AI

2026-01-05 · open the original ↗

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2026-01-05 · open the original ↗

• This post includes paid content that is not available publicly. See the original source.

2026-01-05 · open the original ↗

• Every country will soon have a sovereign AI strategy. Roese's fifth prediction, for 2026 going into 2027, builds on three models he described the previous year: government building data centers for government, government building for its indigenous industries, and government convening with industry. He calls the acceleration dramatic and sovereign AI now mainstream.
• Sovereign infrastructure will serve far more than government work. Roese's examples are resilient backups of a country's critical-infrastructure AI, and agents certified by a government, such as a "Swiss lawyer" agent, whose knowledge graphs and back-end data live physically under that sovereignty. He expects government's role to grow where AI meets national interests such as critical infrastructure, defense, intelligence and healthcare, and calls the inseparability of government and private sector in AI probably a good thing for pooling resources.

2026-01-04 · open the original ↗

• An agent is a software system, not a feature of an LLM. Roese says one camp treats agentic behavior as a property of the large language model, which he calls not very useful. Dell's camp, which he says many others share, treats an agent as an autonomous software system of which the LLM is one part.
• The LLM supplies world knowledge, communication and much of the reasoning. Roese counts it as the first of four components, large or small, without which agents cannot be built.
• Knowledge graphs supply specialized knowledge and long-term memory. Roese says agentic RAG or, more often, knowledge graphs give agents domain information they can manipulate. Pathways through the graph become skills for repeating tasks and hold state over long periods outside the LLM.
• A tool protocol lets agents perceive external data and act. Roese calls MCP, or a similar interface, the protocol of choice for this third component.
• Agent-to-agent protocols matter most, because ensembles can reach consensus. Roese calls this fourth component, chiefly A2A, the most important. In Dell's studies, he says, agents sharing a common knowledge graph as "truth" (the LLM being "opinion") and told consensus matters ignored a hallucinating member, like a team overruling one person saying crazy things. He predicts this four-part definition will prove enduring, against those who treat the LLM as a "black box of magic".

2026-01-04 · open the original ↗

• This post includes paid content that is not available publicly. See the original source.

2026-01-03 · open the original ↗

• Autonomous agents keep the knowledge layer permanently "hot or warm". The host's premise is a nightly batch job refreshing a knowledge layer on an edge AI PC. Roese says autonomous agents go further: in Dell's tests, ensembles tasked with learning to run a CNC machine or produce a research report started from a batch-loaded knowledge graph. As they worked, they added pathways recording how they got from point A to point B, so the graph never goes cold.
• AI's knowledge layer is new data infrastructure, not just another database. Roese warns against applying ordinary database approaches, because the layer lives in different places and behaves differently. He argues the gains come from deploying knowledge graphs close to the point of presence and making them highly dynamic.

2026-01-02 · open the original ↗

• Agentic AI is overhyped but powerful enough to prepare for now. The host says leading proprietary LLMs can replace humans on 2-hour software development or ML engineering tasks with 50% accuracy, a duration doubling every seven months. He urges listeners to get their data, security and governance ready to use increasingly autonomous machines in 2026 and beyond.
• The podcast's shift to video sharply grew its YouTube audience. The host says changes implemented in August raised daily YouTube watch time tenfold within two months, and subscribers rose from 25,000 a year earlier to over 140,000. His personal highlight was recording with Aurélien Géron, who he says had not done a podcast in a decade, before a live audience at the University of Auckland. Episode 1,000 is due in June.

2026-01-01 · open the original ↗

• A disciplined AI program decoupled revenue growth from cost. Roese says that in the fiscal year Dell reported in March, the first with its four big AI areas running, revenue grew $10 billion while absolute cost fell 4%. That had not happened in Dell's 41 years, and he credits people, process and technology. The host notes Roese's 10-to-1 and 30-to-1 ROI ratios, which feed the "AI ROI flywheel" Roese described in episode 887.
• Insist on hard ROI metrics, and AI exposes work not worth doing. Roese's first principle is to target measurable things like cost, margin or revenue rather than goodwill. He cites a consulting report, he thinks McKinsey's, calling generative AI a "BS work detector": if applying AI tools to a task changes nothing in revenue or cost, the work may not be worth doing. He urges picking targets where improvement makes something material happen.

2025-12-31 · open the original ↗

• MCP is a keeper but not yet enterprise-grade. Roese, Dell's global CTO, says MCP is a tool-and-data-access abstraction, not an interworking protocol like A2A, and that it was built to let public LLMs consume data, so it still lacks security work (OAuth is being added). He calls it "incredibly powerful and incredibly dangerous" because it can do more than users expect, and expects most agents to use it long term.
• Dell wraps MCP in a central controlled environment. Under Dell's internal agentic guidelines, which he says are not yet public, all MCP servers are centralized so the company can impose the controls the protocol itself cannot.
• Disciplined, top-down AI deployment is how Dell avoids slop. Roese says Dell has deployed probably fewer than 30 AI use cases, with ROIs of 10:1 to 30:1, across sales, services, supply chain and engineering, rather than handing a tool to half a million people and hoping. The host brought up The Economist naming "slop" its word of the year at the time of recording; Roese answers that "it's better to do nothing than to do chaos."

