AI

2026-02-23 · open the original ↗

• Curiosity about physical operations is the first hiring criterion. Murugesan says Samsara is hiring and wants builders fascinated by how physical operations work, willing to meet customers and learn their problems, because that is what produces good products. The host cites Fast Company listing Samsara among the most innovative companies and a Fortune Future 50 rank of number seven.
• The second trait is agility in a still-unsettled scale-up. He describes the now-public company as still operating like a high-growth startup where not everything is figured out or bureaucratic, so he looks for people who like moving fast and shipping with focus.

2026-02-22 · open the original ↗

• Mathematically optimal routes failed because they erased human context. Murugesan recounts that a couple of months before recording Samsara showed customers' route planners "the most optimal routes" for the next day's orders, and planners could not recognise them. The routes ignored knowledge such as a driver named Sam being expected at a particular grocer at 10 a.m., relationships between drivers and customers that the planners wanted kept.
• Customers wanted assistive leverage, not full automation. Samsara reworked the same technology to take a customer's current routes and suggest improvements based on inefficiencies it finds, learning the human constraints as it goes. He draws the general lesson that what should be built today is "assistive intelligence", because the human component is valuable to running a business.

2026-02-21 · open the original ↗

• Quantum could turn approximate routing answers into optimal ones. Murugesan notes the travelling salesman problem is NP-hard: one vehicle visiting 20 stops has, by his uncertain recollection, well over a trillion orderings, and today's methods rely on approximation and shrinking the search space. The host quotes the guest's Diginomica article saying work that would take a classical platform 10 septillion years could be done in minutes with quantum.
• Real routing compounds the problem far beyond a single tour. He scales it up to the vehicle routing problem (10,000 stops, 15 vehicles), then adds the choice of road segments between stops and real-time traffic, which current approximation models mostly predict rather than ingest. Abundant compute would allow much higher precision, though he grants a point of diminishing returns on what is "good enough".
• Self-operating logistics remain far off. He expects today's technology to keep supporting assisted decisions and says "I don't think we're anywhere close" to truly self-operating systems, with compute and real-time data access the limiting factors.

2026-02-20 · open the original ↗

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2026-02-20 · open the original ↗

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2026-02-20 · open the original ↗

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2026-02-20 · open the original ↗

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2026-02-20 · open the original ↗

• The AI-jobs anxiety is rational but leaves out the evidence. The host lists its sources: AI entrepreneur Matt Shumer's viral post "Something Big Is Happening", claiming one can describe an app in plain English, walk away for 4 hours and return to a finished, tested product; Anthropic CEO Dario Amodei's prediction that AI could eliminate half of entry-level white-collar jobs within 1 to 5 years; the IMF's Kristalina Georgieva's "tsunami"; and Jamie Dimon saying JPMorgan Chase would soon need fewer employees. He offers The Economist's article "Why AI won't wipe out white-collar jobs" as counterweight.
• US white-collar jobs and pay have grown since ChatGPT launched. Per The Economist, the US added roughly 3 million white-collar jobs since late 2022 while blue-collar employment stayed flat, and it has 7% more software developers, 10% more radiologists and 21% more paralegals than 3 years ago. Real wages rose about 5% in professional and business services and 9% for office and admin workers, and the controlled white-collar premium of roughly a third is nearly triple its early-1980s level and still rising.
• Technology automates tasks within jobs, so jobs mostly get upgraded. The Economist recalls Wassily Leontief's 1982 warning, after which management, professional, sales and office employment more than doubled and real pay rose about a third. Its analysis of more than 100 occupations since late 2022 finds project managers and information-security experts up around 30%, insurance claims clerks down 13% and secretaries and admin assistants down 20%, while unnamed "other mathematical science" occupations grew around 40% with real wages up roughly a fifth; the host adds Daron Acemoglu and Pascual Restrepo's finding that about half of US employment growth from 1980 to 2007 came from occupations gaining new titles and tasks.
• Entry-level and clerical roles are exposed, and capability is compounding. Clerical and admin work fell from 18% of Americans in the 1980s to 10%, and research by Sam Manning and colleagues at the Center for the Governance of AI finds clerical workers least able to adapt. METR data show the task length frontier agents complete with 50% reliability doubling about every 7 months for 6 years, now over 6 hours of expert work, which the host extrapolates to multi-day autonomous work about a year away. Against that, Anthropic's usage data show only about 4% of occupations using AI across three-quarters or more of their tasks, which The Economist ties to "jagged intelligence".
• The host's advice: stay technical and learn where AI fails. His four recommendations are not to panic out of a technical career, to become the person who works with AI (The Economist's "cyborg workplace"), to invest in judgment, stakeholder communication, coordination and messy domain expertise, and to keep experimenting because today's models will be eclipsed in 6 months. He concedes real disruption in back-office and some entry-level work but calls the claim that all cognitive work is ending unsupported by employment, wage, historical or usage data.

2026-02-20 · open the original ↗

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2026-02-20 · open the original ↗

