r/BusinessDeconstructed

Proof: Theres a website where you can upload a read only stripe key, heres mine

The build for this was actually a ton of fun - its an all in one cold email outreach tool. You use AI to filter my hundreds of thousands of leads and then AI generates and starts sending a cold email sequence.

For the lead gen im basically basically mapping LLC filings to contact information I find on the internet, trying to find websites, getting domain registration information etc. At the end the leads are pretty solid and so mapping someones ICP to a few thousand leads becomes pretty easy. It's not cheap to run at this scale but i'm fortunate to have a pretty high paying tech job to fund it for a while.

Im building this solo so it gets lonely sometimes. Basically building and talking to customers every waking hour I have of free time but the $1000 threshold is a little more fuel on the fire.

I love building but dont have a ton of people to share my wins with so posting here to share instead :)

Let me know if any builders out there want to connect I love chatting about SaaS startups and respect the hell out of people seriously trying to create and build.

Everyone's reacting to the "less people in 5 years" line. That's not the part I'd sit with.

The part that actually matters is how Uber decides — an adoption leaderboard, tracking who's using the tools and how much, feeding straight into headcount math.

That's not a hypothetical for some future reorg.

That's a live measurement system, running today, on people who have no idea they're on it.

 

I've watched that exact math play out before — in concrete and steel, not a dashboard, years before anyone called it AI.

I had the opportunity to be involved in the early design stage of an expansion project for a famous beverage manufacturing plant in Taoyuan, Taiwan – back in 2021. The beverage brand name is so famous, you'll instantly recognize it. So, I won't name it here.

Our team got to work on cool stuff - latest advanced technologies in high-density and automated racking system, bottle conveyor system, robotics, beverage packers, clean room environment, etc. – things that are expected in a high-tech. manufacturing plant nowadays.

Looking at the projected 10-year production forecast, with the given magnitude of the hardware, I would say they are planning to go big.

It's quite a sizeable expansion.

And you would think that they'd increase their headcount proportionately, right?

You'd be surprised. There IS headcount increase, but not as proportional.

It seems as though the machines were taking more centre stage than the humans. Even the office space increase wasn't even a top priority in the design. Their existing office layout can still accommodate the projected increase in manpower.

It was as if human beings are being set aside to make room for more artificial things – even though what they produce are meant to serve human beings.

Kind of ironic, isn't it?

That was back in 2021 before AI come into the picture. Now the compression is even more acute, it seems.

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Different guest, same fork in the road: does the tool serve you, or does it just get pointed at you.

The industries change.

The question underneath never does — who's holding the ledger, and whether you're the one reading it or the one being read.

A 2026 WRITER survey backs the pattern from the outside too: 75% of execs privately admit their AI rollout is mostly for show, while the people actually inside the tooling get promoted 3x more often and ship 5x more.

That's the leaderboard, confirmed from a different angle.

 

Genuinely curious where you land: is a visible adoption leaderboard a fair way to measure a team, or is it just a slower-motion version of the same cut?

 

Clip credit: 20VC with Harry Stebbings & Uber. DM for credit or removal requests.

 

The leaderboard measures usage. It doesn't measure ownership — and the difference between those two is the actual thing I built the working system around.

Executives don't lack tools.

They lack a ruler.

 

That's the actual claim Ed Zitron made — not "AI is bad," but that the people signing off on AI-assisted work were never equipped to check it in the first place.

They see a document that looks finished and call it done, because "finished-looking" is the only bar they've ever had to clear.

 

For anyone whose whole job is catching the thing that looks fine and isn't — this isn't a tech story.

It's a story about who gets trusted, and why it's rarely the person who's actually right.

 

I've been on the other side of that exact gap.

Long before spreadsheets and dashboards, mine had a tape measure in it.

 

I was working as a Site Engineer for a Singaporean construction company building a primary school in Chua Chu Kang district back in 1998. Time flies. Just graduated from university. Figure I get some site experience first.

One day, I came to the project site. And I saw the newly delivered precast half-flight staircase lying on the ground next the building. I asked around to find out why wasn't it crane-lifted to position, which is between 1st and 2nd floor. And I was told the measurements were off. They couldn't fit it nicely on place.

And so, I went to work. I took my measuring tape, measure the staircase, and recorded the lengths, widths and whatnot. Then I went up to the building's 2nd floor — where the staircase was supposed to fit and meet. And I swung my measuring tape across the length of space between the positions where the 1st and last step of the staircase supposed to sit on. And took the site measurements too.

