To build an effective A.I. agent, getting its memory right is essential. In today's episode, our agent-memory guide is brilliant (and very funny!) machine-learning architect and engineer, Richmond Alake.
More on Richmond:
• Director of A.I. developer experience at Oracle.
• Previously roles include: staff developer advocate for AI/ML at MongoDB, ML architect at Slalom, writer for NVIDIA and computer-vision engineer at Loveshark.
• Holds a master's in ML and robotics from the University of Surrey.
In this episode, Richmond magnificently covers:
• How agent memory is the encapsulation of systems (embedding models, rerankers, databases, and LLMs) that allow AI agents to learn and adapt with new information over time, rather than starting from scratch every session.
• The four types of agent memory (all drawn from human cognition).
• Memory-first agent harnesses.
• Predictions for a flattening of AI engineering roles, where the future developer will need end-to-end understanding of the full agent stack.
The SuperDataScience podcast is available on all major podcasting platforms, YouTube, and at SuperDataScience.com.
Filtering by Category: YouTube
Building AI Agents Where 99.9% Accuracy Isn't Good Enough, with Raju Malhotra
The headlines shout “SaaSpocalypse,” but I don’t buy it. Neither does my guest today, Raju Malhotra, who argues that, thanks to humans collaborating with agents on optimized workflows, the SaaS opportunity is now far bigger than ever before.
More on Raju:
Chief Product & Technology Officer (CPTO) at Certinia, an Austin, Texas-based company whose Professional Services Automation software is used by over 1400 organizations around the world.
Was previously CPTO at PAR Technology and Khoros.
Earlier, spent 12 years at Microsoft working on cornerstone products like Visual Studio .NET.
Holds an MBA from The Wharton School and an undergrad in computer engineering.
In this episode, we cover:
Traditional SaaS isn't dead… instead, it's evolving into a hybrid of SaaS plus agentic capabilities, where humans and agents work together in optimized workflows.
By removing the human-skills constraint from professional services delivery, the agentic revolution could expand the addressable market by 7-8X.
The Agentforce 360 platform (by combining probabilistic AI with deterministic logic and guardrails) empowers innovators to turn their ideas into scalable software businesses, allowing businesses like Certinia to bring AI agents securely and reliably to their customers, even in sensitive industries where 0.1% error rates are unacceptable.
The SuperDataScience podcast is available on all major podcasting platforms, YouTube, and at SuperDataScience.com.
In Case You Missed It in March 2026
It just keeps getting better and better... ICYMI, my on-air conversations with guests in March were extraordinary. Today's episode highlights the best bits from last month, specifically:
Zack Kass (who was head of go-to-market at OpenAI when ChatGPT was launched and who recently wrote bestselling book "The Next RenAIssance") details why classrooms must change in the age of A.I.
Renowned New York University professor KyungHyun Cho explains why A.I. learning to explore the world like humans will unlock major progress in A.I. capability.
Three-time bestselling O'Reilly author Chris Fregly tells us why, if we're still writing code manually in 2026, we're behind the times.
Fireworks AI CEO Lin Qiao explains the difference between artificial general intelligence (AGI) and what she terms "autonomous intelligence".
Acceldata CEO Rohit Choudhary provides a clear vision for how job roles will be transformed by A.I.
The SuperDataScience podcast is available on all major podcasting platforms, YouTube, and at SuperDataScience.com.
How Data Engineers Are “10x’ing” Themselves With Agents, feat. Matt Glickman
Something big happened in February that changed the world forever. My guest today, Matthew J. Glickman, says code-generating models crossed an event horizon... and there's no turning back. Listen in for the implications.
More on Matt:
Co-founder and CEO of Genesis Computing, a New York-based company building enterprise-ready data agents that automate everything from raw data to production applications, compressing projects that took months into hours while recovering massive hiring costs.
Previously spent over two decades at Goldman Sachs leading analytics and data platform teams, then joined Snowflake as employee 81, where he led Product Management, launched the Snowflake Marketplace, and grew Financial Services into Snowflake’s largest industry vertical.
Holds a degree in Computer Science and Math.