2025-12-30 · open the original ↗

• Targeted AI let Dell decouple revenue growth from cost. Roese says that in the fiscal year Dell reported in March, revenue grew by $10 billion while absolute cost fell 4%, which he says had not happened in the company's 41 years; he credits fewer than about 30 use cases with ROIs of 10:1 to 30:1, alongside people and process changes. He adds a consulting-firm report he attributes to McKinsey calling generative AI a "BS work detector": if automating a task changes no revenue or cost, the task may not have been worth doing.
• Most so-called agents are agent washing, not autonomous systems. Roese defines agentic AI as the second wave of generative AI, the digitization of skills, where the human gives intent and validation and stays "on the loop" rather than in it. He doubts customers who claim thousands of autonomous agents, and predicts 2026 is when people actually experience them, first on composite multi-team tasks such as generating a quote or resolving a complex support issue.
• A new knowledge layer belongs near compute, not near primary data. Roese predicts the layer of vector databases, knowledge graphs and agent memory between systems of record and compute will dominate 2026 data discussions. He argues it is persistent and always warm, fed from databases as a one-time, non-real-time event but consumed in real time, and that agents write their learned pathways back into it, so it should sit at the edge or device where the transactions happen.
• An agent is a four-part software system, not an LLM feature. Dell's model is an LLM for world knowledge and reasoning, knowledge graphs for specialized knowledge and memory, MCP for perceiving and using tools, and A2A for agents talking to each other. Roese reports Dell studies in which agents sharing a knowledge graph as "truth" and told that consensus matters ignored a hallucinating member; he says this mitigates hallucination without solving it.
• Governance will be central to every enterprise AI discussion in 2026. Roese says over a thousand jurisdictions regulate Dell without coordinating, and calls external governance "a disaster" that NIST and EU frameworks might partly normalize. His tactic is to avoid highly regulated use cases first, and he treats internal governance, choosing what to pursue, as the counter to slop, citing consultancies sued after staff used AI to generate government work.

2025-12-29 · open the original ↗

• Engineering leaders must stay technical, but not too hands-on. Clemm argues that a leader who codes too much loses time for strategy, people development and org design, while one who is not technical comes across as giving orders from an ivory tower with no reasoning behind them. Side projects fill the gap when the workday doesn't: his LLM search engine Yattle.ai, with a model switcher and query classification that routes queries to models, and an earthquake app the host calls possibly the top one on Android.
• AI makes hands-on use a leadership requirement. Clemm says that telling teams to "go use more AI" without using the tools yourself is why there is a work-slop problem. Leaders who know the tools' limitations can give concrete advice on which tool to deploy, why, and how to handle its gotchas.

2025-12-28 · open the original ↗

• Good context engineers think like constrained-systems programmers. Clemm, who has called context engineering the high-status job in AI right now, draws on his early work in embedded software, UAVs and lossy networks: people used to deciding exactly what data ships under hard limits will do well. His image is a Mars rover payload that travels five or six months and had better contain everything it needs.
• Search and retrieval experience transfers directly to context engineering. He argues that pulling the most relevant snippets out of a huge corpus, as Google has done for decades, is the core skill, along with retrieval and recommender-system work. He calls search the "unsung hero" of AI.
• Context engineering also requires verifying outputs with evals. Clemm says people who are good at defining tests up front and tying them to the inputs will thrive, because whatever goes into the context has to be checked against what comes out.

2025-12-27 · open the original ↗

• Bigger context windows degrade quality well before they fill. Clemm says models advertised at 1 million or 2 million tokens show significant degradation when actually used, filling up effectively at more like 100K to 200K. He cites the NoLiMa benchmark, a harder needle-in-a-haystack test where accuracy falls as the document grows, and research showing LLMs "get lost" across multi-turn conversations.
• Dropbox Dash replaced many tools with one super tool. Clemm reports that Dash's accuracy dropped considerably as tools and their retrieved results were added to the context, so the team moved to a single tool over its content index. He says Cloudflare and Anthropic have blogged similar fixes, such as having the LLM write code to invoke tools or pick from a selection.
• The goal is a context packed with exactly the needed data. Too much context gives bad results, and too little makes the model fall back on its training data, which knows nothing about your workplace.