Then I went back to my office, took out the construction drawings from the drawing rack, lay it on the meeting table. And with a piece of paper, I started drawing it out. I knew full well the measurements I got will not exactly match that in the drawings, because — you know — site tolerances are still allowed and anticipated in the BS Code of Practice.

And then, through calculations, I found it. The measurements were way out of tolerance limit. No wonder the staircase can't fit. The blame squarely landed on our RC works sub-contractor. They screw up the levelling of the building.

Each of us supposed to have an "internal ruler" we rely on, to judge whether things look good or bad. For me, back then, it was Pythagoras and a fresh sheet of paper. My boss, years later, called his the same thing in different words — his "feel," thirty years deep. Kevin O'Leary's is knowing he can smell bullshit from a mile away.

So, coming back to these leadership people that Ed Zitron was attacking: don't they have their "feel" of things before shit hits the fan? Don't they use their "internal ruler" to measure it for themselves? Can't they smell bullshit from a mile away?

https://preview.redd.it/1j7kqeub7yjh1.jpg?width=1024&format=pjpg&auto=webp&s=7c3ef0563fe287471d2b68fbd35d2e35806943c4

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Every post on this account keeps circling back to the same thing, whichever industry the clip's from: the people getting quietly pushed out are rarely the ones who got it wrong.

 

Drop your take: what's your internal ruler, and who around you doesn't have one?

 

Clip credit: Ed Zitron (Better Offline) on Adam Taggart's Thoughtful Money. DM for credit or removal requests.

 

If your ruler's ever been right and still overlooked, it's worth seeing what building past that actually looks like.

 

“你在帮人倒米吗？“

Lauren Tan didn't get replaced by her own tooling.

She got promoted by it — and nobody handed her that promotion.

She built the case for it herself, one lint rule and one CI gate at a time, until the argument was undeniable.

That's the part nobody's really talking about when they talk about AI and engineering jobs: the shift rewards the people who go looking for the leverage first, not the people who wait to be told it's safe to look.

 

That "build the case yourself" instinct is exactly what clicked for me watching my own son learn to run a team instead of carry it.

My son started playing 王者荣耀 (Honor of Kings) since he was a teenager — a 5v5 multiplayer battle arena game where you manage a roster of specialized heroes, growing and levelling up their strengths through battles and gear.

In his early gaming days I could hear him cursing and swearing from his room — bad coordination, worst teammates. There was a phrase we used for a bad teammate in my own career — 帮人倒米*, a Cantonese idiom that literally translates as helping someone tip over their own grain container, meaning ruining or sabotaging someone's livelihood.*

But the cursing became less and less. He got good at managing his heroes and coordinating with his team. He started climbing the leaderboard. People started noticing him and his team. Then, in college, he started getting invited to tournaments — cash prizes when he won, and one lagged-connection loss at a KL tournament he still suspects was foul play.

Time has changed — my dad would've killed me for wasting my teenage years on video games.

Now he's in university, still playing, still winning tournaments and cash prizes with his team.

Why I'm bringing this up: I always thought these AI agents are kind of like the heroes my son uses in the game. Your skill is in your managing these heros and how to grow them, level them up to serve your purpose. You don't go down to the battle yourself. You engage the heros to do it for you.

The skill is in the managing.

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I keep walking into the same room wearing a different name on the door — the accountant's room, the analyst's room, now the engineer's.

Every time, someone's being told the machine is coming for their hours, not their name on the work.

 

Drop your take — are you already the head chef of your own stack, or are you still doing all the cooking yourself?

 

Clip credit: MTS (Monitor The Situation) — full video on their channel. DM for credit or removal requests.

 

If encoding your own judgment into the system sounds like the actual leverage skill here, the mechanism I built around exactly this is one link away.

Bloomberg's own reporting already answers the question this clip raises — is the "messy middle" projected or already happening?

At Commonwealth Bank of Australia, Microsoft, Uber, and Hyatt, it's already happened: sizable call-center headcount cut using automated phone and chat systems, savings already banked.

Economist Molly Kinder's point isn't a forecast.

It's a line item that's already closed.

 

I've watched this exact math play out before superior technology ever touched a keyboard.

