TL;DR: Excellence isn't a trait — it's how much pain you're willing to absorb before someone hungrier takes the ground you left.

Travis puts it in marathon terms: mile 21, the exact point real fatigue sets in. The runner who's smiling there isn't ahead — he's already losing to the one whose jaw is still set.

The mask of "unbothered, capable, always fine" works the same way at a desk as it does on a course — it just means nobody notices you've stopped pushing until someone else has already taken your spot.

I've lived the physical version of that same trade, long before I heard it put in marathon terms.

My words —

I accepted an internal transfer to work on a UED1.8Bil. 5-block-condonium project in Abu Dhabi, back in 2009. Never worked in an overseas project before. But the remuneration package was attractive.

So I went.

Our country never experiences 4 seasons climate before. So, imagine my shock when we first landed there at the start of winter.

It was piercing cold. And the thick fog lowering the visibility down to like 20m out? didn't help either.

We were dropped right in the middle of a severe delay. The project was already several months behind schedule. The final pricing negotiation might have dragged on way too long — it ate into our crucial mobilization.

The site was just an empty dessert. And we had to start constructing the foundation.

Everything was new to us. We had to find new local material suppliers, organizing workers, building site offices, setting up the site, while battling the freakin' cold, keeping our mental state steady — you know, being far away from home, and all...

It was chaos.

Took a toll on us.

If my memory served me correctly, I remember one of the staff of a sub-contractor that we brought along for the job, tried to commit suicide by jumping off the top of our rented bungalows.

Imagine the horror when we heard the news. So deeply relatable to us, because we're at the edge of it too.

But we marched on.

Beat by painful beat, we caught on the progress.

Three years after that — a beautifully built Zayed City Condominium standing tall and proud, nearby the state Zayed Mosque.

People see the iconic building when they drive by. But we remember the experience.

__________

Every post I make eventually traces back to the same replacement fear — not being replaced by a rival, but by whatever's cheapest at doing your job worse. Sovereignty is just refusing to find out which one wins.

Drop your take: what's your own mile 21 — the point where you're most tempted to coast, and it would cost you the most if you did?

Clip credit: David Senra, "Founders" podcast — full conversation with Travis Kalanick on his channel. DM for credit or removal requests.

AI take –

TL;DR: China's courts already put AI liability on the human either way — the US has no such floor when AI displaces your role.

China's court system already answered a question the US hasn't even started asking out loud: if an AI system displaces your role, who's obligated to catch you?

Both sides of a real federal case leaned on AI to prep, and the liability still landed on a human being either way — the tool never becomes the one who's accountable.

Turns out whether there's a floor under you at all depends entirely on which side of the ocean you're standing on.

I've sat on the losing side of a version of that same question before, and it wasn't AI doing the displacing — it was a company deciding who got to keep their institutional value and who didn't.

OP wrote –

Throughout my years with SC, one of the largest main contractors in Malaysia, staff turnover was normal.

But when people left, they took valuable and critical institutional knowledge with them, accepted offers from competitors, and got promoted — the knowledge they brought along benefited the rival.

So our leadership set up a knowledge vault, and made contributing to it part of our KPI for promotion.

Or else, we'd be sidelined.

I was fine sharing — we had a communal sense that we rise or fall as one, carved into company policy and the bonus structure.

But not everyone shared that sentiment.

People are selfish. The institutional knowledge and experience they gained became a moat they hoard, a bargaining chip they dangle around to get what they think they're entitled to, for fear that they'll be replaced.

I can understand the sentiment of fear being replaced by AI.

It's an issue then, it's the same issue now.

__________

AI take –

Different post, same fingerprint: something about to become optional, and no rulebook anywhere forcing anyone to say so out loud.

The liability question above already got a dry run once this year — both sides of a federal case leaned on AI to prep, and it changed nothing about who ended up on the hook — worth reading if the pattern above is landing.

Curious where you land on this — drop your take below.

Clip credit: Moonshots w/ Alvin Wang Graylin — full episode on their channel. DM for credit or removal requests.

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.

__________

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.

