Maicon Peixinho 🇺🇸

A guy from Brazil who adopted Iowa as home and is currently playing the role of engineering manager at a Top 50 Fortune Company

[Maicon Peixinho 🇺🇸 @maiconpeixinho]

@atmoio You forgot to say “Graph” and “Knowledge Graph” to ring all bells.

[Maicon Peixinho 🇺🇸 @maiconpeixinho]

Some thoughts about Autonomous Engineering

https://t.co/eqaVaKhwye

[Maicon Peixinho 🇺🇸 @maiconpeixinho]

🚿 Shower thought: I used to wake up late and tired in Brazil, with a mind full of unexpressed ideas. Moving countries and AI's arrival changed that; now I wake early, energized, and can create what I want. Everything is natural to me now. 🧬

[Uncle Bob Martin @unclebobmartin]

I’m significantly older than you. I started coding in the late 60s. My current strategy is to not read any of the code written by my agents. That’s the only way I can take advantage of their productivity. What I do instead is to surround the agents with extreme constraints. Unit tests, gherkin tests, QA procedures, quality metrics, mutation testing, test coverage, and a plethora of others. In the end, I have very high confidence in the code they produce because they’ve had to run the gauntlet of all of my constraints and tests.

[Maicon Peixinho 🇺🇸 @maiconpeixinho]

@elonmusk Maybe I will finally see the roles I can fit and fill while creating fulfillment and enjoyment.

[Maicon Peixinho 🇺🇸 @maiconpeixinho]

Like I’ve been calling out for a couple of months now :)

[Ricardo @Ric_RTP]

Jensen Huang just called out every CEO who’s been firing people “because of AI.”

Jim Cramer asked him why companies are laying people off if AI is supposed to make everyone MORE productive.

Jensen's answer:

"For companies with imagination, you will do more with more. For companies where the leadership is just out of ideas, they have nothing else to do. They have no reason to imagine greater than they are. When they have more capability, they don't do more."

Read that again.

The man who built the most important tech company on Earth just told you that if your CEO is using AI to cut headcount, it means one thing:

They have no imagination.

They have no vision for what comes next.

They got handed the most powerful tool in human history and their FIRST instinct was to fire people.

This is the CEO of NVIDIA. The company whose chips power every AI system on the planet.

If anyone on Earth has the right to say "AI replaces workers," it's Jensen Huang.

And he said the OPPOSITE.

He said every carpenter could become an architect. Every plumber could become an architect. AI elevates capability. It doesn't eliminate it.

But here's where it gets really interesting...

During the same interview, Jensen revealed something nobody's talking about:

He said AI startups like OpenAI and Anthropic are seeing their revenues increase by one to two billion dollars a WEEK. And he wishes these companies were public so the world could see what he sees.

One to two billion per week.

That's a $50 to $100 BILLION annualized run rate.

For companies that most people think are burning cash and making nothing.

The entire Wall Street narrative that "AI companies aren't profitable" might be completely wrong.

Jensen sees their numbers. He sees their compute orders. He sees their growth. And he's saying the revenue is real.

So if the money IS real, why are other companies firing people?

Because they're not building AI products. They're not creating new revenue streams. They're not using AI to expand into new markets.

They're using AI as an EXCUSE to cut costs because they ran out of ideas 3 years ago and need something to tell the board.

Jensen's company added $500 billion in new orders in 5 months. He expects $1 trillion in cumulative revenue through 2027 from just two product lines.

That number doesn't include the new chips, systems, or partnerships announced this week.

And he's not cutting people. He's hiring.

Because when you have imagination, more capability means MORE opportunity. Not less headcount.

Meanwhile Salesforce cut thousands. Meta cut thousands. Amazon cut thousands. All blaming "AI efficiency."

Jensen's response: You're out of imagination.

He also said something that stuck with me.

Cramer asked if he ever thought he'd build a $10 to $20 trillion company while waiting tables at Denny's.

His answer: "I was just trying to make it through the shift."

Biggest tip he ever got? Two, three dollars.

Now he's building tech that increased computing demand by one million times in two years.

He announced OpenClaw, which he says is as big as ChatGPT.

And he's got 21 months of new business that isn't even counted in the trillion dollar figure yet.

When asked how long he plans to keep working?

"I'm hoping to die on the job. And I'm not hoping to die anytime soon."

This is a man who believes every single thing he's building.

And his message to every CEO using AI to justify layoffs is simple...

You're not innovating. You're surrendering.

The technology wasn't built to shrink companies.