Suncon was getting jobs overseas. One of the countries we went to was India. We were building infrastructure — roads and bridges there. I didn't go. But my seniors went stationed there. When they came back during their scheduled holidays, one of them, a project manager, told me this story.

It so happened, that building roads and bridges inland means clearing jungles and passing through villages. As they were doing it, of course they engaged local villagers to be their workers and supervisors. Well, of course building infrastructure means bringing in heavy machineries, such as excavators, bobcats, mobile-cranes, 4-wheel-drive land-cruisers, etc. You know — the usual.

But the local villagers weren't happy. They complained that all these machineries have deprived the local population of their means of making a living. They have so many mouths to feed. A lot of them are quite poor. And many of them are very hunger for work.

And so a huge argument broke out. They even spitefully challenged our project team that they vast manpower was more superior than our machinery. I was so surprised when I heard it. How can they say that? How was that even a reasonable challenge, you know. My curiosity had the best of me.

Well, the project manager came up with an idea. He said, since they're so confident of their manpower, why don't we have a competition. Let's do a challenge of moving earth from point A to point B for our excavator/mobile-crane operator versus their vast manpower.

And they accepted.

At the day of challenge, the project manager set up two huge piles of earth at point A for both teams. The local villagers' team had their "vast" manpower formed a long-ass line between A and B, and started moving earth, with their primitive buckets and whatnot.

For our team, we set up our mobile-cranes, excavators and bobcats on strategic locations. And off we go.

You can guess the result. We won by a large margin. We were obviously much faster and better at it.

After that, the local villagers concede defeat.

The math is actually quite similar here. The one with superior technology always wins. This AI-take over is no different.

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Every time I dig into one of these stories the shape repeats: the tool doesn't ask permission, it just wins the argument by moving faster than the objection can be raised.

 

Ever watched something you thought was irreplaceable lose, and lose fast? Drop your take below.

 

Clip credit: Center for Humane Technology — full video on their channel. DM for credit or removal requests.

 

If you want to see how I'm actually building leverage against this instead of just watching it happen, it's just one link away

This is my second account and the funny thing is I’m not even really focused on this one right now. I mostly just let it run while I focus on other things. When I first started, I thought finding products was going to be the hardest part. Turns out, once you understand what to look for, that’s probably the easier part. The small details are what actually make a difference.

I started paying more attention to products that were already selling instead of trying to guess what might become a winner. I’d look at Amazon, check what was selling, search the same products on eBay, and look at sellers who were already getting sales. Seeing actual sold counts made it a lot easier to understand what people were willing to pay. From there it was mostly about getting the listing right, making sure the margins made sense, and being slightly more competitive on price.

One thing I learned pretty quickly is that making the sale is only half of it. Keeping the account healthy matters just as much. Responding to buyers quickly, avoiding unnecessary cancellations, dealing with issues before they become disputes, keeping inventory updated, all the boring stuff nobody really talks about. I also started experimenting with markdowns and sending offers to people watching my listings. None of these things felt like they would make a huge difference individually, but over time they added up.

Eventually I had thousands of listings across my accounts. I built routines around inventory, customer service, returns, and sourcing so I wasn't manually doing everything every day. That's really what made it possible to keep this going while working a full-time job. I’ve had months where the numbers were much higher, and obviously $2.2k isn't some crazy life-changing amount. But seeing my second account make that much in a month when I’m barely focused on it is pretty encouraging.

I think that's the part I like about this. It wasn't some overnight success or one random viral product. It was just figuring out what works, building systems around it, and sticking with it long enough for the small things to start adding up. Lmk if you have some questions, happy to answer them!

P.S. If anyone wants to go deeper into eBay dropshipping, I’ve got a Discord where I share my experience, what’s been working for me and things I’ve learned along the way. You guys can check this out: ebay roadmap

Every one of these clips lands the same blow eventually: a role someone spent years building gets quietly outperformed by a system that never clocks out.

 

Kavak sells used cars across Latin America — a genuinely messy transaction: ~20,000 SKUs to choose from, then financing, insurance, and a trade-in valuation stacked on top.

Historically, closing one sale meant routing a customer through 15 separate human specialists across 15 different teams, each holding one piece of the process.

 

Alejandro Maza Ayala, Kavak's Chief Product & AI Officer, explained on a16z's show how they fixed it — not by making a support bot, but by building a single "mega-expert" agent that holds all 15 specialties at once (financing, insurance, trade-in, advisory) and puts that one agent in front of the customer.