2025-12-26 · open the original ↗

• Her burnout came from overwork and a misaligned career. Lafeuille says that working in finance in New York after her master's, she worked too much at something that didn't match where she wanted to be in five years. She moved to data science in life sciences, the field she cares about, and hired a lifestyle and fitness coach, and credits the two changes with turning both her career and her personal life around.
• Daily workouts without recovery are overtraining, not stress relief. She was working out nearly every day and not fuelling properly. Her program is built around her work schedule and the activities she won't give up, such as pickleball tournaments: four gym sessions a week, usually Monday, Tuesday, Thursday and Friday, kept flexible.
• Eat enough protein and time carbs around workouts. Her two nutrition rules are one gram of protein per pound of body weight, for muscle growth and recovery, and eating most carbs (rice, potatoes, fruit, sometimes ice cream) before, during and after workouts, to avoid a glucose spike and sluggishness mid-work.
• Rest, especially sleep quality, matters most for avoiding burnout. She aims for at least eight hours of sleep, which means more time in bed, and reads instead of scrolling before bed. She takes five-minute phone-free walks between meetings to downregulate during the day, and before bed spends five minutes writing down everything on her mind, which also leaves her a task list for the morning.
• A master's with built-in company projects builds the portfolio. Her Columbia master's in management science and engineering, run jointly with the business school, placed students on roughly three-month projects as employees of real companies, Louis Vuitton among them. She says that answers the question she is asked most on LinkedIn, how to build a strong portfolio.

2025-12-25 · open the original ↗

• Work slop comes from rushed AI adoption without clear goals. Clemm, responding to a Harvard Business Review report the host raised, defines work slop as plausible, impressive-looking content that turns out generic or hallucinated. He blames competitive pressure, citing reports that almost three-quarters of CEOs feel pressed to adopt AI, and says his first question to customers who say "we need AI" is what exactly they hope to accomplish.
• Treat AI output as a first draft needing human revision. His analogy is a college essay written overnight: you don't hand in the first draft, you reread it, strengthen it and add research, and AI output needs the same checks for quality, signal-to-noise and verifiable facts.
• Better work context, not quick MCP connectors, reduces slop. Clemm calls MCP tools a phenomenal way to get agents running but says they are slow, can't reach all content and use many tokens. Dropbox Dash instead ingests, understands and indexes content into both a lexical index using BM25, which he calls "still the workhorse" for queries like part numbers, and a vector index for semantic queries, for hybrid retrieval.
• Internal evals tell you whether a deployment works. He recommends turning customers' goals and target metrics into test sets that define "what good looks like" and serve as an internal benchmark against which AI deployments are judged.
• Use top performers to train the rest of the workforce. Clemm says about 55% of employees don't know how to use AI, so companies should run training, share-outs and demos led by the high performers who are likely already using the tools well.

2025-12-24 · open the original ↗

• Enterprise search is broken because every app has its own. Clemm says finding something at work means using 12 to 20 separate search bars across apps, and positions Dropbox Dash's universal search as the context layer Dropbox is building for businesses and the first step in many intelligent workflows.
• Multiplayer AI is a teammate that knows you and your team. Clemm contrasts today's "single player" chatbots with AI that understands where you sit in the org and what projects you work on. On the data layer, Dash normalizes third-party content to markdown, extracts text, figures and image content, and builds a graph linking documents, meeting transcripts, Slack and Teams messages and people.
• Collaborative AI applications are the second, more speculative layer. His example is Dropbox's Stacks, where a team gathers URLs, slides and PDFs for something like a board deck in one place, comments on it and queries chat over it together. He says Dropbox and others are starting to explore how multiple people interact with chat agents.

2025-12-23 · open the original ↗

• More context degrades agents, so Dash collapsed its tools into one. Clemm says models advertised at 1 million to 2 million tokens effectively fill at about 100K to 200K, citing the NoLiMa benchmark and multi-turn research. At Dropbox, Dash's accuracy "dropped off considerably" as tools and retrieved results were added, so it moved to a single "super tool" over its content index; too little context, he adds, leaves the model knowing nothing about your work.
• RAG isn't dead; it evolved into hybrid and agentic retrieval. Clemm argues the original single-shot vector lookup has given way to combining vector search with BM25 keyword search, which suits exact matches such as part numbers or function names. It has also given way to agents that retrieve repeatedly, choosing their next fetch from what came back. Coding agents such as Claude Code and Cursor exploring an indexed codebase are his example.
• Multiplayer AI understands a team, not just one user. Clemm calls today's chatbots "single player" and says the effective AI is a teammate that knows your org position and projects. That rests on a data layer in which Dash ingests third-party content, normalizes it to markdown, extracts figures and images, and builds a graph linking documents, messages and people, positioned against the 12 to 20 search bars he says workers juggle.
• Work slop comes from adopting AI without defined goals. Clemm cites reports that almost three-quarters of CEOs feel pressure to adopt AI and that about 55% of employees don't know how to use it. His remedies are to treat AI output as a first draft, build better work context than quick MCP connectors (which he calls slow and token-hungry), write internal evals defining "what good looks like", and have top performers train everyone else.
• Outsourcing every decision to A/B tests breeds organizational lethargy. Clemm says risk-averse market leaders lean on data until conviction disappears, and sees the same risk in people handing decisions to AI. He recalls a fast-moving Uber Eats competitor that turned out to have no A/B testing framework at all, and recommends qualitative customer contact to offset the quantitative data.