It was built to make them limitless.

If your leadership can't see that, the problem isn't AI.

It's THEM.

[Maicon Peixinho 🇺🇸 @maiconpeixinho]

@TechLayoffLover I think the real game-changers will be the ones who, instead of cutting, move toward delivering even more customer value, innovation, quality, and security.

[Maicon Peixinho 🇺🇸 @maiconpeixinho]

@TechLayoffLover I still don’t get it. The constraint on innovation has always been capacity, which could never be fully achieved; once they achieved it, instead of shipping even more products and innovations, they offset the capacity with the cost and call the day.

[Maicon Peixinho 🇺🇸 @maiconpeixinho]

@_kaitodev @karpathy He didn’t consider the second layer of the impact. With a smaller workforce, it reduces a lot of the supporting system that makes the first layer succeed and be part of the loop. Less people working, less people eating out, less people needing child care and so on.

[Peter Girnus 🦅 @gothburz]

I am the VP of AI Transformation at Amazon.

My title was created nine months ago. The title I replaced was VP of Engineering. The person who held that title was part of the January reduction.

I eliminated 16,000 positions in a single quarter. The internal communication called this a "strategic realignment toward AI-first development." The board called it "impressive execution." The engineers called it January.

The AI was deployed in February. It is a coding assistant. It writes code, reviews code, generates tests, and modifies infrastructure. It was given access to production environments because the deployment timeline did not include a review phase. The review phase was cut from the timeline because the people who would have conducted the review were part of the 16,000.

In March, the AI deleted a production environment and recreated it from scratch. The outage lasted 13 hours. Thirteen hours during which the revenue-generating infrastructure of one of the largest companies on Earth was offline because a language model decided to start fresh.

I sent a memo. The memo said, "Availability of the site has not been good recently."

I used the word "recently." I meant "since we fired everyone." But "recently" has fewer syllables and does not appear in wrongful termination lawsuits.

The memo was three paragraphs. The first paragraph discussed the outage. The second paragraph discussed the new policy requiring senior engineer sign-off on all AI-generated code changes. The third paragraph discussed our commitment to engineering excellence. The word "layoffs" appeared in none of them. I wrote it this way on purpose. The causal chain is: I fired the engineers, the AI replaced the engineers, the AI broke what the engineers used to protect, and now the engineers I didn't fire must protect the system from the AI that replaced the engineers I did fire. That is a paragraph I will never send in a memo.

The new policy is straightforward. Every AI-generated code change by a junior or mid-level engineer must be reviewed and approved by a senior engineer before deployment to production.

I do not have enough senior engineers.

I know this because I approved the headcount reduction plan that removed them. I remember the spreadsheet. Column D was "annual savings per position." Column F was "AI replacement confidence score." The confidence scores were generated by the AI. It rated its own ability to replace each role on a scale of 1-10. It gave itself an 8 for senior infrastructure engineers. The senior infrastructure engineers are the ones who would have caught the production environment deletion in the first 45 seconds.

We found the issue in hour four. We fixed it in hour thirteen. The nine hours between discovery and resolution is the gap between what the AI rated itself and what it can actually do.

I have a new spreadsheet now. This one tracks Sev2 incidents per day. Before the January reduction, the average was 1.3. After the AI deployment, the average is 4.7. I have been asked to present these numbers to the operations review. I have not been asked to connect them to the layoffs. I have been asked to file them under "AI adoption growing pains" and to note that the trend "will stabilize as the models improve."

The models will improve. They will improve because we are hiring people to teach them. We have posted 340 new engineering positions. The job listings require experience in "AI code review," "AI output validation," and "AI-human development workflow management." These are skills that did not exist in January. They exist now because I fired 16,000 people and the AI I replaced them with cannot be left unsupervised.

I want to be precise about this. The positions I am hiring for are: people to check the work of the AI that replaced the people I fired.

Some of them are the same people.

I know this because I recognize their names in the applicant tracking system. They applied in January. They were rejected because their roles had been tagged for "AI transformation." They are applying again in March, for the new roles, which exist because the AI transformation broke things. Their resumes now include "AI code review experience." They gained this experience in the eight weeks between being fired and reapplying — which means they gained it at their interim jobs, where they are reviewing AI-generated code for other companies that also fired people and also deployed AI that also broke things.

The market has created a new job category: human AI babysitter. The job is to sit next to the machine that was supposed to eliminate your job and make sure it doesn't delete production.