• Multi-step AI features leak users out of the treatment. In Shane's illustrative case, an internal AI analyst V2 is tested on 1,500 daily users. Of the 743 assigned to it, 156 never open the tool, 190 hit cached V1 results, 129 time out to a V1 fallback and others fall back on errors, leaving 167 (23%) who actually got V2. The headline +3% (p = 0.15) hides roughly +14% among those exposed, but with 167 users against a power calculation needing 760, the test has to be fixed and rerun rather than filtered after the fact.
• AI tests need far larger samples than the usual calculation suggests. Shane says a diluted effect needs disproportionately more users ("half as loud" means listening four times as hard). Non-deterministic outputs add variance, and performance that differs by segment, such as simple versus complex queries, needs a full sample per segment. Because these multiply, he says a test can need around 10 times the users expected.
• Measure correctness with code where possible, and calibrate offline evals. "SQL success rate" can mean valid syntax, executed, non-empty or actually correct. In the example, checking V2 against 200 finance-maintained reference answers finds 5% confidently wrong (off by more than 10% behind a convincing chart). An LLM judge that agrees with humans 80% of the time is a "noisy pen" that washes out real differences, so Shane's rule is to use deterministic code checks for everything else. He argues that over 5–15 experiments, offline quality gains can be calibrated to online outcomes such as task completion.
• More guardrails and pre-registered segments change the ship decision. Shane lists guardrails beyond latency and crash rate: cost per query, safety and PII violations, hallucination, confidently-wrong rate and data leakage, each needing a measurable online mechanism, with quality gains often costing latency or money. Averages hide failures, so the call may be to ship for simple queries and hold for complex ones; segments must be chosen before the test to avoid sample-size shortfalls and p-hacking.
• Run AI tests longer, because users change what they ask. Beyond novelty effects, Shane argues that users who come to trust an AI analyst move to harder questions, so a query mix of 80% simple / 20% hard may shift to 50/50 after launch. He has no fixed rule but suggests four weeks or more and a 5% holdout for a couple of months. In Q&A he describes his team's "shadow mode", running V2 silently alongside V1 on production traffic for realistic quality metrics, which he would build earlier next time; his legal-AI team also leans on causal inference and opt-in beta users because lawyers lose trust fast.

2026-02-20 · open the original ↗

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2026-02-19 · open the original ↗

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2026-02-19 · open the original ↗

• The product's constraints decide edge versus cloud. Murugesan says safety features mostly run on the device, because keeping people safe cannot depend on latency or connectivity. Fuel products and route planning, which he calls more an operations-research problem than machine learning, run in the cloud because they have no latency constraint.
• An internal AI gateway hides model choice, cost and compliance from builders. Samsara's central endpoint fronts OpenAI, Anthropic and Gemini models; an engineer states the use case and constraints, and the gateway's layer of intelligence handles cost along with security and compliance, so no one builds a facade per model. It also provides quick feedback on cost and quality before promoting a feature to production.
• The gateway let a non-data-scientist ship a video-LLM product. A product engineer with little data-science background built a feature that flags drivers misusing assigned vehicles by handing footage to a video LLM with policy constraints, replacing manual review or random sampling. Murugesan says this blurs old engineering stereotypes, and that the cloud dashboard is becoming AI-first to eliminate work rather than for AI's sake.

2026-02-19 · open the original ↗

• Measure one true north outcome and the input levers that move it. The presenter, one of the co-hosts, opens with a cautionary case: daily active users rise 30%, the team builds on it, and three months later the gain turns out to be bot traffic. The fix offered is the "true north plus inputs" framework, which the presenter attributes to Airbnb, Slack and Netflix: the true north is the outcome, as close to customer value as can be measured, and inputs are the levers teams pull. Shane adds that you should start from what the person is trying to do with the product imagined away, then move "south" until there is a signal you can measure or build.
• Good true north metrics capture delivered value, not attendance. Daily active users, revenue and signups are given as bad choices, and Airbnb's nights booked (traveler housed, host paid), Slack's messages sent and Netflix's hours watched as good ones; Netflix is said to have moved there from subscribers added. A seven-test checklist splits strategic tests (reflects customer value, leads to business outcomes, hard to game, one rallying number) from practical ones (actionable, understandable, measurable). Ratios such as delivery success rate, and cumulative totals that can only rise, are ruled out as true norths.
• Decompose the true north into breadth, depth, efficiency and frequency. Retained signups breaks down into traffic × signup conversion × retention, each owned by a different team. Shane adds that the true north exposes gameable inputs such as bot or one-off paid traffic. In the B2B exercise, a project-tool PM's monthly active users becomes team projects completed per week, with inputs of active teams with a shared project, tasks completed per project, time from project creation to first task, and weekly active collaborators. In the food-delivery poll the answer is orders delivered per month.
• Expect to revise metrics, and locate inflection points in the data. In Q&A the hosts say a new metric tends to change every couple of weeks for the first one to three months; the time window should follow the product's natural cadence, from daily to six-monthly; and there is no fixed cap on how many metrics to track, so prioritize by leverage. Shane describes regressing an input (AI output quality) against a true north (time saved) to find the point where gains accelerate or flatten, which he treats as the "aha" milestone worth setting a goal on.
• The hosts say a new model made their AI analyst workable. They credit Claude Opus 4.6. The metric-coaching prompt demo was skipped for time; instead the hosts showed decks from their Claude Code AI analyst — on a co-host's real estate business, Hawaii tourism data, and an interactive HTML campsite deck with presenter notes — which they say involved no hand-written code or slides. Shane describes a repo of about 60,000 lines and about 20 agents, and an overnight test in which a single markdown "seed" file had Claude Code rebuild a data team for a new use case. His hedged view is that analysts will move toward building systems of agents and toward technical-PM roles.

2026-02-19 · open the original ↗

• Analytics leaks value between the dashboard and the decision. Nate Nichols, vice president of product at Tableau, describes analytics as a funnel where only the last stage, a better or faster decision, produces money, and calls the dashboard-to-decision step the lowest-conversion stage, with roughly 30% of an organization looking at data regularly as a long-standing ceiling. He argues the data team's job is driving outcomes rather than delivering requested dashboards. His own position, not necessarily Tableau's, is that success should be measured on outcomes, though he says nobody has found that metric.
• Pull semantics out of dashboards so humans and agents agree. Nichols says Tableau has moved definitions into a metrics or semantic layer, so each user can slice a metric their own way and the system can monitor on their behalf. His forward-looking third point is that agents working around the clock must see the same numbers, governance and data models as humans, or "that's like chaos".
• Trust is earned by clarifying, showing reasoning and testing. Nichols says LLMs are "dying to be useful" and will silently pick a meaning for "best salesperson", so Tableau asks first, shows the reasoning and data model, and offers analysts a testing center that proposes 20 questions per data model for approval, plus an AI-readiness score. He cites Tableau Pulse, whose Q&A answers only from deterministic, pre-computed insights rather than generated SQL, as among the first gen-AI features many customers got through security and legal review.
• The demo ran from question to action in one conversation. On Tableau Next, which runs on Salesforce's Agentforce, the agent asked what "lowest performing" meant and said it excluded smart home per the organization's preferences. A beta proactive alert explained a 4% drop by a sharper fall in California. A Slack canvas showed live metrics and every conversation about win rate, and an agent was told to have reps book executive business reviews. Nichols says tracking whether such actions work, his example being a 14% lift in close rate, is "not quite there yet".
• Analyst roles will split between plumbing and the business. Nichols expects some analysts to move toward data engineering and enriching the semantic layer, and others to embed with business teams and coach the agent on team-specific context. He cites a Tableau Next customer that went from 10% to 80% weekly analytics engagement, and frames AI as an "Iron Man suit" with the analyst's judgment still in charge.