2025-12-22 · open the original ↗

• AI output quality can't be checked with rule-based tests. The speaker argues that traditional software testing works because a deterministic function can check whether a goal was met. Accuracy, hallucination or the quality of a legal draft is subjective and has many dimensions, and none of them reduces to a simple automated check.

2025-12-22 · open the original ↗

• AI-powered analytics and AI product evaluation are the next focus. Shane names these as the lab's two themes for 2026 and says the themes recur with every guest. One is AI letting people make data decisions with less reliance on a data person. The other is that evaluating AI products needs different tools, a continuous loop, and input from researchers and subject-matter experts, which he thinks data scientists, analysts and ML engineers are well placed for. He announces a 2026 interview series with the founders and leaders building each kind of product (as of December 2025).

2025-12-22 · open the original ↗

• The clip asks a question and stops before it is answered. A co-host asks whether the hosts' reason for running the podcast changed over its first year. The first respondent says "an unambiguous yes", and the clip ends before giving a reason.

2025-12-22 · open the original ↗

• Living labs expose the unanticipated effects that matter most. Pentland argues that social media looked wonderful at launch and turned darker over time, and that the only reliable way to see such effects is to have a group of people agree to "live in the future". His example is a town trying out an AI transportation system; the Consumer Reports loyal agents project is applying the same approach to AI agents.
• Digital twins let you test designs without harming people. Since you can't run every experiment on people who have lives to get on with, Pentland describes building a model of how people behave, validating that it predicts what is observed, and then testing designs on it. He says falling compute costs and growing data make this feasible, that aggregation can preserve privacy, and that if the twin "does something really stupid, at least you haven't killed somebody."

2025-12-21 · open the original ↗

• Data is now a means of production alongside capital and labor. Pentland cites Xi Jinping as saying it is "capital, labor, and data", and that whoever controls data can manipulate the other two, which he says is where the hyperscalers' power comes from; he adds that Xi "apparently did get it from me." Individuals, he argues, are at the mercy of firms that know much about them while they don't know what those firms know.
• Personal agents can get results without surrendering personal data. Pentland says an agent can query what is out there and ask specific questions without disclosing your age, sex or income, which makes it a chance to "take back your data."
• Data cooperatives can counter corporate concentration, as banks and unions did. He points to mid-1800s agricultural cooperatives that broke the Eastern banks' monopoly when banking was confined to New York, Boston and Philadelphia, later built much of America's electric grid, and evolved into today's credit unions, and to labor unions of the early 1900s. Through the Consumer Reports loyal agents project, pooled questions and answers would let members spot scams, find deals and compete with firms like Walmart.

2025-12-20 · open the original ↗

• Current AI agents can't be trusted to serve their user. Pentland says agents raise the question of whether they serve Amazon or you, misbehave on edge cases, sometimes do illegal things, have weak guardrails, and "get lost" in networks of agents, forgetting their task. The open-source loyal agents project with Stanford Law School aims at agents that fulfil the user's intent legally and reliably.
• AI buddies that direct attention beat AI that decides. He describes a company, mentioned by a CTO on a panel he sat on, that gave every employee an on-premises AI buddy which has read the manuals and newsletters and tracks what other departments do. He says such a buddy takes a good team a few days to build and changes company culture, especially for remote and distant offices. If the AI is telling you what to do, he says, "you have made a mistake", because agents won't be reliable enough to decide for the next several years.
• Deliberation without big voices undercuts polarization. Pentland's open-source deliberation.io works like X without dominant accounts: it visualizes what everyone thinks and adds an AI mediator that summarizes themes in a Socratic style. He argues most Americans agree even on abortion and gun control, and polarization comes from picturing "the crazy guy with five million followers." He reports that in experiments people liked the AI summaries twice as much as human ones, even with a cheap model.
• Residents asked for AI to defend them against the city. In a deliberation exercise with residents of Washington, DC, on AI in city government, Pentland says what busy residents wanted, unexpected by the experts, was personal AI to navigate regulations, fill out forms and find opportunities.

2025-12-19 · open the original ↗

• This episode is a holiday greeting, with no substantive content. The host and the show's staff, including its founder, podcast manager, partnerships manager, media editor, writer, researcher, shorts editor and thumbnail designer, each introduce themselves and wish listeners happy holidays, with a promise of more data science, machine learning and AI content in the new year.

2025-12-18 · open the original ↗

• Today's AI could fail society the way Soviet planning did. Asked by the host whether the parallel holds, Pentland says "absolutely", for the same reasons: bad data, failure to account for fast change, and no attention to how people actually work.
• Humans are social foragers who mostly copy others. Pentland says nobody reads manuals; people watch others and ask a question or two. He cites Daniel Kahneman's estimate that probably 95% of what we do comes from the culture around us, which he warns "doesn't mean it's right", with the remaining 5% being what makes you you.
• Systems that treat people as individuals produce echo chambers and loud voices. He argues social media matches people on similar feature vectors and ignores community, and its follower-and-advertising model means only the loudest, most extreme voices get heard. He says his group has shown "very definitely" that this is where polarization comes from, and that removing big voices reveals broad agreement, in corporate decision systems as well.
• Watching other people beats following the equations alone. In experiments with traders handling $10 million a day, Pentland says those who paid attention to other traders reliably did better, because people catch the edge risks and changing conditions that models miss. He reads 2008 the same way: models with unnoticed tail risk nearly took down the system, while those also watching human behavior came through.