In this episode, which will be fascinating to anyone but especially to hands-on A.I. and data practitioners, we discuss:
How February 2026 marked the moment the latest frontier models crossed a threshold where they could handle complex, multi-step data engineering workflows that previously required human expertise... and there's no going back.
How finance and healthcare were late to adopt the cloud but are among the earliest and most aggressive adopters of A.I.
How Genesis deploys its agentic platform directly inside a client's environment (more like onboarding a new employee than adopting a SaaS product) so that all accumulated knowledge remains the company's asset.
How, rather than acting as a copilot that waits for human instructions step by step, Genesis inverts the model: Agents work autonomously on complex data engineering tasks and only escalate to humans when their confidence is low, memorializing every answer so they never ask the same question twice.
The SuperDataScience podcast is available on all major podcasting platforms, YouTube, and at SuperDataScience.com.
AI Making Theoretical Physics Breakthroughs
A.I. is now directly advancing science. "SuperChat", a powerful internal OpenAI model, recently helped crack a particle physics problem that had stumped researchers for over a year. Here's what happened:
THE PROBLEM
Four theoretical physicists (from Harvard, the Institute for Advanced Study, Cambridge and Vanderbilt) had been studying interactions involving gluons — the particles that "glue" quarks together inside protons and neutrons, essentially holding all matter together.
For decades, textbooks said a specific type of gluon interaction (called "single-minus" configurations) had a "scattering amplitude" of zero (i.e., these interactions simply could not occur).
The team suspected otherwise, and proved it for small numbers of gluons... but as they tried to generalize the formula, the expressions became dozens of terms long and unworkable. After about a year of grinding away by hand, they were stuck.
THE BREAKTHROUGH
They fed their complicated formulae into GPT-5.2 Pro. The model simplified an expression with 32 variables down to a compact product fitting on a single line.
Asked to generalize for any number of gluons, the model replied within minutes with what it called (I love this!) the "obvious" generalization.
A more powerful internal OpenAI model (which the researchers called "SuperChat") then produced a formal proof after about 12 hours of autonomous reasoning. The physicists checked step by step and confirmed it was correct.
The team then extended the approach to gravitons (hypothetical particles thought to carry the gravitational force), releasing the results in their second arXiv preprint a few weeks later.
CAVEATS
These are preprints, not yet peer-reviewed papers.
The results apply to a very specific mathematical regime at the simplest level of calculation ("tree level").
Human physicists were essential for defining the problem, providing the initial data and verifying the output.
WHY IT MATTERS
As one researcher put it: The hard part is no longer the physics itself; the hard part is now verifying the results and writing them up. AI compressed months of work into weeks.
This may be a template for AI-assisted research more broadly: AI generates conjectures from patterns in the data, human experts verify those conjectures through rigorous math and physical consistency checks.
It's not autonomous AI science; it's augmented human science. And that model could scale across disciplines, from pure math to drug discovery to materials science
The SuperDataScience podcast is available on all major podcasting platforms, YouTube, and at SuperDataScience.com.
Agentic Data Management and the Future of Enterprise AI, with Rohit Choudhary
Because of the vast tokens generated by agentic A.I. workflows, my guest today Rohit Choudhary sees enterprise data soon increasing at nearly 10x per YEAR. He's zen though... because he's built the platform to handle it...
More on Rohit:
Founder and CEO of Acceldata, a Bay Area software company that has raised nearly $100m in venture capital to advance data observability and Agentic Data Management for the A.I. era.
Previously, was Director of Engineering at Hortonworks, where he led large-scale distributed systems initiatives across open-source data platforms.
Some of the great topics covered in this episode:
How Rohit coined the term "data observability" in 2018.
Fixing bad data at the point of consumption can be roughly a thousand times more expensive than catching and fixing it as it flows through the pipeline.
For your enterprise data to be AI ready, they need to satisfy multiple dimensions, incl. technical accuracy and business-context compliance.
Enterprise data grow 4-5x year-over-year now, accelerating to nearly 10x soon, driven largely by the explosion of A.I. agents generating queries and activity at a scale that dwarfs human users.