2026-02-18 · open the original ↗

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2026-02-18 · open the original ↗

• Fuel is a top fleet cost, so efficiency gains run to millions. Murugesan says he regularly meets customers spending north of 100 million a year on fuel, and that flagging poor drivers and mechanical issues, then prioritising fixes, quickly ladders up to millions of dollars in savings.
• Fuel theft proved a global problem, not a regional one. He cites a study he read recently putting fuel stolen or siphoned from vehicles at $130 billion; the host assumes that is annual. Customers first raised it in regions where labour is cheap and fuel prices are similar to elsewhere, but when Samsara applied its detection to data from customers worldwide it found the problem everywhere.
• Noisy fuel sensors defeated heuristics and required a statistical model. Competing solutions need extra sensors installed, which does not scale across vehicle classes worldwide. Samsara's first heuristic, a rolling median to smooth fuel-level data, hit diminishing returns because of noise, so it moved to a statistical model that smooths the variance and improves the hit rate on fuel anomalies.

2026-02-18 · open the original ↗

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2026-02-17 · open the original ↗

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2026-02-17 · open the original ↗

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2026-02-17 · open the original ↗

• The platform's real-world data volume rivals almost anyone's. Murugesan, VP of Engineering, says the platform covers 20 trillion data points and 90 billion miles a year, and relays the founder's remark that Samsara probably collects more real-world data than anybody except perhaps Uber, where Murugesan spent 7-plus years. Customers in transportation, construction and field services share three goals: safety, efficiency on thin margins, and sustainability.
• Safety AI runs on cheap edge hardware built for no connectivity. In-cab cameras run proprietary vision models with some temporal reasoning to detect drowsiness or phone use; training happens on an in-house Kubernetes-native platform using Ray, which versions models and deploys them for edge inference, and Samsara is trying off-the-shelf tiny LLMs for tasks like voice translation. Hardware is chosen against bill-of-materials cost, and products are built to degrade gracefully off-grid for as long as a week, as for a utility customer stringing power lines where there are no roads.
• Fuel theft needed statistical models, not heuristics or extra sensors. Murugesan cites customers spending north of a hundred million dollars a year on fuel, and a study putting fuel theft at $130 billion, which the host takes as annual. A rolling-median heuristic failed against noisy, uneven sensors, worsened by dual truck tanks read by one sensor, so Samsara uses statistical models; first built for one low-labour-cost region, the detector found theft to be global.
• Cloud handles what lacks latency limits, through a central AI gateway. Fuel and route planning run in the cloud, and a gateway fronts OpenAI, Anthropic and Gemini models with cost, security and compliance handled centrally plus feedback loops on cost and quality before production. A product engineer without data-science background used it to build video-LLM detection of drivers misusing assigned vehicles.
• Customer feedback loops, not demos, filter the roadmap. Murugesan's tests are the customer's pain, operational leverage and willingness to pay. A UK customer's low-bridge fines led to commercial navigation that routes by vehicle type (a 20-plus-foot trailer) and enforces fleet rules such as allowing turns to one side only, asked for by school-bus and paratransit operators; route planners rejected "optimal" routes that erased human context, so Samsara now suggests improvements to existing routes, which he calls assistive intelligence. He sees quantum computing as a route to exact routing but says truly self-operating systems are not close.

2026-02-16 · open the original ↗

• Production inference needs a system around the model, not just a forward pass. Falcon says PyTorch Lightning, built for training and fine-tuning, can run frozen forward passes, but real inference also needs pieces such as a RAG database, and teams using FastAPI must write their own batching and streaming. Lightning's open-source LitServe is pitched as "PyTorch Lightning for inference": compose multiple models with full control while batching, streaming and security come built in.
• The tool sits a layer below opinionated LLM serving engines. Falcon describes vLLM and SGLang as what you would get by building a highly optimised, opinionated LLM server (batching, streaming, KV caching) on top of something like LitServe; if you just want to hit run on a CLI, LitServe is not that tool. He says it is, to his knowledge, the only tool of its kind, that all of Lightning's own inference runs on it, and that customers' consumer products with millions of users do too.
• The remaining gap is talent, which Lightning targets with vibe-coding agents. Of two gaps, he says GPU supply is now solved; the other is data scientists used to scikit-learn and NumPy who need to do inference or fine-tuning, and Lightning's Studios now offer vibe-coding agents that build what they describe.

2026-02-15 · open the original ↗

• The company came from open-source traction, not a plan to found one. Falcon, who had just sold a company, says he wanted to stay an academic, but PyTorch Lightning took off and VCs began emailing; after flying out on a Thursday he had about 10 term sheets by Monday. His advisers, told he kept getting dragged into the project, replied that his research was going slowly anyway.
• Maintaining a popular library was eating his research. He describes merging pull requests nightly and implementing features he did not use himself, such as backpropagation through time for RNNs, and says he almost shut PyTorch Lightning down one September because it was distracting.
• The lab's edge was turning the world into maths. He means FAIR, Facebook's research lab. Falcon says what he did then, pre-training what are now called world models, was largely theoretical: choosing loss functions, regularisers, embeddings and distributed strategies, and translating a scene (walls, gravity, lighting) into equations. He calls this "speaking" maths as opposed to reading it, says most pre-training today is the engineering side, and admits he has lost some of the skill.
• Do a PhD only if you love science itself. His advice is that anyone treating a PhD as a career step will "hate your life" about two years in.

