profession.

Of course, if your goal is different, if SWE is a craft or a hobby or an ivory tower pursuit for you, then feel free to make whatever decisions you want that don't fit your vision of "correctness".

Appreciate you sharing this anecdote. There's multiple archetypes of "successful" managers (i.e. meaning different ways of being useful, not success in terms of kudos or promotions or whatever), and the one you highlight is what I think of as the base/foundational value that a manager should provide (i.e. that one is able to create the quality of "smoothness"). Then, a truly exceptional manager, may also be a technical leader, may also contribute architecture or code, may also be a star recruiter/attractor/retainer of talent, etc. But IMO those are add-on's, and a base manager should first keep the ship on course first.

The quiet enterprise professional, preventing fires and course-correcting to avoid "random shit" or missed requirements, just very non-sexy/non-glorious work, and a pretty literal example of, "if you do your job right, then no one will notice you did anything at all."

Fanatics Betting and Gaming | Software and Data Engineers (up to Staff) | REMOTE (US/Canada) | https://jobs.lever.co/fanatics/4c2cf288-34da-48d8-b2a2-30ebf... (sample role for Staff)

Fanatics Betting and Gaming (aka FBG, or BetFanatics) is a well-capitalized new venture, within the broad umbrella of Fanatics as a holding company, that is poised to disrupt the existing sportsbook and casino/iGaming markets. Specifically I lead the Data Platform and Integration teams, and we are now looking to grow our existing core launch team, with additional mid/intermediate level engineers; our hiring strategy was to hire "from top down", so you'll be joining a team with truly excellent Principal, Senior Staff, and Staff level engineers who can guide and mentor you, while still leaving plenty of room for you to own key components, deliver autonomously, and to grow

We plan to encompass all aspects of an end-to-end data platform, including data lake, data warehousing, analytics and visualization, enterprise reporting, regulatory and privacy compliance, as well as building out personalization (P13N) and other DS/ML platforms. We also partner with other teams within Fanatics, collaborating on MarTech and general data activation, and not to mention all the other places where data is needed (Fraud/Trust, Ops, Customer Care, etc.). We don't buy into buzzwords, but we believe in many of the underlying principles and good ideas behind "modern data stack" and "data mesh architecture".

Seriously, just try out the existing options like DraftKings, FanDuel, BetMGM, etc., and see for yourself how customer-unfriendly they are, how unwelcoming the first-time user experience is (especially for any total newcomers to sports-betting), how little personalization or customization there is, and how different it is from the kind of frictionless browsing/shopping/consumption experience you'd expect with modern customer-obsessed data-driven products like Audible or Amazon or Netflix or Robinhood (e.g. one analogy: we'd like to make sports-betting as approachable as Robinhood made buying stocks). We deeply believe we can do better, and powerful foundational data platforms are a crucial ingredient.

Personally, myself and many others within the company are also deeply passionate about what the industry calls "responsible gaming", i.e. building trust with players/bettors via data-driven features to empower players to make responsible decisions (rather than dark patterns focused on squeezing players, or taking advantage of whales, or enabling addictive behavior). We definitely think we can easily "raise the bar" here, compared to industry norms focusing on meeting the bare minimum legal/regulatory standards for player protection.

- Tech stack: primarily AWS based, with common modern options thrown in like Snowflake, Tableau, dbt, Amplitude, etc.; near-term focus is on building launch-ready MVP data platform, with plans for much more sophisticated features and capabilities over time

- Comp: competitive with FAANG/Big-N tech, with strong base salary, target bonus, private RSUs (for pre-IPO Fanatics stock), and potential for sign-on cash where justified

- Some recent-ish press coverage of Fanatics as a whole: https://www.cnbc.com/2022/04/11/fanatics-aims-to-be-100-bill..., and of Fanatics Betting and Gaming in particular: https://sportshandle.com/fanatics-planning-sports-wagering-e...

Feel free to connect or message me on LinkedIn (referencing this post): https://www.linkedin.com/in/ye-wang-368923a/

I think that poster probably meant feedback loops or virtuous cycles (ones that create beneficial second and third order effects, with outsized impact vs what you might assume based solely on the first order change), more so than network effects.

I suspect you're mostly venting and having a wistful mid career crisis, as even a small amount of effort of research into this topic will lead you into a wealth of readily available resources on how to achieve "FIRE" (financial independence retire early), mixed with a dash of geographic arbitrage.

For a very basic beginner intro, you can start here: https://www.mrmoneymustache.com/2012/01/13/the-shockingly-si... ... In a nutshell, at one extreme (I do not recommend this, you will not succeed if you pursue this type of hyper frugality) if you can save 90% of your income, you'll be able to retire in about 3 years (assuming you start with 0 net worth, no major debts, etc.). All other retirement plans are just variations on speed and relative acceptable frugality.