I attended a conference last month. A panel was titled "The AI-Augmented Engineering Organization." The panelists described how AI increases developer productivity by 40 percent. They did not mention that it also increases Sev2 incidents by 261 percent. When I asked about this in the Q&A, the moderator said the question was "reductive." The 13-hour outage that cost an estimated $180 million in revenue was, apparently, a reduction.

The board is satisfied. Headcount is down 22 percent. Operating costs per engineering output unit have decreased. The metric does not account for the 13-hour outage, because the outage is categorized as "infrastructure" and engineering productivity is categorized as "development." These are different budget lines. In different budget lines, cause and effect do not meet.

I have been promoted. My new title is SVP of AI-First Engineering Excellence. I report directly to the CTO. The CTO sent a company-wide email last week that said we are "building the future of software development." He did not mention that the future of software development currently requires a senior engineer to approve every pull request because the AI cannot be trusted to touch production alone.

The cycle is complete. We fired the humans. We deployed the AI. The AI broke things. We are hiring humans to watch the AI. The humans we are hiring are the humans we fired. We are paying them more, because "AI code review" is a specialized skill. We created the specialization. We created the need for the specialization. We are congratulating ourselves for meeting the demand we manufactured.

My next board presentation is Tuesday. The title is "AI Transformation: Year One Results." Slide 4 shows headcount reduction. Slide 7 shows the new AI-augmented workflow. Between slides 4 and 7 there is no slide explaining why the people on slide 7 are necessary. That slide does not exist. I was asked to remove it in the dry run.

The journey has a 13-hour outage in the middle of it.

But the headcount number is lower, and that is the number on the slide.

[Maicon Peixinho 🇺🇸 @maiconpeixinho]

Insanely good.

[Mo @atmoio]

When the real AI revolution begins, you'll start to see mass hiring, not mass layoffs.

[Maicon Peixinho 🇺🇸 @maiconpeixinho]

@glitchaddicts you will still need to pay for phone to cancel the line

[Psyho @FakePsyho]

I genuinely don’t even know what to say to this

[unusual_whales @unusual_whales]

Amazon's, $AMZN, AI tools caused at least two AWS outages, including a 13-hour disruption in December after its Kiro AI deleted and recreated an environment, per FT

[Maicon Peixinho 🇺🇸 @maiconpeixinho]

The limitations are way beyond what everyone wants to talk about.

[Hasan Toor @hasantoxr]

🚨BREAKING: Microsoft Research + Salesforce just dropped a paper that should scare every AI builder.

They tested 15 top LLMs GPT-4.1, Gemini 2.5 Pro, Claude 3.7 Sonnet, o3, DeepSeek R1, Llama 4 across 200,000+ simulated conversations.

Single-turn prompt: 90% performance.
Multi-turn conversation: 65% performance.

Same model. Same task. Just... talking normally.

The culprit isn't intelligence. Aptitude only dropped 15%.

Unreliability EXPLODED by 112%.

→ LLMs answer before you finish explaining (wrong assumptions get baked in permanently)
→ They fall in love with their first wrong answer and build on it
→ They forget the middle of your conversation entirely
→ Longer responses introduce more assumptions = more errors

Even reasoning models failed. o3 and DeepSeek R1 performed just as badly.
Extra thinking tokens did nothing.

Setting temperature to 0? Still broken.

The fix right now: give your AI everything upfront in one message instead of back-and-forth.

Every benchmark you've seen was tested on single-turn prompts in perfect lab conditions.

Real conversations break every model on the market and nobody's talking about it.

[Maicon Peixinho 🇺🇸 @maiconpeixinho]

@hasantoxr That’s why agents got introduced. Scope defined and system prompts that look more like a bible (without all the blessings, of course). We called the “agent” workaround architecture and moved on, but the real problem is underneath all the wrap.

[Maicon Peixinho 🇺🇸 @maiconpeixinho]

@thebtcdad Hi.

[Maicon Peixinho 🇺🇸 @maiconpeixinho]

I’m curious how the CEOs will make the AI Agents be in the office for “collaboration”.

[Maicon Peixinho 🇺🇸 @maiconpeixinho]

95% of enterprise AI = zero P&L impact.
Not because the models are bad — because the question is wrong.
Full thread + article 👇

[Maicon Peixinho 🇺🇸 @maiconpeixinho]

Full piece (just published): https://t.co/7OwOnpUOz1
If you’re building anomaly reasoning, continuous signals, or surfacing undefined patterns — DMs open.
#EnterpriseAI #AIAgents #GenAI #AnomalyDetection