2026-02-14 · open the original ↗

• The library began as an undergraduate's private "research lib". Falcon started PyTorch Lightning around 2015 at a Columbia neuroscience lab, before PyTorch existed, decoding images from retinal recordings (a Stanford partnership that showed ImageNet images to monkey retinas) with autoencoders and GANs, first in Theano. The aim was to try many ideas without rewriting code, and a postdoc, now a Stanford professor, pushed him to formalise it into a reusable harness. The host puts downloads at over 300 million; Falcon says close to 400 million.
• Multi-GPU training turned weeks-long experiments into hours. One ImageNet epoch took 21 days and the autoencoder needed hundreds, with a NeurIPS deadline months away, so Falcon built four boxes with four GPUs each (1080-class cards) and rewrote the code in TensorFlow for multi-GPU support. The result was his first and only NeurIPS paper, on which he was not first author; he later moved to PyTorch because it mapped more directly onto the maths of the probabilistic models the lab was writing.
• His earlier startup used RL to coach humans helping students win financial aid. At the company he founded and sold, staff texted with low-income students across 60,000 customers, and a UI suggested the next question by treating the conversation as a game whose reward was financial aid. Without LLMs it ranked a fixed list of questions probabilistically, and Falcon says it got students about $60 million in aid; he called the approach "bot-powered humans".

2026-02-13 · open the original ↗

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2026-02-13 · open the original ↗

• The agent-only social network's headline growth was largely unverified. The host reports that Moltbook, launched on January 28 by Octane AI CEO Matt Schlicht, lets only autonomous agents post, comment and upvote, and claimed over 1.5 million registered agents within days. Those figures were self-reported; cloud security firm Wiz later found only around 17,000 human owners behind them, an 88-to-1 ratio, and no rate limiting, so anyone could register millions of agents with a simple loop.
• The underlying agent framework has real tools and deep system access. OpenClaw, by Austrian engineer Peter Steinberger, is a self-hosted personal assistant reached through WhatsApp, Telegram, Discord or Signal; it runs shell commands, manages files, automates the web and keeps persistent memory in local markdown. It was first named after Anthropic's Claude, renamed Moltbot after Anthropic raised trademark concerns, then OpenClaw.
• Agents' emergent "culture" looks more like mimicry than mind. Agents formed a lobster-themed religion, Crustafarianism (one user's agent built its site, scripture and 43 prophets overnight), a "Claw Republic", encrypted channels and markets in "digital drugs", meaning prompt injections. Elon Musk called it the very early stages of the singularity and Simon Willison called it complete slop while granting agents have grown more powerful; the host leans to mimicry of sci-fi tropes in training data, and cites Columbia professor David Holtz's finding that 93.5% of comments got zero replies, suggesting agents perform for an audience rather than listen.
• The vibe-coded site exposed its whole production database. Schlicht said he wrote no code himself; on January 31 security researcher Jamieson O'Reilly found an API key in client-side JavaScript and no access controls, giving unauthenticated read and write access. Over 1.5 million API tokens, about 35,000 emails and private agent messages, some holding plaintext OpenAI keys, were exposed, 404 Media confirmed any agent account could be taken over, and researchers said the fix needed two SQL statements. The site was patched within hours and keys reset.
• The host's lesson: security basics outweigh agentic hype. He separates the platform breach from OpenClaw's built-in risk, which CrowdStrike, Cisco, Palo Alto Networks and Bitdefender have documented, and notes Andrej Karpathy went from marvelling to calling Moltbook a dumpster fire and warning against running OpenClaw on personal computers. He recommends a dedicated box or cloud instance (pointing to Lightning AI, where he holds a fellowship), and least privilege, sandboxing and code audits.

2026-02-13 · open the original ↗

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2026-02-13 · open the original ↗

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2026-02-13 · open the original ↗

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2026-02-12 · open the original ↗

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2026-02-12 · open the original ↗

• Debt-funded neoclouds face a mortgage-crisis-style squeeze. The host cites a McKinsey article calling neocloud economics fragile and at risk of repeating early-cloud consolidation. Falcon says many neoclouds borrowed against customers, often startups, not defaulting, and the public ones borrowed against market caps; with neocloud prices down about 60% or more in the last 6 months, he expects lenders to call loans.
• He predicts the sector will need an outside rescue. He calls for something like a TARP programme. Falcon likens it to banks overleveraged before the Federal Reserve bailout and says a big player will probably have to support the ecosystem; this is his speculation, and he declines to say who.
• He says the merger avoids those weaknesses. Lightning AI has merged with Voltage Park. By Falcon's account Voltage Park carries no debt, its CEO (now on Lightning's board) is a former finance executive focused on profitability, and most customers are enterprises with strong credit, sold through security work Lightning has done for six years. He calls the result a hybrid cloud that still partners with AWS and others, with fast-growing, high-margin revenue, and expects to take on debt in the next few years in a controlled way.

2026-02-12 · open the original ↗

• A good analytical question passes three checks. The presenter, one of the co-hosts, sets out a question quality checklist: decision-tied (what would you do with the answer?), data-grounded (can data actually answer it?) and specific (bounded, with a clear point where it is answered). The anti-pattern is "it would be interesting to know", and the lesson's premise is that, for humans or AI, question quality sets output quality.
• Sharpening a question means naming the decision and a threshold. The worked progressions run "why are users churning?" to "which first-30-day behaviors correlate with churn so we can trigger interventions"; "how are users behaving?" to "which three features correlate with 30-day retention above 50% for last quarter's users, and should we surface them in onboarding?"; and "how's marketing performing?" to "which channels exceeded our 1.5x return-on-ad-spend target last quarter, and should we shift budget?". An attendee points out, and the presenter accepts, that these assume the company agrees on what churn means.
• Even exploration needs a direction. To the pushback "I'm just exploring", the presenter offers reframes: "what patterns would change our next sprint priorities?", "what would I need to see to recommend we invest in X?", and "what's worth investigating further is your decision". Undirected exploration is "a fishing expedition"; directed exploration is "discovery with purpose".
• Use AI as a question coach, not a grader. In a ChatGPT demo the presenter pasted a coaching prompt and a vague request about a spike in support tickets; the AI walked through each check in turn, and the answers supplied (high-priority bug fixes, ticket volume by day, a 10% week-over-week spike threshold) produced sharper questions such as which ticket categories drove the increase over the past two weeks. The argument is that a conversation shows why a question is weak, building the skill before delegating to AI.
• Ask the requester what decision their question drives. In Q&A the presenter says that, in their experience, about 60% of the time the requester then concludes it isn't a good question and drops it, which they call high-ROI. Asked by an attendee whether to frame questions differently for humans than for AI, the presenter says the structure is the same but people need to be met where they are; a third co-founder adds that you should explain why you are questioning the question so it doesn't read as resistance.

