It's fascinating how much 💰💰💰 Palantir makes from bespoke applications. Their software architecture is standardized, but a massive amount of bespoke configuration, data pipeline engineering, and localized code is required to make any given deployment successful.
Pete Aven retweeted
"I really don't care about being right. I just care about success." - Steve Jobs, clip from the 1996 doc. Triumph of the Nerds
Pete Aven retweeted
Yann LeCun: The AI industry is completely LLM-pilled. Everybody is working on the same thing.
“They’re all digging the same trench.”
“They’re stealing each other’s engineers, so they can’t afford to do something different.”
“If they start going on a tangent, they’re going to fall behind the other guys.”
“It’s one big reason I left Meta. Meta also became LLM-pilled.”
“I cannot imagine that we can build agentic systems without those systems being able to predict the consequences of their actions.”
Pete Aven retweeted
My prediction for software engineering over the next 3 to 5 years.
AI won't replace software engineers. It'll drive even greater demand for them.
A team of 5 experienced engineers using AI will outperform a team of 20 pre-AI.
Which will dramatically reduce the cost of building software.
I think we'll see Jevons paradox in action. As something becomes cheaper and more efficient to produce, demand for it can increase dramatically.
Thousands of software projects that previously weren't economically viable will become worth doing.
The winners will be companies, big and small, that behave like Google, Amazon and 3M, with a culture of experimentation, a willingness to try lots of ideas and the discipline to kill their darlings.
TL/DR: Smaller engineering teams, more innovation, more software products, and potentially more software engineers than ever.
What do you think?
There is a massive gap between Machine Learning (ML)/AI expertise and Software Engineering. There is a BIG difference between creating an LLM and using one. ML experts in the spotlight trumpeting the end of Software Engineering are currently mistaken.
ML experts misunderstand the task. ML/Deep Learning experts are smart folks. They work with smart Software Engineers to generate their models. LLMs are amazing. But there is a HUGE difference in creating a model and using a model and code generation is not software engineering.
Databases are the future.
.@CarnegieMellon Appoints Database Professor @pateljm as Dean and Vice President of CMU Miami db.cs.cmu.edu/2026/10/carneg…
So much goodness here in patterns & best practices for building a knowledge layer for observability. Not everyone should follow the lead of building this on a homegrown triplestore layer on top of key-value or use RDF, LPG works great, but the rest I love netflixtechblog.com/ontology…
Pete Aven retweeted
Which skill is your biggest gap right now?
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Pete Aven retweeted
This is the least surprising graph in the world if you understand how gradient descent works.