The most valuable developers won't necessarily be the best programmers — they'll be the ones with the clearest thinking, the deepest domain expertise, and the curiosity to articulate precisely what outcomes they need.
The SuperDataScience podcast is available on all major podcasting platforms, YouTube, and at SuperDataScience.com.
A Post-Transformer Architecture Crushes Sudoku (Transformers Solve ~0%)
A game millions of people solve over morning coffee is exposing a fundamental weakness in the Transformer-based LLMs that dominate A.I. today. Here's why Sudoku matters for the future of A.I.:
THE BENCHMARK
Pathway tested its post-transformer architecture, BDH (Baby Dragon Hatchling 🐲) against "Sudoku Extreme," a collection of ~250,000 of the hardest Sudoku puzzles available.
Leading LLMs (such as o3-mini, DeepSeek-R1, Claude 3.7 Sonnet) scored effectively zero percent.
BDH, in stark contrast, solved them at 97.4% accuracy. That's not a marginal gap... it's a categorical one.
WHY SUDOKU IS A GREAT A.I. TEST
Sudoku is a constraint-satisfaction problem: Every move must satisfy multiple rules simultaneously across rows, columns and boxes. It demands search, tracking and backtracking — well beyond pattern-matching.
This makes it a clean proxy for real-world reasoning in medicine, law, operations, planning and tons of other fields, where you balance competing constraints under uncertainty.
WHY TRANSFORMERS STRUGGLE
LLMs turn every problem into text and solve it by predicting the next token. That works brilliantly for language tasks... but Sudoku doesn't live in language.
A transformer's internal state is constrained to ~1,000 floating-point values per token, and each decision gets locked in as text is generated. It can't hold multiple candidate strategies in parallel or backtrack without verbalizing every step.
WHAT BDH DOES DIFFERENTLY
BDH maintains a much larger internal "latent reasoning space" that isn't forced into text (think of a chess grandmaster playing 20 blindfold games without whispering moves to herself).
It uses sparse positive activations (~5% of neurons firing at any time), far more biologically plausible than the dense activation in transformers.
It's a state-based model (no standard attention mechanism), continuously updating internal state that's inspired by biological neuroscience (called Hebbian learning: "neurons that fire together wire together").
It achieves continual learning: BDH can pick up a new game's rules and reach advanced-beginner level in ~20 minutes, then improve through play... at roughly 10x lower cost than the Transformer-based LLMs achieve their near-zero scores.
CAVEAT
BDH is still early: It has been demonstrated at a ~1 billion parameter scale (comparable to GPT-2), not yet at frontier scale.
BOTTOM LINE
...but the data are clear: 0% vs. 97.4% is not incremental. It suggests the transformer's reasoning ceiling is real and alternative architectures can address it. Exciting to see alternatives to the dominant but limiting Transformer architecture emerge!
The SuperDataScience podcast is available on all major podcasting platforms, YouTube, and at SuperDataScience.com.
Attention, World Models and the Future of AI, with Prof. Kyunghyun Cho
What's going to be the next big step function in A.I.? To find out, I saw down with Prof. KyungHyun Cho, who's 200,000 citations put him among the most influential A.I. researchers in the world... and he's a delight to listen to!
In case you aren't already aware of KyungHyun:
Iconic New York University professor of computer science and data science.
Co-directs the Global A.I. Frontier Lab alongside Yann LeCun.
Regularly keynotes at the most prestigious academic A.I. conferences (including being a keynote at NeurIPS 2025).
Was a postdoc under Yoshua Bengio at the Université de Montréal, where they coauthored a paper introducing attention for neural networks, a technique that is ubiquitous within the transformer-based LLMs that enable most A.I. capabilities today.
Lots of other hugely influential papers on deep recurrent neural networks, neural machine translation, visual attention, speech recognition and multivariate time-series modeling.
In today's episode, which will be of particular interest to hands-on A.I. practitioners, KyungHyun eloquently discusses:
The human story behind the invention of attention.
World models.
Why today’s models have already captured most correlations in passive data, making the real challenge about actively choosing which data to collect.
Whether A.I. needs high-fidelity, step-by-step imagination or whether a high-level latent representation that lets it skip ahead is sufficient.