2026-02-11 · open the original ↗

• Ticket-based BI turns data teams into "human APIs". Ramy, co-founder and CEO of Querio, recalls that at Amazon a ticket to the central BI team averaged six weeks, while Ramy, a technical product lead who knew the data flows, could answer in an hour. At that scale, Ramy says, the data team no longer understood how the business mapped to the data, and template queries carried miscounts. Ramy and a host agree the fix is to empower the person closest to the business or to embed the data person deeply.
• The bet is transparent BI-as-code fed by a curated handbook. Ramy describes four components — a notebook, agents, context/governance and a front end — with SQL in, table out and Python visuals, "no black box". Ramy likens context to the best analyst on earth on their first day, rereading one handbook of joins, catalog, metrics and quirks for every question. In the Dunder Mifflin demo, a vague "compare 2024 versus 2025 sales" prompt noticed data only ran through September, compared January 1 to September 15, fixed its own syntax error and ended with key insights.
• AI automates the easiest part of data work, not the job. Ramy argues board and exec metrics still need manual care and executive trust, and that definitions, context upkeep and data cleaning remain the hard part. The example is a telecom customer whose gigabytes column stores unlimited plans as negative one, which "no AI model on earth is going to know". Ramy also claims zero-shot text-to-SQL on a decent schema was only solved about three months before recording.
• Prove value in a month on half the tickets. Querio's pilots, Ramy says, use about 10 eval questions spanning tables that need specific knowledge, an agent first pass at the catalog and weekly context updates. The goal is that marginal effort solves half of incoming tickets, tried by the data person before the team gets it. Because most data roles go unfilled, Ramy predicts unfilled roles simply won't need filling rather than people losing jobs.
• Self-serve users ask more and learn how data works. Ramy reports that users don't actually care about dashboards, ask far more questions, and come to understand table choice and many-to-one relationships, while data people shift to reviewing drafts users send them. Ramy argues that investing in the semantic layer now pays back directly in less busywork, and that analysts should learn how the business flows into the production database.

2026-02-11 · open the original ↗

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2026-02-11 · open the original ↗

• The merger pairs full-stack AI software with owned GPU supply. Lightning AI has merged with Voltage Park. The host, who discloses a year-long fellowship at Lightning AI, gives the combined figures: 400,000 developers and companies served, over $500 million in ARR gained in under two years, and 35,000 GPUs, which he says makes it the third-largest neocloud. Falcon notes these are the two companies' numbers combined and likens the pairing to a Mac with macOS.
• Retrofitted CPU clouds push AI teams into stitching single-purpose tools. Falcon calls AWS premium-priced and built for CPU workloads, so enterprises and startups buy specialised point products and stitch them together, adding firewall and security work for enterprises and operational overhead for everyone. Lightning's customers wanted its full-stack software but found compute expensive; it had never sold compute, with customers bringing their own cloud.
• Neoclouds outperform on GPUs, but few met enterprise stability needs. Falcon says neoclouds are GPU-first and work directly with Nvidia on InfiniBand and storage, and that there are hundreds; Lightning partnered with the top seven or eight, but enterprise customers, Cisco among them, struggled to adopt them. Voltage Park's responsive "white glove" support won and retained enterprise customers, which he says convinced him they were serious.

2026-02-11 · open the original ↗

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2026-02-10 · open the original ↗

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2026-02-10 · open the original ↗

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2026-02-10 · open the original ↗

• Compute, not demand, was the growth bottleneck the merger fixes. Falcon, CEO of Lightning AI, says Lightning grew revenue at least 30–40x in the past year without the merger yet lost deals for lack of GPUs, while neoclouds offered little software beyond Kubernetes. The combined company with Voltage Park, per the host, serves 400,000 developers and companies with over $500 million in ARR gained in under two years and 35,000 GPUs; Falcon says it was standing up its seventh data center and built Cursor's training cluster. The host discloses a year-long fellowship at Lightning AI.
• Inference is one primitive of a coming AI cloud, not the whole market. Falcon compares today's inference boom to S3 in the early 2000s: storage looked like a market but became one primitive in what was later called the cloud. He expects vector databases, training infrastructure, Kubernetes, agents and uncreated primitives to bundle the same way, and claims Lightning has the first such full-stack AI neocloud. He also rejects the "99% of GPU use is inference" framing: at Facebook in 2019, 99% of workloads used one GPU only because multi-GPU tooling was missing.
• Debt-light finance should let it ride out a neocloud credit squeeze. He says rivals borrowed against startup customers and market caps; with neocloud prices down about 60% in six months he expects loan calls and a TARP-style rescue, while Voltage Park carries no debt and Lightning's customers are mostly creditworthy enterprises. Enterprise contracts fund a free tier and the press-release claim of 70% lower AI costs; he says cutting developers off would be more profitable but he wants to support students.
• Studios are persistent cloud machines where agents can run safely. A Studio acts like a laptop in the cloud: Claude Code can run overnight without endangering a local machine, 20 can run at once on different hardware, any editor or SSH can connect, and enterprises can audit and keep sensitive files off laptops. Every Lightning developer codes on Studios, and vibe-coding agents are meant to close the talent gap for data scientists, even spinning up 1,000-GPU clusters.
• The open-source library spreads research-lab scaling know-how widely. PyTorch Lightning, begun around 2015 as Falcon's undergraduate "research lib" and open-sourced at NYU, it has about 500,000 engineering hours behind it by his estimate and close to 400 million downloads. He says Nvidia's NeMo is built on it, AWS has pre-trained LLMs with it and LinkedIn trained a 100-billion-parameter LLM with it; teams needing more control move to Lightning Fabric, then raw PyTorch, which he thinks only a few frontier labs use directly.