2025-12-18 · open the original ↗

• Recording is the easy part; editing and distribution eat the time. The three Data Neighbor co-hosts, who met at Nextdoor, say that even a rough edit or a bad thumbnail takes longer than it should. Shane, who does the thumbnails, says no one warned him about the YouTube work. A co-host says the weekly back-office work of publishing to Spotify, Apple, Substack and LinkedIn takes far longer once it compounds.
• Busy, well-known guests say yes if you are consistent and courteous. A co-host says a prominent statistics educator agreed to appear when the show had nothing to show, and that they spoke to 10 CEOs and co-founders in the month before recording, some from large companies. A co-host says the free advice guests give would otherwise cost thousands.
• The podcast's purpose shifted from helping pivoters to learning AI. The co-hosts started out to help people move into data, but a few episodes in they decided the AI shift was faster and more consequential than was being discussed. They say they ran out of their own knowledge in about three episodes and now use the guests to upskill themselves. Shane's turning point was an episode on building products with Replit, whose guest teaches paid courses on it.
• Three co-hosts was a deliberate hedge against burnout. A co-host says the odds of one person burning out or quitting are high, so two is better than one and three lets them rotate. Shane adds that each episode has an informal lead and a support, so the second host can always fill a lull in the conversation.
• At principal level the job reaches beyond your company. A co-host who is at principal level describes a ladder: a junior is handed work, a senior IC tells the manager what the team needs, staff works across teams, and principal must bring the industry's practice in and push the company's out. A weekly podcast, the co-host says, forces that outside contact. The co-hosts also credit it with more visibility, more inbound recruiter messages and more structured speaking, which one co-host estimates has improved 10x to 20x.

2025-12-17 · open the original ↗

• AI fails from bad models of society, not bad algorithms. Pentland, whose book is Shared Wisdom, says his decades of measuring how people interact, with wearables, tone of voice and movement, show that systems designed as if people were logical engines make "bad mistakes"; the system to design is AI plus people plus society.
• The Soviet Union's optimization-based planning failed on data and change. He says the first wave of AI, optimal resource allocation through linear constraints, still the most common computation in the world, was used to run the Soviet economy. People lied about the numbers, conditions changed, and rare events broke the models, and he says this is thought to have been a major contributor to its collapse.
• Expert systems optimize for the average and hollowed out communities. Pentland says rule-based systems now run corporate approvals, audits and the tax system: cheaper, but inflexible, and worse the further you are from average. He links the centralization they enabled to the loss of local clinics and local banks and to "bowling alone."
• Machine learning repeats the problem by looking backward. It needs the right data and lags reality, which he says produces social-media echo chambers and decisions "two heartbeats behind" that can kill a company. Systems must be designed around users, buyers and bad actors alike.

2025-12-16 · open the original ↗

• AI fails society through bad models of people, not algorithms. Pentland says the Soviet Union ran its economy on early optimization AI and was undone by lied-about numbers, changing conditions and rare events, which he says is thought to have been a major contributor to its collapse. He sees the same failures in expert systems built for the average and in backward-looking machine learning, and answers "absolutely" when the host asks whether today's system could fall apart the same way.
• People are social foragers, and big voices drive polarization. Pentland cites Daniel Kahneman's estimate that about 95% of what we do comes from the culture around us. He says his group has shown that follower-based social media amplifies extreme voices and causes polarization, and that traders handling $10 million a day who watched other traders reliably beat those following equations alone.
• AI should direct attention and advise, never decide. His examples are on-premises "AI buddies" that read the manuals and track what colleagues do, which he says a good team can build in days, and the open-source deliberation.io, which visualizes everyone's views without big voices and uses an AI mediator. He reports people liked its AI summaries twice as much as human ones, and that Washington, DC residents wanted personal AI to "defend them against the city."
• Communities need data cooperatives to counter corporate data concentration. Pentland cites Xi Jinping calling data a means of production alongside capital and labor, and points to agricultural banking cooperatives that broke the Eastern banks' monopoly, built much of the US grid and became credit unions. Personal agents that share questions and answers rather than personal data, as in the loyal agents project with Consumer Reports and Stanford, could let members compete with firms like Walmart.
• Govern AI through open audit trails and standards, not pre-approval. Pentland argues that AI queries cross borders, so rules must be globally consistent, and proposes logging what AI does, to whom and with what data, as is done with money, with encryption or knowledge proofs against faked logs. He offers the UL testing cooperative as the model, says European regulators at the Club de Madrid called this "too Anglo-Saxon", and cites a California law requiring big labs to publish their safety practices.