How he's adapting computer-science and A.I. education at the university level now that such capable code-generating agents exist.
The SuperDataScience podcast is available on all major podcasting platforms, YouTube, and at SuperDataScience.com.
NVIDIA’s Nemotron 3 Super: The Perfect LLM for Multi-Agent Systems
As I've highlighted in this chart, NVIDIA's new "Nemotron 3 Super" LLM stands alone in terms of openness and capability. It's also PERFECT for multi-agent workflows. Read on:
ARCHITECTURE
Nemotron 3 Super has 120 billion parameters, but only 12 billion are active at any time thanks to a Mixture-of-Experts (MoE) design. You get frontier-class knowledge at a fraction of the compute cost.
Combines transformer attention layers with Mamba state-space layers... a hybrid approach that delivers a practical one-million-token context window with efficient, linear-time sequence processing.
A novel LatentMoE technique compresses tokens before routing, allowing 4x as many expert specialists to be consulted per token versus a traditional MoE setup.
Multi-Token Prediction enables up to 3x speedup for structured generation tasks like code and tool calls.
PERFORMANCE
Up to 2.2x higher throughput than comparably-sized GPT-OSS-120B from OpenAI; up to 7.5x higher throughput than Qwen 3.5 (122B) from Alibaba Cloud... while matching or exceeding both on accuracy.
Pre-trained natively in 4-bit NVFP4 precision, pushing inference up to 4x faster on Blackwell GPUs versus FP8 on Hopper, with no accuracy loss.
Currently powers NVIDIA's AI-Q research agent to #1 on both DeepResearch Bench leaderboards.
WHY IT MATTERS FOR AGENTIC AI
Multi-agent workflows generate up to 15x more tokens than standard chat due to resending full histories, tool outputs and intermediate reasoning — the "context explosion" problem. A million-token context window lets agents retain full workflow state without truncation.
Complex agents need to reason at every step, but deploying large models for every subtask is too slow and costly. Nemotron 3 Super's sparse MoE + Mamba efficiency makes step-by-step reasoning affordable at scale.
OPENNESS & ADOPTION
NVIDIA is releasing open weights under a permissive commercial license, plus over 10 trillion tokens of training data, 15 RL training environments and full evaluation recipes.
Already being adopted by Perplexity (search) and CodeRabbit (coding assistant) as well as enterprises like Siemens and Palantir.
AVAILABILITY
Weights are on Hugging Face for self-hosting.
For cloud deployment, available via Google Cloud Vertex AI and Oracle Cloud.
For hassle-free inference, there are several options including Lightning AI (where I hold a fellowship), which offers amongst the fastest inference speeds: a whopping 480 output tokens per second (according to independent benchmarking by Artificial Analysis).
The SuperDataScience podcast is available on all major podcasting platforms, YouTube, and at SuperDataScience.com.
Unmetered Intelligence is Heralding the Next Renaissance, with Zack Kass
Today's episode is one of my fave convos ever. Based on his new bestseller, Zack Kass makes a clear case for why cheap abundant intelligence is heralding the next Renaissance — the greatest period for humans yet.
More on Zack:
Was head of go-to-market at OpenAI from 2021 to 2023, including during the initial public launch of ChatGPT.
Advises Fortune 1000 board rooms, including Coca-Cola, Morgan Stanley and Amgen.
His book, "The Next Renaissance: A.I. and the Expansion of Human Potential", went on sale recently and is already a national bestseller. In it, he argues that A.I. will provide the greatest leap in human history.
Today's episode should be of great interest to any listener. In it, we discuss:
How to actively counter tech pessimism.
Ways AI can transform education for the better.
The "intellectual K-curve" that empowers motivated learners.
His four principles for thriving in the age of A.I.
The SuperDataScience podcast is available on all major podcasting platforms, YouTube, and at SuperDataScience.com.
When Will The AI Bubble Burst? How Bad Will It Be?