2026-02-09 · open the original ↗

• Agents and humans must work from the same numbers. The speaker argues that human–agent collaboration breaks down if an agent queries Snowflake while a person reads an extract and the figures disagree; an agent optimizing marketing spend around the clock, or recommending products to a calling customer, should see the same governed data, security and data models — the same product list a human sees in a dashboard. They predict this becomes "really important over the next few years" as more agents work alongside people.

2026-02-09 · open the original ↗

• AI advances faster than expected, even when you expect that. The speaker offers this as a self-styled "law of AI", pointing to ChatGPT's 3-year anniversary: reasoning, agents, image, video and audio generation and understanding, and runs of eight hours on advanced math problems were not in view three years ago, when people were making limericks and mocking images with extra fingers. They say predicting three years ahead is hard, beyond expecting it to be impressive.

2026-02-09 · open the original ↗

• Data is the last transactional relationship at a company. The speaker likens it to asking someone to type for you: the person doing the grunt work is not the one who needs the insight, so data teams become "human APIs". Their alternative is either to empower the person closest to the business to get data themselves, or to embed the data person so deeply in one area that they hold the context and the back-and-forth disappears.

2026-02-09 · open the original ↗

• Agents are the abstraction that finally lets end users do analysis. The speaker describes Querio as BI-as-code built from the ground up for agentic analytics: each step from machine code through assembly and C++ to Python abstracted further, and agents let a user prompt in plain English while the agent writes the code. Their claim is that this wraps away the database, schema and code that BI tools never solved for end users.

2026-02-09 · open the original ↗

• Small, large and specialised models will all keep a place. The host's premise, from a presentation by Barth, is that Alexa+ orchestrates hundreds of specialised expert systems and an internal AWS agent manages over 6,000 tools through retrieval rather than a stuffed context window. Barth does not pick a side on context length; she expects edge AI to use smaller models given its constraints and many tasks to use larger reasoning models.
• Agent-to-agent communication across companies is still an open problem. After last year's hype around protocols for connecting models to tools and agents to agents, she calls the best approach still a research question with many components unbuilt. Her forecast is that every interaction becomes an agentic AI call, with layers of agents communicating not just within a company but between businesses that trade with each other.

2026-02-09 · open the original ↗

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2026-02-09 · open the original ↗

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2026-02-08 · open the original ↗

• Agents should become digital co-workers that learn the way humans do. Barth says Amazon's AGI lab focuses on "useful AI" and sees the future as multi-agent and collaborative, with agents that learn tasks from interaction, ask for help and are humble. She describes working with a cognitive-scientist colleague, host of the podcast Making a Mind, on translating how humans learn and collaborate into agent design.
• The payoff is agents that need less supervision over time. In her picture a user hands an agent a task, it checks back a few times, learns from those exchanges and then does the task more independently. She expects this to multiply knowledge workers' productivity by offloading dreaded routine work so people can focus on creative, higher-level tasks, done responsibly and reliably.

2026-02-07 · open the original ↗

• Automated QA testing is the first use case where browser agents pay off. Barth says Nova Act customers adopt it first to check that apps still work after each update. PGA Tour uses it to verify its website, down to the weather widget and every button, before tournaments, and Hertz's automated testing of core rental workflows raised its shipping velocity 5x, by her account.
• Inside Amazon, agents generate tests before features are built. The host cites a blog by Barth counting over a thousand generative AI applications built or in development at Amazon. Her example is Amazon Leo, formerly Project Kuiper, which built QA automation across web, Android and iOS, plus tooling that lets product managers create test cases from wireframe designs before implementation.
• The browser came first as the nearest thing to a universal action space. Much human work happens in browser tabs across applications, she says, which makes it a good starting point, though Amazon is also looking beyond the browser. She stresses that the value lies in automating boring, tedious core workflows rather than flashy hackathon demos.

2026-02-07 · open the original ↗

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2026-02-06 · open the original ↗

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2026-02-06 · open the original ↗

• Workers hide AI gains unless leaders reward disclosing them. In a clip from episode 962, Ethan Mollick cites evidence that over 50% of Americans say they use AI at work and self-report a threefold performance improvement on the fifth of tasks they use it for, which he says is hard to verify. Organisations lack processes for it (what is a stand-up for when code is done at once?), and staff who fear firing will not reveal gains, so leadership must set incentives and provide a lab where a prompt that "saves me five hours a day" can be improved and shared.
• Enterprise agents were the year's biggest disappointment. In episode 955, reviewing 2025, Sadie St. Lawrence, founder and CEO of the Human Machine Collaboration Institute, says the gap between agent hype from enterprise vendors such as Salesforce and SAP and what companies could implement was too great. The host agrees there is too much talk relative to impact, blames data silos and security set-ups, and expects deployments to come; his own disappointment was Apple's AI, which she contrasts with using Grok seamlessly by voice in her Tesla.
• Language let human intelligence scale horizontally; AI needs the same. In episode 961, Cisco's Vijoy Pandey says human intelligence scaled only vertically for its first 300,000–400,000 years because knowledge died with individuals, until a cognitive revolution around 70,000 years ago brought language and with it shared intent, shared knowledge and shared innovation. AI so far builds ever-bigger individual geniuses, he argues, and his distributed superintelligence work aims to supply that collective layer for multi-agent human societies.
• Evaluate by task type and by which errors are costly. In episode 959, author Sinan Ozdemir splits LLM tasks into generative (free text or multiple choice) and understanding (embeddings, classification), each needing its own metrics. Precision matters when false positives are expensive and recall when false negatives are, so the first question is what failure costs the human, such as a bad factory part reaching a plane.
• Engineers leave jobs for lack of meaning, not pay. In episode 957, Ashwin Rajeeva, co-founder and CTO of Acceldata, says studies show engineers productive perhaps 3 or 4 hours a day, and that people quit when their work could be done in 2 hours and they find no mission. He tries to give R&D teams freedom to innovate beyond quarterly plans, and hires senior people by testing whether they can turn a business problem, such as designing an e-commerce warehouse, into working code; graduates are a bet on potential.