 

The result: 2.1x the conversion rate of their own human sales team, tripled customer satisfaction.

The agent never tires, never forgets a customer's history, and when it makes a mistake, the correction propagates to the other 200,000 agents in the fleet by the next morning — a scale of self-correction no individual human career can match working alone.

 

It closes on Alejandro flatly stating that the industry assumption — "customers aren't going to want to buy expensive things from AI" — is wrong, and Kavak's numbers are the proof.

 

When I read the transcript, it felt so eerily similar to the Borg Collective Mind in Star Trek.

That's the ultimate evolution.

The question we need to ask is, will it serve us, or subjugate us?

 

If your role is the coordination layer between departments — the person routing a customer between financing, insurance, and everyone else — that's precisely the layer this consolidates first.

Worth sitting with, not scrolling past.

 

Clip credit: a16z — full video on their channel. DM for credit or removal requests.

 

Drop your take below.

 

The actual build-vs-be-built-around decision doesn't happen in the comments section — it's just one link away.

A panel of AI researchers and founders — Peter Diamandis, Alex Wissner-Gross, Emad Mostaque — just sat with a number that's hard to argue with: $2,000 in compute, and ten decade-old, previously-unsolved math problems came back with machine-checkable proofs.

 

Not "AI is getting better at math" in the abstract.

A Fields Medalist said he'd recommend one of the proofs for publication without hesitation.

A cosmologist called it "a dark night for mathematics" — "the old gods are being slaughtered by the new machine gods."

 

Then Emad closed it flat: "It's a bad time to be a pure mathematician."

 

Here's what they're not saying yet.

>Back in 2013/2014, I was with M+W High Tech Projects, on a design-and-build project in Kulim, Kedah, Malaysia. Our M&E engineer wanted an opening cut straight through the middle of a reinforced concrete beam — right where the bending moment peaks. I caught him before he did it. Told him no. That's beyond madness — you don't sacrifice a beam's structural integrity for an M&E opening. Had him redirect the ducts instead. Structural safety came first.

The engineering knowledge wasn't rare.

The judgment — catching the mistake before it became permanent — was.

 

Same pattern here. Ten unsolved proofs, correct on paper, for $2,000. The correctness was never the scarce part.

 

Hmm — this actually pulls the same thread as a post I put up about the corporate ladder losing its entry-level rungs to AI.

Different profession, same mechanism: whichever rung gets automated first isn't random, and the people still standing on it are the ones who saw it as a pattern instead of a headline.

 

Drop your take — is judgment actually the thing that survives this, or is that just the story we tell ourselves until it's our turn?

 

Anchor your perception so you can profit 10x more — it's just one calculation away from where the real leverage sits once execution gets this cheap.

Position beats genius more often than anyone in this space wants to admit.

Every time I trace how these AI bets actually get funded, it's the same mechanism repeating.

 

Actually, this reminded me of a post I did a while back — a fund manager naming the real signal for buying the bottom, and it wasn't a chart either.

 

Rob Toews (partner at Radical Ventures) says his fund meets nearly every "Neolab" that gets funded — brand-new companies with no product, no roadmap, sometimes not even a clear technical direction.

Just an accomplished founder saying "I'm from OpenAI/Anthropic/Meta, so I want to raise a billion dollars."

They pass on almost all of them.

 

The exception was Anthropic.

Spun out of OpenAI five years ago.

Investors at the time called the entry valuation insane.

It's now worth a trillion dollars.

Toews' own framing: "there will be another Anthropic" — the mechanism isn't a one-off, it's a filter that occasionally clears.

 

I've watched someone spot a bubble this early before. Not in AI — in property. This isn't my story, it belongs to a friend.

>I'll call him Chew — I think that's his surname, it's been a long time. We went to the same university, graduated the same year, both went into construction in Malaysia. He switched upstream to a property developer — a subsidiary of a mainland China parent company — and eventually relocated there for the better part of a decade, right as the property market was in its super-expansion phase. The bubble kept ballooning without ever showing a crack. Chew saw the opportunity, and lock in his purchase of one of the units. The price — he told me — rose 10 fold over the years. Then, like the rest of the shrewd investors, he saw the writing on the wall. He liquidated his holdings and made a huge windfall, right before the bubble burst.

 

Clip credit: The Information — full video on their channel.

DM for credit or removal requests.

 

Drop your take below — has anyone here ever watched someone else make that call before you did?

