You might believe you should spend less time thinking about code because of AI.
I strongly disagree! We’re watching this play out live where tons of AI generated code becomes a liability.
At the end of the day, an engineer needs to be responsible / on call for code that gets shipped to production. If you don’t understand the system you’re trying to debug, you’re probably going to have a bad time.
Yes, AI can help with all of this, if you set up the proper systems. You can have agents triage prod logs, look at errors, etc. You can speed up parts of the investigation, but an engineer needs to make the call. There might be serious customer or financial implications from that change.
I expect the trend continue for trimming dependencies, vendoring code so you can modify it directly, preferring simpler systems with fewer abstractions, and spending waaaay more time thinking about system design and code maintenance.
I’ve said this before, but it’s a great time to get familiar with CS fundamentals and some of the history behind what great software looks like. Many parts will be different in the coming years as AI progresses, but also a lot more than people realize will stay the same.
A Norwegian neuroscientist spent 20 years proving that the act of writing by hand changes the human brain in ways typing physically cannot, and almost nobody outside her field has read the paper.
Her name is Audrey van der Meer.
She runs a brain research lab in Trondheim, and the paper that closed the argument was published in 2024 in a journal called Frontiers in Psychology. The finding is brutal enough that it should have changed every classroom on Earth.
The experiment was simple. She recruited 36 university students and put each one in a cap with 256 sensors pressed against their scalp to record brain activity. Words flashed on a screen one at a time.
Sometimes the students wrote the word by hand on a touchscreen using a digital pen, and sometimes they typed the same word on a keyboard. Every neural response was recorded for the full five seconds the word stayed on screen.
Then her team looked at the part of the data most researchers had ignored for years, which is how different parts of the brain were communicating with each other during the task.
When the students wrote by hand, the brain lit up everywhere at once.
The regions responsible for memory, sensory integration, and the encoding of new information were all firing together in a coordinated pattern that spread across the entire cortex. The whole network was awake and connected.
When the same students typed the same word, that pattern collapsed almost completely.
Most of the brain went quiet, and the connections between regions that had been alive seconds earlier were nowhere to be found on the EEG.
Same word, same brain, same person, and two completely different neurological events.
The reason turned out to be something nobody had really paid attention to before her work. Writing by hand is not one motion but a sequence of thousands of tiny micro-movements coordinated with your eyes in real time, where each letter is a different shape that requires the brain to solve a slightly different spatial problem.
Your fingers, wrist, vision, and the parts of your brain that track position in space are all working together to produce one letter, then the next, then the next.
Typing throws all of that away. Every key on a keyboard requires the exact same finger motion regardless of which letter you are pressing, which means the brain has almost nothing to integrate and almost no problem to solve.
Van der Meer said it plainly in her interviews.
Pressing the same key with the same finger over and over does not stimulate the brain in any meaningful way, and she pointed out something that should scare every parent who handed their kid an iPad.
Children who learn to read and write on tablets often cannot tell letters like b and d apart, because they have never physically felt with their bodies what it takes to actually produce those letters on a page.
A decade before her, two researchers at Princeton ran the same fight using a completely different method and ended up at the same answer. Pam Mueller and Daniel Oppenheimer tested 327 students across three experiments, where half took notes on laptops with the internet disabled and half took notes by hand, before testing everyone on what they actually understood from the lectures they had watched.
The handwriting group won by a wide margin on every question that required real understanding rather than surface recall.
The reason was hiding in the transcripts of what the two groups had actually written down.
The laptop students typed almost word for word, capturing more total content but processing almost none of it as they went, while the handwriting students physically could not write fast enough to transcribe a lecture in real time, which forced them to listen carefully, decide what actually mattered, and put it in their own words on the page.
That single act of choosing what to keep was the learning itself, and the keyboard had quietly skipped the choosing and skipped the learning along with it.
Two studies. Two countries. Same answer.
Handwriting makes the brain work. Typing lets it coast.
Every note you have ever typed instead of written went into your brain through a thinner pipe. Every meeting, every book highlight, every idea you captured on your phone instead of on paper was processed at half depth.
You did not forget those things because your memory is bad. You forgot them because typing never woke the part of the brain that would have made them stick.
The fix is the thing your grandmother already knew.
Pick up a pen. Write the thing down. The slower road is the faster one.
@r0ck3t23@asymco Patient, established players in supply chain, ERP, and industry software can deliver practical AI outcomes in manufacturing, finance, and healthcare once foundational layers stabilize.
dylan field shared on lenny's podcast how he got figma's first users:
he wrote a script to scrape twitter, built a network graph of the design community in Gephi, ran an analysis to find the most influential nodes, then reached out to every one of them
"can I buy you a coffee?"
this is basically what smart outbound looks like in 2026, just with better tools:
1) use data to find who's actively talking about your problem space
2) rank them by engagement and network reach
3) reach out referencing something specific they said
the playbook from 2012 is still the best GTM strategy :)
the only difference is now you don't have to write your own scraper or limit yourself to one platform, just use the Crustdata API
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.
Recently companies like Atlassian, Cursor, Google & Meta have all publicly shared that they encourage design and dev teams to use vibe-coding for rapid prototyping purposes. https://t.co/hH8WuWtwDg
This instantly changes the environment for design tools.
Most 'AI in supply chain' projects fail because they underestimate the importance of real-time data integration. S&OE automation thrives on accurate, timely data feeds. Invest in robust data pipelines or risk failure.
Trump's January 2026 economic blueprint proposes incorporating Venezuela's oil and Greenland's minerals to fuel U.S. growth, following U.S. intervention in Venezuelan politics and renewed purchase overtures to Denmark.
Trump's economic strategy is fascinating: including Venezuela and Greenland in the growth plan isn't just geopolitical theater; it's a masterstroke in shifting power dynamics. It's all about who controls the resources.