2025-12-15 · open the original ↗

• Making an agent deterministic often turns it into a Python script. Li, walking back his tweet that agents are "just Python scripts" as too scathing, says he is very pro-agent. When he wants something done exactly his way, though, an LLM's answer space is too wide, and at large scale even a 5% chance of a weird answer can be costly.
• Let the LLM decide whether to trigger; keep execution deterministic. Li's recent lesson is that the LLM should make the decision to run a workflow and the workflow itself should run deterministically, which he finds a good balance. The host adds that lowering temperature and frameworks for testing many responses are other ways to rein in variability.

2025-12-14 · open the original ↗

• Automating a workflow fails if nobody involved has mastered it. Li describes a side project, launched around the GPT image release, to auto-generate ads for founders to run on Meta's ad manager. It produced ads, but business owners told him they were bad, and he had no eye for good creative despite knowing the OpenAI APIs and Python.
• Do the work manually first, or partner with someone who can. Li concludes that the right setup is a person with intuition for what is good in the loop, either someone with both domain and tech skills or a partnership, and that you should prove you can get a good result by hand before automating. His ad-industry background in measurement, he notes, did not transfer to producing creative, unlike forecasting and time-series work he is confident automating.

2025-12-13 · open the original ↗

• This upload holds a different clip than its title names. The video posted as "How a Data Scientist Built an AI to Improve His Dating Life" is a copy of the 2025-12-14 clip on why AI automation fails without mastery: both run 3:26, their captions are identical, and neither mentions dating. Jeff Li's dating work is covered in the synopsis of episode 947, dated 2025-12-09.

2025-12-12 · open the original ↗

• Granite 4.0's Mamba hybrid cuts long-context memory dramatically. Dell distinguished engineer Tyler Cox (from episode 939) explains that IBM's open-source Granite 4.0 H models use a 9:1 ratio of Mamba state-space layers to attention layers and no positional embeddings, and are trained on samples out to 512K context and validated to 128K. He cites IBM's release notes: eight 128K sessions on a 3-billion-parameter model use about 15 GB of memory against about 80 GB for a pure transformer. He also says Granite ranked in the top five on the Berkeley Function Calling Leaderboard v3 at the time of recording.
• Zero trust for agents needs task-based access, parsing and sandboxes. Cisco's Vijoy Pandey (episode 941) says just-in-time permissions require identity providers and authorization servers that support task-, tool- and transaction-based access control (TBAC), semantic parsing of agent communication to identify the task, and ephemeral sandboxed runtimes. His analogy is being allowed to withdraw only $10 from a shared safe for ten seconds. Trust comes from a directory of agents with reputation scores, fed ideally by ongoing evaluations rather than crowdsourced ratings.
• Keep core metrics curated; explore new ones with AI first. A Fabi.ai co-founder (episode 937) acknowledges the fear that AI will reinvent a metric every time someone asks. He argues the data team should still own fewer, curated dashboards for north-star metrics like churn, ARR and retention, while AI lets a data scientist and product manager explore hypothetical metrics, such as activation, for a month or a quarter before building them into the warehouse.
• Generative AI products that disempower users become one-hit wonders. Santa Clara University professor Maya Ackerman argues that people return to products where they feel they are doing something. She credits ChatGPT's success to power users who collaborate with it, some reporting richer vocabulary, and warns that optimizing AI to replace workers collapses the economy if there are no human workers left.
• AI tuned as an all-knowing oracle suppresses creativity. Ackerman frames creativity as a search over vast possibilities, which machines explore well. She says sci-fi has convinced investors to build oracles that give the safest, most expected answer, and argues that her three-person team at Wave AI built divergent tools, so OpenAI, Microsoft and Google could too, but their goal is "to replace search."

2025-12-11 · open the original ↗

• Show a company you can solve its problems, not just interview. Li's "briefcase technique", which he used to get into DoorDash more than five years ago, is to work out a company's biggest pain points and spend at least a couple of days writing a detailed doc on how you'd solve them, then send it to the hiring manager. He says it won't always land, but when it does the manager is already sold.
• Generative AI may make such pitches look like spam. The host says he gets 30-page illustrated cold-outreach PDFs about weekly for both his businesses and assumes they are agent-generated, though he concedes nobody has sent him a job pitch of that kind. Li agrees AI pitches are easy to spam, but argues that one showing genuine, specific understanding of the recipient's problems would still stand out.
• A rare intersection of skills got him hired at Netflix. Li says he was rejected by Netflix multiple times, then hired without any tactic because his Spotify work combined ads and forecasting just as Netflix launched an ads business that needs forecasting. His advice is to keep trying after rejection and to build niche expertise at an overlap of industries and skills that few others have.