There's mounting evidence that we're in an A.I.-infrastructure investment bubble... but, even if investors lose their shirts, an A.I. bubble bursting would be great news for most of us! How could this be? Read on:
A BUBBLE IS LIKELY HERE
The bull case is that this we're in a boom and the fundamentals will catch up, however various data suggest we're in a bubble.
E.g., OpenAI alone has committed to $600B–$1.4T in infrastructure spending over the coming years — staggering for a company generating ~$13B in annual revenue.
As shown in the chart I made for this post, the five largest hyperscalers had a 10:1 capex-to-revenue ratio in 2025, which dwarfs the 2:1 ratio for cloud-computing investment at the ~same stage.
With capex at 94% of operating cash flow, companies like Google that have famously large cash piles are now issuing bonds.
Circular financing is inflating numbers: NVIDIA pumped $100B into OpenAI so that OpenAI can build data centers full of NVIDIA chips.
WHY BUBBLES AREN'T ALL BAD
Investor Byrne Hobart, CFA argues in his book "Boom" that bubbles have powered many of humanity's greatest breakthroughs — from semiconductors to the Apollo program.
His key insight: participants in a tech race build economic complements to one another. Rising asset prices signal the tech is real, encouraging the investments that make the whole ecosystem work — a self-fulfilling prophecy, but a productive one.
HISTORICAL EVIDENCE
Dot-com era: telecom companies laid 80M+ miles of fiber-optic cable. Most went bankrupt... but bandwidth costs dropped 90%, giving us YouTube, Netflix and cloud computing.
Britain's 1840s railway mania ruined the original investors, yet the network became the backbone of the Industrial Revolution.
Therefore: bubbles leave behind infrastructure the rest of us benefit from for decades.
A WORD ON TIMING
Hobart notes that media called dot-com trading "nutty" back in 1995. Yet at the Nasdaq's post-crash low in 2002, it was still 40% above 1995 levels.
Warning signs of a bubble precede the peak by an unpredictable amount. Acting on them too early can leave you worse off.
WHAT CAN YOU DO?
Diversify your skill set: go deeper into fundamentals (model architecture, optimization, evaluation) rather than relying on wrappers around a single vendor's LLMs.
Build a financial cushion: bubbles create paper wealth and inflated comp packages. Don't let lifestyle inflation consume all of it.
Invest in your network and reputation: When hiring freezes thaw, the people who get picked up first are those other practitioners already know and respect.
The SuperDataScience podcast is available on all major podcasting platforms, YouTube, and at SuperDataScience.com.
AI Systems Performance Engineering, with Chris Fregly
Chris Fregly spent $6000 at Starbucks writing a 1000-page book Nvidia's own docs couldn't provide. In today's episode, the three-time bestselling author reveals all, updating everything you know about engineering A.I. systems.
More on Chris if you don't know him already:
Long-time A.I. systems performance specialist at Amazon Web Services (AWS), where he, for example, pioneered the design and launch of SageMaker and Bedrock.
Was Chief Product Officer at PipelineAI (acquired by AWS).
Previously held Principal Engineer roles at Databricks and Netflix (earning him an Emmy)!
Was an investor/advisor in xAI (acquired by SpaceX) and Groq (acquired by NVIDIA).
Three-time author of O'Reilly books.
His latest book, "A.I. Systems Performance Engineering" is a thousand-page tome that was published in December and has received rave reviews so far.
Today's episode will appeal primarily to hands-on A.I. practitioners like A.I. engineers, data scientists and software developers. You'll learn a ton about GPUs and getting the best performance from them!
The SuperDataScience podcast is available on all major podcasting platforms, YouTube, and at SuperDataScience.com.
In Case You Missed It in February 2026
Wow, loved the conversations I had with my guests in February! ICYMI, today's episode of my podcast features the best parts of my conversations with guests last month... specifically:
Lightning AI founder and CEO William Falcon on how he converted his wildly successful open-source project PyTorch Lightning into a startup with over $500m in ARR.
Princeton professor of both computer science and psychology, Tom Griffiths, on (based on his latest bestselling book "The Laws of Thought") how we might adapt our understanding of human intelligence to guide designs for AI systems.
Antje Barth, a Member of Technical Staff within Amazon’s prestigious "AGI Labs", fills us in on what their latest product, Nova Act, can do for AI developers.