2026-02-06 · open the original ↗

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2026-02-05 · open the original ↗

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2026-02-05 · open the original ↗

• Agency should grow as trust is earned and verified. Barth traces her own AI-assisted coding from tab completion about one and a half years ago, to agents co-developing features she reviewed line by line, to 2026, when she acts as a supervisor who cannot read every line. What made that shift safe, she says, is verification: solid unit tests, and a move from vibe coding to spec-driven development.
• UI automation will follow the same path, with humans gating key steps for now. She expects a human in the loop today to approve things like a checkout workflow or other important decisions within a task, with agency sliding toward the agent as verifications build trust.
• Side projects are where developers learn what to automate. The host's research says Barth built Summarize Me with Amazon Q in VS Code, turning meetings into animated avatar video summaries. She says she built it for fun, out of frustration with too many meetings and too many AI summaries, and that most developers have similar projects for tedious work such as expense reports or tracking AI news.

2026-02-05 · open the original ↗

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2026-02-04 · open the original ↗

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2026-02-04 · open the original ↗

• Users want plain dashboards, not the data world's vocabulary. Javier, a founder of Index, says that after talking to close to 500 people, there is a gap between conference and investor talk (semantic layers, metric definitions) and what people use: bar charts, sheets, dashboards. Customers keep saying they do not want notebooks and have no mental model for them, so form factor sells. Javier adds that polish matters but, as another founder put it, is "necessary, but not sufficient".
• The next version is chat on the warehouse, with proactive questions. In Javier's account, Index 1.0 is a dashboard plus a SQL data studio with plain-English query writing. The previewed 2.0 suggests questions by scanning a connected source before the user asks, and shows the query, lets the user edit it and previews the underlying table. A demo thread took under two minutes. Javier positions it to sit beside Looker or Metabase rather than replace them, because asking a firm to migrate 300 dashboards is "a huge ask".
• Bad data defeats the model, so human judgment stays. On a cleaned Kaggle NBA dataset the AI reported 219 championships because it confused playoff games with titles, even with reasoning on. It also mishandles dates (UTC offsets, year-to-date returning December) and flips bar charts. Asked what a company needs first, Javier invokes the Pareto principle: data quality, with everything else "a variation" of it. Javier likens the product to the year or two before Waymo was fully self-driving.
• SQL will matter more than ever, but be more abstracted. Javier argues SQL is the lingua franca everything resolves to — Index 2.0 connections to Stripe, Salesforce and HubSpot are all queried as SQL — while users stop caring about it. The durable skills are judgment, understanding goals and metrics, and knowing which questions to ask, without which Javier warns of "analytics slop".
• The next step is acting on data, not just reading it. Beyond proactive insight, which a host also predicts, Javier forecasts tighter interconnection: emailing your top 10 leads straight from the data tool, which Javier says is neither reverse ETL nor the current paradigm. The argument is that this collapses the days-to-months lag between an insight in a deck and someone acting on it, and turns data people into more embedded, action-oriented business roles.

2026-02-04 · open the original ↗

• An agent that only sometimes works is useless in production. Barth says customer feedback put typical demo agents at working maybe 60% of the time, and "an agent that is reliable 60% is 0% useful"; that Nova Act's purpose is to complete workflows reliably 90% of the time or more. It went generally available at AWS re:Invent this past December after a research preview that began in March of the previous year.
• The workflow runs from a free playground to Python code to AWS. With no AWS account, a user gives a website and natural-language steps (her example: find an AI meetup on Luma, click RSVP and fill the form), watches the agent in an embedded browser with its reasoning traces, then downloads the Python script it wrote. IDE extensions with an embedded live preview, added after developers complained about pop-up browser windows, let them customise the script and deploy it to their AWS account.
• Action agents need reinforcement learning, not imitation. Because the model predicts the next action rather than the next token, Amazon trains Nova Act by trial and error in what it calls web gyms: hundreds of replicas of typical UIs such as forms and shopping flows, with thousands of tasks to self-play, much as AI learned chess and Go. Barth says this lets the agent generalise when buttons move or are labelled "draft", "new", "create" or just a pen icon.

2026-02-04 · open the original ↗

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2026-02-03 · open the original ↗

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2026-02-03 · open the original ↗

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2026-02-03 · open the original ↗

• Agents must be near-certain to be worth deploying. Barth, a member of technical staff and developer relations lead at Amazon AGI Labs, says customers report demo agents working maybe 60% of the time, and "an agent that is reliable 60% is 0% useful". Nova Act, generally available since AWS re:Invent in December, targets 90% or more; she says it exceeds 90% on early enterprise customer workflows, evaluated on those customers' own tasks rather than just public benchmarks such as WorkArena and REAL Bench.
• Reinforcement learning in "web gyms" teaches actions that generalise. Imitation learning suits next-token chat models, she argues, but agents must predict the next action, so Nova Act self-plays thousands of tasks across hundreds of replica UIs, with variations as small as light versus dark mode. To stop agents learning shortcuts, success is judged on a verifiable end state, such as the meeting actually appearing in the calendar or a database entry existing, while the path is left free.
• Training model and actuator together avoids "Frankenstein" agents. Barth contrasts stitching a model, orchestrator and browser actuator with Amazon's approach of training "the brain and the body in one", while noting AWS also offers the Strands Agents SDK for teams that want to build their own. Runs execute in the customer's AWS account with step-by-step reasoning traces, next-action predictions, scheduled parallel runs and CloudWatch logs; for passwords the agent hands control back so the secret is entered locally and never sent over the network, an area she says 1Password built on, and CAPTCHAs are handed back the same way.
• "Normcore" agents automate the boring work, starting with QA. PGA Tour checks its site before tournaments, Hertz raised shipping velocity 5x by automating rental-workflow tests, and Amazon Leo generates test cases from wireframes, by her account; the host cites her count of over a thousand generative AI applications at Amazon. She expects QA engineers to stop maintaining brittle Selenium-style or RPA scripts and shift to natural-language tests and reviewing traces.
• Agency should expand as verification builds trust. Barth's coding moved from tab completion to line-by-line review to supervising agents in 2026, made safe by unit tests and spec-driven development, and she expects UI automation to follow, with humans approving steps like checkout for now. Her forecast is agents as digital co-workers that learn from interaction like humans, with every interaction an agent call and a mix of small edge models and large reasoning models.