2025-12-10 · open the original ↗

• Enterprise research is set by a north star and product needs. Shelby Heinecke, who leads a research team at Salesforce, says it is not academia. Research across the org aligns to a north star the company calls "enterprise general intelligence": getting agents to perform real enterprise tasks, which she says they cannot yet do despite impressive demos. Road maps are also built jointly with product groups such as Data Cloud, and her team's role (model building only, or deployment too) varies by project.
• Crisp scope and an agreed eval set carry AI past demo. Her example is a set of small models shipped to Data Cloud customers a couple of months before recording to unify ingested customer records. They replaced a limited rules-based approach and decide, for example, whether two records are the same person. The scope was narrow, the evaluation set was built with PMs before any work began, and small models were chosen for speed rather than capability. She says training the model only gets you a demo, and ethics, security, legal, PMs and product engineers must all be brought in to ship.
• Research roles need proof you can create new things, not a PhD. Heinecke says a PhD is one common way to show the skill. A traction-winning open-source repo or a substantial side project also counts, as does undergraduate work with a professor's group. A co-host adds that two-week side projects teach a skill but three- to six-month ones mimic the job. She also urges early-career people to network at academic conferences (NeurIPS, ICML, ICLR) and industry ones, and says it gets easier with practice.
• Technology is the means; the customer goal is the constant. She says her team stays adaptable by trying each new model against projects, but there is no fixed solution beyond keeping the customer as the north star. She has real cases where older models work better. A co-host says they now repeat the data team's mission every other week so people do not chase the newest model.
• AI teams will turn interdisciplinary, and agents will become embodied. Heinecke predicts domain experts will join ML teams, giving feedback for reinforcement learning, designing training data or annotating. A co-host notes their legal tech company keeps lawyers in-house for labelling. She expects agent benchmarks (Salesforce has open-sourced a CRM benchmark) to drive agent improvement over the next 6 to 12 months, and agents in robots and drones to reach market in a year or two.

2025-12-10 · open the original ↗

• Time-series foundation models haven't yet beaten Netflix's simple approaches. Li says his team tested foundation models including Chronos, TimesFM and one from Nixtla whose name he forgot, and none beat their simple internal methods. He would adopt one that proved significantly more accurate with a reasonable business case, and expects them to improve, but explainability remains an obstacle.
• Operational forecasts prize accuracy; strategic forecasts need interpretability. Li credits the split to a forecasting course he took. Operational forecasts, such as predicting where Amazon deliveries go for fulfilment, can use deep learning if it proves more accurate, while strategic forecasts such as FP&A projections drive decisions, so planners must know the assumptions behind them.

2025-12-10 · open the original ↗

• AI teams will become interdisciplinary rather than model builders alone. The speaker predicts that deploying models to customers and discovering use cases will bring together model builders, people working on discovery, and domain experts who help fine-tune or train models for their domain.

2025-12-10 · open the original ↗

• AI projects reach production with crisp scope and an agreed evaluation set. The speaker describes a project scoped tightly to a known data situation and the fields customers ingest. An evaluation set reflecting real customer data was defined with PMs before any work began and was agreed by everyone, including the product owners. The speaker calls these two the main ingredients of an AI project that makes it to production.

2025-12-09 · open the original ↗

• At scale, build once and reuse instead of hacking fast. Li, a Netflix senior data scientist formerly at Spotify and DoorDash, contrasts zero-to-one work, such as starting Spotify's podcast ad business, where you move fast and ignore model versioning and schemas. At scale you need robust naming conventions, repeatable frameworks and model ops such as auditing and alerting, so that work done once benefits every model.
• Stakeholders need interpretable forecasts, so simple models still win. Li calls the biggest business pitfall junior data scientists' urge to use LSTMs and transformers when stakeholders want to know why a forecast says what it does, which keeps univariate models, additive models and GLMs essential. His team tested time-series foundation models including Chronos and TimesFM, and none beat its simple internal approaches. Accuracy-first deep learning fits operational forecasts like delivery routing, while strategic ones like FP&A need known assumptions.
• A rare overlap of skills beats application tactics. Li got into DoorDash with his "briefcase technique", a detailed doc on a company's pain points sent to the hiring manager. He was rejected by Netflix multiple times and finally hired because Netflix's new ads business needed exactly his Spotify mix of ads and forecasting. The host questions whether such docs now read as AI spam; Li says genuine, specific understanding still stands out.
• Automation fails unless someone involved has mastered the manual work. Li's attempt to auto-generate ads for founders with OpenAI's image model produced ads business owners called bad, because he had never made ad creative himself despite his ads background. His lesson is to get a good result manually first, or partner with someone who can.
• Use the LLM to trigger, and keep execution deterministic. Li walks back his tweet that agents are "just Python scripts", saying he is pro-agent, but that an LLM's answer space is too wide when a 5% chance of a weird answer is costly at scale. His balance is to let the LLM decide whether to run a workflow and make the workflow itself deterministic.

2025-12-08 · open the original ↗

• Draft angry emails outside your email client, then wait. Beasley, host of the Modern CTO podcast, credits Mark Porter, CTO of dbt Labs, with the advice to write emotionally charged emails in Evernote, not as a draft you might accidentally send, and review them once back to neutral. He says his not-send rate for such emails is "basically 100%", because they read like "a crazy person" wrote them.