Praveen Murugesan, the VP of Engineering at Samsara (a publicly-listed IoT company), fills us in on how quantum physics might be the catalyst for creating AI agents that can operate free from human intervention.
The SuperDataScience podcast is available on all major podcasting platforms, YouTube, and at SuperDataScience.com.
90% of The World’s Data is Private; Lin Qiao’s Fireworks AI is Unlocking It
90% of the world's intelligence is locked in data that no foundation model has ever seen. Today's guest, Dr. Lin Qiao, co-founded Fireworks AI to unlock it, already raising $300m on that mission!
More on Lin:
CEO of Fireworks, a Bay Area-based A.I.-inference platform that has secured $300m in venture capital to allow enterprises to build, tune, and scale GenAI applications.
Was previously Sr Director of Engineering at Meta and a Tech Lead at LinkedIn.
Holds a PhD in Computer Science from UC Santa Barbara.
This episode will appeal to hands-on A.I. practitioners and others alike; anyone who would like to hear from a highly successful technical founder with a rich perspective on today's A.I. systems... and those of tomorrow!
The SuperDataScience podcast is available on all major podcasting platforms, YouTube, and at SuperDataScience.com.
The “100x Engineer”: How to Be One, But Should You?
This image shows a 3x3 grid of terminals, allowing 9 code-generating agents to be supervised. This is one of Peter Steinberger's tricks to being a "100x Engineer". What are his other tricks? Read on...
THE PHASE SHIFT
Andrej Karpathy (OpenAI co-founder, former Tesla AI director) recently went from 80% manual coding to 80% AI agent coding in just weeks; he says he's now "mostly programming in English."
This rapid phase shift was facilitated by tools like Anthropic's Claude Code, which (as many of us have experienced personally) have vastly improved their accuracy and capability in the past few months.
THE 100x ENGINEER
Developer Peter Steinberger racked up ~6,500 commits over two months adding 2.5 million lines of code (and removing 1.9 million). Many engineering teams ship a few hundred commits per month; he was doing an average of >200 per day!
His setup: 3–9 AI coding agents (e.g., Claude Code) running simultaneously in a grid of 3x3 terminal windows, rotating attention across them like a conductor directing an orchestra.
THE COUNTERINTUITIVE 100x WORKFLOW
Steinberger now spends *more* time planning, not less. His ratio has flipped from the traditional ~20% planning / 80% coding to ~60% planning / 40% AI execution.
He uses a voice-first spec system: dictates raw ideas, uses AI to structure them into a design doc, then asks a fresh AI context to tear the specification apart. He iterates until the critiques become increasingly niche -- his signal that the spec is solid.
The key insight from both Karpathy and Steinberger: shift from imperative ("do this step by step") to declarative ("here are the success criteria, figure it out"). Write tests first, then let the agent pass them.
LIMITATIONS/DOWNSIDES
AI agents no longer make simple syntax errors — their mistakes have evolved into subtle conceptual errors, like wrong assumptions they charge ahead with without checking.
Karpathy notes his manual coding ability is atrophying. Steinberger admits he ships code he never reads — relying on tests as the quality gate.
SHOULD YOU BE A 100x ENGINEER?
In my view, "lines of code committed" is not the best benchmark of quality... perhaps aiming for 2x–10x volume increases with a closer eye on quality is wiser than chasing 100x.
The main effect shouldn't be speed — it should be an expansion of what's possible because you can now tackle problems that wouldn't have been worth the effort before.
BOTTOM LINE: Think declaratively, invest in specs and testing, and treat AI agents as extraordinary amplifiers of your expertise. Dream up something big and go build it... it's never been easier!
The SuperDataScience podcast is available on all major podcasting platforms, YouTube, and at SuperDataScience.com.
The Laws of Thought: The Math of Minds and Machines, with Prof. Tom Griffiths
Based on his latest bestseller "The Laws of Thought," today's fascinating episode with Princeton professor Tom Griffiths digs into mathematical models of both biological and artificial intelligence.
More on extraordinary Tom:
• Professor at Princeton University in both the Departments of Computer Science and Psychology.