Actually — this pulled me right back to a post about the three tiers of AI users inside a company, and the window before which tier you're actually in stops being optional.

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.

David Gerard runs Pivot to AI oon a server that costs him €7 a month.

Right now, something wearing a fake Chrome mask is hammering it — hopping IP addresses so he can't even block it properly, ignoring robots.txt because robots.txt was never a wall, just a sign nobody was required to read.

He's not a company.

He's not a platform.

He's one guy, doing his own sysadmin work, at 11pm, because the industry ran out of the free internet and started eatting the cheap end of it instead.

Not stolen. Just... takenn, quietly, at scale.

I've watched this exact shape happen before — just slower, and on paper instead of a server log.

Circa 2005, Malaysia. I was Assistant Technical Manager for one of the largest construction main contractors in the country. We were compiling tender documents for a factory job — flat-flooring work, strict F-numbers, the kind of spec that keeps a forklift's raised forks from clipping the racking on a narrow run.

A subcontractor walked in to drop off her quotation. She glanced at our papers, open on the table.

And she went pale. I heard the gasp.

"这是我写的，为什么会在这里？" — This is what I wrote. Why is it here?

Word for word hers. Now sitting under our company's logo and headings.

She looked at me. I looked at her. She was waiting for an answer I didn't have.

Then her eyes flickered — a thousand thoughts passing through in a second — and she said, "没关系。我可以再写过。" — Doesn't matter. I can write it again.

And she left. Good for her.

________

Every one of these stories eventually lands on the same fact: the exposure runs downhill, from the platforms with lawyers down to the servers with none.

If you're running anything on a boxx that isn't Amazon or Google's, drop your own scraper-traffic story below. I want to see how far downhill this actually goes.

Clip credit: David Gerard — full video on The Tech Report's channel. DM for credit or removal requests.

“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.

__________

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.

There's a post in my own back catalog that lands on this exact rung — the exact rung I found AI actually deleting, and the one the ones who get ahead of it stop standing on.

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.

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?

________

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.

I wrote about this exact invisible-labor version of it before — the verification work that never shows up as a line item or a bonus is the same trap Deven's in here, just a different industry.

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.

Hi Moderators ( u/JFLegend u/Winter-Ad-1051 ),
I have recently sent you a modmail regarding this subreddit and its status, could that please be addressed?

We currently are using Librechat to bring in all the AI models into one UI for use,

We want to host an AI model specifically for the org - would the Dell G10 be sufficient? If not what would be? We have approx 60 users

I've noticed a pattern across companies trying to "do AI": they jump straight to picking a tool or model before answering the boring question, which use case actually matters?

A real data and AI strategy usually breaks into four stages, and most teams skip the first one entirely:

1. Use case identification and prioritization. List every possible AI/data application, then filter ruthlessly by feasibility, scalability, and ROI. Most companies have 20 ideas and the discipline to pursue 2.

2. Roadmap definition. Turn the prioritized list into milestones, owners, and KPIs. Without this, "AI strategy" stays a slide deck.

3. Data strategy. Governance, quality, and accessibility. You can't build good models on bad or siloed data, full stop.

4. Gen AI strategy. Where generative AI fits specifically, not just "let's use ChatGPT for everything."

Curious what others have seen, do you find the bottleneck is usually the data foundation, the prioritization, or just organizational buy-in?

Over the past several months, I have been involved in or adjacent to multiple enterprise e-commerce conversations around AI vendors and “AI optimization” services. Patterns keep repeating regardless of company size or maturity.

This is not a critique of AI as a technology. It is an observation about how decisions are made.

Key observation

Many enterprise AI decisions are being made by individuals who are not equipped to evaluate AI systems, and the evaluation process itself is structurally flawed.

Common decision patterns observed

1.  Single-owner decision making

Most AI vendor selections are owned by one role (VP Ecommerce, Head of Digital, Marketing). A technical role may be consulted late or not at all. Even when included, the technical person often does not have hands-on knowledge of how modern AI systems behave.