 

If you want to know how this mechanism works for me, the actual math behind reading a bet like this before it's obvious is one link away.

The job title survives longer than almost anyone attached to it.

That's the part nobody puts in the internal memo when they call a role "AI-assisted."

 

Scott Galloway put a real number on it, talking to Steven Bartlett on The Diary Of A CEO.

He says he'll cut legal fees by a third this year — not because the law changed, but because a prompt now does the $400–$2,000 contract review a name-brand firm used to bill him for, at a fraction of the junior associate markup.

 

Bartlett went further with his own fund.

They planned to hire five analysts.

They hired one — Molly.

Two agents, two Mac Minis, and she screens inbound deals, scores them against a framework, and preps them for the investment committee herself.

Five jobs, one person, same org chart line.

 

Same ratio on executive assistants: ten planned, three hired.

One runs travel, one runs scheduling, one meets people at the door.

 

I've watched this exact pattern before, minus the AI.

I was a Technical Manager for a China Construction company here in Malaysia.

I contributed a lot into their technical and tendering work — helped build up a real chunk of their documentation and tendering process.

But about six months in, I'd exhausted all my know-how for them, I guess.

Then the announcement came at the end of my year there.

My contract wasn't renewed.

I was just let go, just like that.

I remember what Deng Xiaoping said: "无论白猫，或者黑猫，会抓老鼠的就是好猫" — black cat, white cat, doesn't matter, so long as it catches mice.

I guess they think I'd outlived my usefulness.

Can't catch mice anymore.

 

That's the mechanism underneath "AI-assisted" that nobody names out loud.

It's not that the work got automated.

It's that the one person left is now doing what used to justify five headcounts, and the fifth person's job title is the only part of the org that didn't change.

 

Actually, this reminded me of something — a former SpaceX CIO cut a 175-person engineering team down to 6 using the same compression math, and the ratio held there too.

 

Drop your take — did you know your own job has a ratio like this attached to it?

 

If you want to know how this mechanism works for me, it's the same math running under my own setup — it's just one link away.

 

Clip credit: Global Talks — full video on their channel.

DM for credit or removal requests.

There's a specific kind of quiet that happens right before a margin call.

I don't think enough people talk about that part — not the number, the quiet.

 

Leopold Aschenbrenner's AI-focused fund went from zero to $45 billion, then down to $10 billion.

Not all of it was margin, but enough of it was, and it was concentrated — five correlated AI-infrastructure-adjacent positions (SMCI, SanDisk, Micron, CoreWeave among them) dropping together in the same month, ~35% in one stretch.

That's the mechanism people skip past: it wasn't one bad bet, it was one bet wearing five different tickers.

 

He was also short Adobe. Adobe went up.

Being partially right doesn't save you — a hedge only protects what it's actually sized to protect, and this is the part that got him: three prime brokers (per CNBC's reporting) started calling at once.

Not one call. Three, simultaneously.

That's the system's own alarm bell going off.

 

Here's mine, since we're talking about leverage used well versus leverage that uses you:

>Years ago I was on the tender team for a major Malaysian construction job, competing against the two other largest contractors in the country. My GM, Mr. Chung, spotted the one contractual gap the client's own consultants hadn't caught. When they tried to pump him for the fix for free, he didn't fall for it — he told them straight: we can help you solve it, cheaply, with minimum delay, but we have to get the contract first, before we can go in and assess it. That's not a bluff. That's understanding your own leverage well enough to use it on your own terms, not the other side's. We won the contract months later. Leopold never got that chance — the market decided his terms for him, not the other way around. He still walked away with $300 million, which is the part nobody in the clip stops to sit with. That's not the actual takeaway, though. The takeaway is knowing exactly how much leverage you're carrying, on purpose, before someone else decides it for you.

 

Ken Griffin (Citadel) stepped in as the liquidity provider — what Tom, PBD's co-host, called a "white knight" on the show.

Half the room called that predatory.

Half called it structural necessity.

Both readings are true at once, and that's the actual lesson:

>Understanding why rescuers exist structurally is a different literacy than just watching one show up.

 

Clip credit: PBD Podcast — full video on their channel. DM for credit or removal requests.

 

Drop your take: predator, or necessary?

Genuinely curious where this sub lands on it.

 

If you want the actual mechanics of how leverage gets used for you instead of against you — the kind of structural read Mr. Chung had — it's just one link away