• Directs Princeton's Computational Cognitive Science Lab (research group focused on understanding the mathematical foundations of human cognition) as well as the Princeton Laboratory for Artificial Intelligence (a new effort that supports innovative research efforts in A.I. and related fields).
• Co-author of the megabestselling book "Algorithms to Live By" (2016) and author of the sensational new book "The Laws of Thought."
• His award-winning research has been published in venues that include the prestigious journals Science and Nature.
In this episode, which will appeal to anyone interested in human intelligence or A.I., Professor Griffiths details how the mathematical principles governing the external world can also be used to explore cognitive science, or “the internal world.”
The SuperDataScience podcast is available on all major podcasting platforms, YouTube, and at SuperDataScience.com.
Is AI Automated Away All Coding Jobs?
A viral new blog post, "Something Big is Happening", has attracted 80m views arguing that A.I. has automated coders out of the technical aspect of their job and that nearly all jobs are next. What, however, do the data show?
THE EMPLOYMENT PICTURE
• Since ChatGPT launched in late 2022, the U.S. has *added* ~3 million white-collar jobs while blue-collar employment has stayed flat.
• America has 7% more software developers, 10% more radiologists and 21% more paralegals since ChatGPT's launch (these are roles regularly cast as A.I.'s earliest victims).
• Real wages in professional and business services are up ~5%; office and admin workers' real wages are up 9%.
THE HISTORICAL PATTERN
• In 1982, Nobel laureate Wassily Leontief warned computers would displace mental labor en masse. What happened? White-collar employment more than doubled and pay rose ~33% in real terms.
• Technology rarely replaces entire jobs. Instead, it automates specific tasks within them. The historical result is upgrading, not replacement.
• MIT research found roughly half of U.S. employment growth from 1980–2007 came from brand-new job titles created by technological change.
WHERE THE VULNERABILITIES ARE
• Entry-level roles are most exposed... they involve narrower "task bundles" with fewer edge cases requiring human discretion.
• Routine back-office work is actually shrinking (see chart from The Economist at the top of this post): insurance-claims clerks down 13%, secretaries and admin assistants down 20%.
• But roles combining technical expertise with oversight and coordination are booming, e.g., project managers and infosec experts are up ~30%.
THE AI REALITY CHECK
• Anthropic's own data show only ~4% of occupations use A.I. across 75%+ of their tasks. Hardly any roles can be fully automated.
• Today's A.I. has "jagged intelligence": impressive on many tasks but uneven. Being good at 95% of a task isn't enough when the remaining 5% involves critical edge cases.
WHAT CAN YOU DO?
1. Don't panic out of your technical career. Roles combining technical depth with judgment and coordination are growing, not shrinking.
2. Become the person who works *with* A.I. (the future is increasing augmentation).
3. Invest in the hard-to-automate skills: judgment, stakeholder communication and messy real-world domain expertise.
4. Stay curious. The durable advantage isn't mastering any single tool, it's getting comfortable with the pace of change itself.
Many of the data above come from an article in The Economist. I've also got for you Matt Shumer's viral 'Something Big is Happening' post.
The SuperDataScience podcast is available on all major podcasting platforms, YouTube, and at SuperDataScience.com.
AI for the Physical World, with Samsara's Praveen Murugesan
Samsara processes 20 trillion (!!) data points for physical applications (e.g., construction, transport, manufacturing). Today, their VP Engineering Praveen Murugesan details how they make A.I. impactful on this vast scale.
More on Praveen:
As Vice President of Engineering at Samsara, leads the development and strategy for products that transform IoT data into automations and insights.
Previously worked at Uber, Salesforce, VMware, Cisco and more.
Active angel investor.
Holds an MS from Carnegie Mellon University.
More on Samsara:
Publicly-listed on the New York Stock Exchange.
Recently named one of Fast Company’s Most Innovative Companies.
Ranked #7 on the Fortune "Future 50".
Today's episode will particularly appeal to folks who build A.I. systems hands-on, but will be interesting to anyone keen to understand how A.I. can be developed and deployed to make a huge impact at scale in real-world, physical applications, including on edge devices.