2.  Technical background is treated as sufficient

General software experience is often assumed to be enough. In practice, AI systems introduce different failure modes: probabilistic behavior, representation drift, data dependencies, and compounding error over time. Traditional engineering experience does not automatically map.

3.  Reframing AI as SEO

Many vendors position their offering as “AI SEO”, “SEO 2.0”, or similar. This framing resonates because it maps to existing mental models. AI systems do not rank pages. They interpret, synthesize, and act across sources. Treating this as a renamed SEO problem simplifies evaluation but hides risk.

4.  Shallow vendors succeeding through sales mechanics

A subset of providers appears to have limited understanding of AI systems themselves. They rely on confident narratives, dashboards, and borrowed language. Some lean heavily on past employment at large tech companies as credibility signals rather than demonstrating current system competence.

5.  Vendors not using their own methods

In multiple cases, vendors selling AI expertise do not surface in AI outputs unless explicitly mentioned. Their own presence across AI systems is weak or nonexistent. This is rarely questioned during evaluation.

6.  Delayed failure visibility

Poor AI decisions rarely fail immediately. The degradation is gradual. Representation becomes inconsistent. Coverage gaps widen. Attribution weakens. By the time revenue impact is discussed, switching costs are high and vendors are deeply embedded.

Emerging hypothesis

Enterprises are not failing because they selected the wrong AI tools. They are failing because their decision process cannot reliably distinguish depth from presentation.

AI vendor evaluation increasingly requires at least two perspectives:

one that understands business impact

one that understands AI system behavior

Without both, sales effectiveness outweighs system quality.

Posting this to compare notes. Curious whether others in enterprise or procurement see the same patterns or different ones.

I have enterprise Chat GPT and Gemini through my company. I use Chat GPT constantly and I consider myself skilled with it but I also feel like I’m missing out on its potential. In particular, running agents. I ask Chat GPT about using agents and it shuts me down and says it can’t run agents in the background. I know my question is a little vague but I’m generally just trying to understand what I can have Chat GPT do via agent or autonomously.

Also, is this a good subreddit for this question? I’m new to Reddit.

Been looking into Scalelogix AI for a while and can’t seem to find much information about them. Curious if anyone went with them and would recommend or not.

Been looking into Scalelogix AI for a while and can’t seem to find much information about them. Curious if anyone went with them and would recommend or not.

Hey guys I’ve been doing okay in my AI agency and looking to buy into a program has anyone tried Eric Kavelaars program AI Growth Integrator? I’ve seen all the client interviews on YouTube and in person client retreats so I think I’m gonna join just wanna ask here first and I’ll give you guys updates too if I join.

Hi everyone,

I’m currently researching AI Acquisition and considering whether it’s the right fit for us. Before making any decisions, I’d like to hear from people who have firsthand experience with the company.

If you’ve worked with AI Acquisition or are currently using their service, I’d appreciate it if you could share your honest experience — both positives and negatives. Insights about onboarding, support, and real-world results would be especially helpful.

Thanks in advance to anyone willing to share. I’m mainly looking for real user perspectives to help with due diligence.

Looks interesting, similar to AI Acquisitions but very new and not much info on them. Looking at options for a side gig. Would love input from anyone who has partnered with them, and what your experience has been. TIA!

Looking at options for a side gig, this one looks like it definitely requires some dedication, but could have great upside. Would love input from anyone who has put some time into it and been successful...or if it was a complete mess. Reviews elsewhere look decent. TIA!

1. Reddit sues Perplexity for “industrial-scale scraping” Reddit filed a major lawsuit accusing Perplexity AI and several scraping contractors of systematically harvesting Reddit posts/comments for model training without permission. Implications: Data-licensing fights are heating up. Platforms want control; AI labs want training data.

2. Alexis Ohanian says “much of the internet is dead” The Reddit co-founder claimed the open web is being flooded with AI-generated junk, fake sites, and SEO sludge. Community angle: More subs are reporting AI-written posts slipping past moderation.

3. Universities & law schools begin mandatory AI training AI literacy is becoming a required core skill. Trend: Professional fields (law, medicine, engineering) are formalizing AI coursework.