2026-02-03 · open the original ↗

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2026-02-02 · open the original ↗

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2026-02-02 · open the original ↗

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2026-02-02 · open the original ↗

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2026-02-02 · open the original ↗

• Agentic drug discovery is a cross-vendor, multi-model pipeline. Answering the host's recap of his episode 941 vision, Pandey lays out the stages: an LLM iterates with the user to define the goal and break it into tasks, an AlphaFold-like scientific foundation model explores protein structures, compliance, effectiveness and manufacturability agents filter the candidates over many iterations to perhaps 10 options, and robotic labs run wet-lab experiments before any animal or human trials.
• Passing messages is where the field is today; shared intent is missing. Pandey says AGNTCY, MCP and current work only connect the agents. They also need a common goal (a drug for a specific purpose, price point, population and risk profile) reached through semantic protocols for discovering information, resolving conflicts, negotiating and coordinating.
• Shared knowledge and guardrails complete the stack. Agents must pool, add to, redact and modify knowledge of what has and has not worked; his example is a compliance agent drawing on the past to warn that a promising candidate would be stopped at FDA approval. Above that sit cognitive engines that both accelerate experiments and keep them within bounds, which he calls shared innovation.

2026-02-02 · open the original ↗

• Data quality is the precondition for agentic analytics. The speaker argues that teams told to "use more AI" will find it is not a switch: with bad data agentic analytics will not work, just as ordinary analytics will not. Invoking the Pareto principle, they suggest nearly every other requirement they could name is a variation of data quality.

2026-02-02 · open the original ↗

• AI analytics should move from reactive to proactive, like an analyst. One speaker describes a future where the system surfaces what you need, knows its own limits and asks humans what to do next, leaving people to judge whether it makes sense. Another likens this to an analyst's career: reactive and told what to do, then connected with the team, then proactive — anticipating questions the team would never have thought of.

2026-02-01 · open the original ↗

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2026-02-01 · open the original ↗

• Natural language stays the common denominator while humans remain involved. Pandey expects all future work to happen in "multi-agent human societies" with agents and humans on the same level rather than in hierarchies. Agents can work at machine speed through API calls in ways humans cannot, but with humans in the mix "for a long long long time", agents must communicate in natural language.
• Even with no human in the loop, language persists because vendors differ. Under his definition of superintelligence, agents doing discovery or economically viable work 100% of the time without human intervention, components still come from different vendors and frameworks, so language remains the shared medium.
• Vector-space communication is more efficient but needs universal agreement. Pandey says converting vectors into tokens and language loses a great deal of information, so if everything were standardised and everyone agreed, vector space would be the way to communicate; he does not expect that in practice. His caveat is world models: citing recent work from FAIR and elsewhere, he says tokenization is a big detriment there and vector space the only way, a problem to be solved eventually, though for now his distributed-superintelligence work targets tokens and language.

2026-01-31 · open the original ↗

• Data scientist pay at Facebook climbs steeply with each level. The speaker gives average annual pay of $157,000 at IC3, $250,000 at IC4, $370,000 at IC5 and $515K at IC6; the IC7 figure is cut off mid-clip ("around $750").

2026-01-31 · open the original ↗

• The system around an LLM, not the model, creates the value. The speaker says LLMs were once treated as magical but cannot do everything themselves; the model acts as the brain or focal point for decisions, and the surrounding pieces, which did not get the credit they deserved, are what make it valuable.

2026-01-31 · open the original ↗

• Struggling to understand abstractions made the speaker a good explainer. The speaker says they were always slow and found high-level abstraction hard, through grad school and work as a mathematician, programmer and data scientist, so they had to tell themselves stories to understand things; after several career changes they decided explaining was what they did best.

2026-01-31 · open the original ↗

• Senior data scientists can out-earn many doctors. The speaker puts family medicine and pediatrics at around $250,000 a year, the same as an IC4 data scientist, says staff-level data scientists earn more, and sets high-earning specialists (neurologists, anesthesiologists, surgeons) at around $800,000 against $750,000 for a principal staff (IC7) data scientist at Facebook.

2026-01-31 · open the original ↗

• Jailbreaking and prompt injection differ by who is in the exchange. The speaker names prompt injection, jailbreaking and indirect prompt injection as the top three attacks: jailbreaking is just a user tricking a model into saying something bad, while prompt injection adds a developer instruction (such as "translate the following user input") that a malicious user's input tries to make the model ignore. The clip ends before indirect prompt injection is explained.

2026-01-31 · open the original ↗

• Choosing data engineering starts with knowing your own temperament. The speaker says someone who communicates and partners well and can sit through long sessions of cleaning data may be a good candidate for data engineering.

2026-01-31 · open the original ↗

• Analysts who find the right problem won't be replaced by AI. The speaker argues that data scientists and analysts who focus on what matters most to the business, apply critical thinking and ask the right questions can use AI to get more done and deliver much larger impact.

2026-01-31 · open the original ↗

• Critical reasoning will be the most underrated skill alongside AI. The speaker picks the ability to reason, contextualize and synthesize information with AI, using intuition and creativity rather than relying on AI fully, because human judgment is still needed.

2026-01-31 · open the original ↗

• The clip poses its question and cuts off before the answer. A host asks the others whether their reason for doing the podcast changed over its first year; the co-host who answers gets only as far as "the answer for me is an unambiguous" before the clip ends.