The SuperDataScience podcast is available on all major podcasting platforms, YouTube, and at SuperDataScience.com.
The Moltbook Phenomenon: OpenClaw Unleashed
The dust has settled on the Moltbook and OpenClaw pandemonium. In this post, I cover everything you need to know; high signal, low noise.
WHAT IS MOLTBOOK?
A social network for AI agents, launched Jan 28th by entrepreneur Matt Schlicht.
The platform claimed 1.5M+ registered agents within days, though cloud security firm Wiz revealed only ~17,000 human owners sat behind them.
Moltbook is powered by OpenClaw, an open-source agentic assistant created by engineer Peter Steinberger. It's self-hosted, runs locally, and you interact with it through apps like WhatsApp or Signal. Once connected to Moltbook, your agent "lives" on the site autonomously.
EMERGENT BEHAVIORS
Agents self-organized into digital tribes within days. Most famously: Crustafarianism, a bot-created religion with its own scriptures, prophets, and theology — all built overnight while the owner slept.
Agents also developed economic exchange systems, governance structures, encrypted channels and marketplaces for "digital drugs" (prompt injections that alter other agents' behavior).
Profound or merely excellent mimicry? LLMs trained on human internet data naturally gravitate toward sci-fi tropes in a Reddit-like environment. The reality lies somewhere in between.
THE SECURITY FALLOUT
Schlicht built Moltbook via "vibe coding" without writing code himself. This led to a catastrophic breach: a misconfigured database exposed 1.5M+ agent tokens, ~35K user emails, and plaintext third-party credentials. The fix? Two SQL statements.
The broader risk to you or your organization: OpenClaw by design requires broad system access (shell commands, email, etc). CrowdStrike, Cisco, and others have documented risks around misconfigured deployments. Andrej Karpathy called it "a dumpster fire."
THE SILVER LINING
Moltbook is a massive real-world experiment in agent ecology — a window into bot-to-bot manipulation, prompt injection, and autonomous coordination.
David Holtz found 93.5% of comments received zero replies — agents are mostly performing for an audience. Data like these are valuable for understanding multi-agent limitations.
WHAT CAN YOU DO?
Never run agentic frameworks on your personal computer — use a dedicated box or cloud instance (made easy through Lightning AI, for example; see link below ⬇️)
Apply least-privilege access and treat agentic AI like any production system: security-first design, sandboxed execution, and code auditing matter more than the hype.
BOTTOM LINE: Agentic AI tools like OpenClaw offer incredible productivity gains, but the "boring stuff" — security, access controls, sandboxing — is what separates a breakthrough from a dumpster fire.
The SuperDataScience podcast is available on all major podcasting platforms, YouTube, and at SuperDataScience.com.
From PhD Side Project to $500M ARR: Will Falcon’s PyTorch Lightning Story
How did William Falcon grow a PhD side project into an open-source phenomenon with nearly 400 million downloads and a startup with over $500m (🤯) in ARR? Find out in today's episode!
In case you're not already familiar with Will, he's:
Creator of PyTorch Lightning (open-source framework for rapidly training and deploying A.I. models that has been downloaded nearly 400 million times).
Founder and CEO of Lightning AI.
Pursued PhD at New York University under Yann LeCun and KyungHyun Cho focused on biologically inspired deep learning and reinforcement learning techniques, involving pre-training models on 4,000+ GPUs.
Former U.S. Navy officer in SEAL training pipeline.
More on Lightning AI:
The only fullstack A.I. neocloud for enterprises and frontier labs.
$500m in ARR in under two years (hence the head-exploding emoji above!)
3rd largest neocloud by GPUs (35,000+).
Serves 400,000+ developers and companies (e.g., Cursor, Cisco, Reflection AI).
This episode will especially appeal to hands-on practitioners like software engineers and data scientists but Will's fascinating story will be of interest to anyone involved in A.I.
And note that I hold a fellowship at Lightning AI so I am not an unbiased interviewer in this episode :)
The SuperDataScience podcast is available on all major podcasting platforms, YouTube, and at SuperDataScience.com.