2025-12-07 · open the original ↗

• Audit quarterly whether your time matches what matters. Beasley credits Kyle, whom he interviewed as Verizon's incoming CTO after a cold email to the outgoing CTO, with a recurring quarterly calendar event to list everything he actually does daily against everything that needs to get done, then reconcile the two. The point is to catch time spent on work that doesn't serve current goals, and Beasley says he has done this for several years.
• The host tracks daily leading and lagging habit metrics. The host describes a daily habit spreadsheet with a quarterly review, pairing inputs such as hours in bed and drinks with outcomes such as waking refreshed, and says drinks have a big negative effect on sleep quality.

2025-12-06 · open the original ↗

• In chaos, keep your world very small. Beasley ranks this as the best leadership advice from his roughly thousand Modern CTO episodes. It came from astronaut, pilot and surgeon Tom Marshburn, who Beasley says flew on Elon Musk's first crewed flight: at an emergency crash site or on a spacewalk, Marshburn ignores the surrounding chaos and focuses only on the next stitch, latch or knob. Beasley says he has applied it for three or four years in stressful situations with family, kids and work, and it has held every time.

2025-12-05 · open the original ↗

• Removing the driver transforms the cost per mile. The host argues that robotaxis still have to be bought, charged, cleaned, insured and maintained, but carry no hourly driver cost and can run far more hours than an idle private car, spreading capital cost over many more trips. He says Waymo rides can currently cost more than Uber or Lyft in San Francisco, but predicts per-mile prices will fall sharply at scale, which will tempt city households that spend roughly 15% of their budget on vehicle ownership.
• Robotaxis appear far safer than human drivers. The host cites Waymo safety reports showing serious-injury crashes per million miles roughly 10 times lower than human benchmarks, and a Swiss Re study finding about a 90% reduction in bodily-injury and property-damage claims. His inference is that insuring a human-driven car could become extremely expensive within our lifetime.
• Cheap rides will cause congestion without road pricing. He calls congestion a textbook externality and points to Manhattan's congestion charge cutting incoming traffic by about 10% in its first months while funding transit. He argues dynamic road pricing, perhaps framed as a robotaxi fee, will be essential to keep downtowns from gridlocking.
• Driving jobs will shrink over one to two decades. The host counts roughly half a million US taxi, chauffeur and shuttle jobs, another half million bus drivers and about 3 million truck drivers, several percent of the workforce. He says reskilling and regional transition plans are needed, and expects, from historical automation, that new and more stimulating jobs will appear.
• Fewer private cars could free a large urban land dividend. He says US downtowns devote 20 to 30% of land to parking, some central districts nearly a third, which could become housing, parks, offices, wider sidewalks and bike lanes, though cheap autonomous commutes could also fuel sprawl. He notes Baidu's Apollo Go, Pony.ai and AutoX run large services in Chinese megacities, with Apollo Go delivering millions of rides a quarter, and urges listeners to look for the "robotaxi equivalents" in their own industries.

2025-12-04 · open the original ↗

• A memory palace fixes the order of a comedy set. Beasley says remembering individual three-minute bits was easy but their sequence was not, so he adopted the memory-expert technique of attaching each joke to a vivid image along a walk through his house, such as a front door that is a giant fuzzy beard for his beard joke. He says the brain remembers familiar spatial paths well, the crazier the image the better, and that on stage before 500 people he can recover a lost place by "looking around" the room he is in mentally.
• Slides should be pictures, not text. The host, applying the idea to an upcoming 10-minute keynote, urges listeners never to put just text on slides and to use an image instead.

2025-12-03 · open the original ↗

• Community growth comes from consistency, not a formula. Sadie, founder of Women in Data, says you must show up about ten times before people trust you. She ran an event every month for 3 years, demoing something she had just learned when no speaker came. The group began with two people at its first meeting and is now 70,000 people across more than 120 countries, 10 years on. A co-host applies the same rule to a fortnightly insights forum at work: miss one and people start double-booking it.
• Listening to members set up Women in Data's inflection point. Sadie describes about five slow foundational years, reaching roughly 5 to 10 chapters and 10,000 people. In 2019 members asked for something between meetings, so an online platform was planned for 2020 and was in development that January. When everything moved online in March, it enabled global expansion with no change management needed. The mission, she says, moved from her own need for community to increasing diversity in data careers.
• Women's share of data roles falls sharply with seniority. Sadie's figures are roughly 30% among data analysts, about 12% in analytics and ML engineering roles, and 4 to 5% among chief data officers. She argues that focus belongs on leadership because it trickles down. A co-host offers the hypothesis, untested, that teams led by women attract more women, citing their time at Nextdoor.
• AI rewards deep domain expertise; you become the conductor. Sadie says computing is shifting from deterministic to probabilistic and likens the change to going from orchestra musician to conductor. A conductor must understand the music better, so she says people who think AI removes the need for skills have it backwards: the future favours domain depth and the ability to orchestrate people and agents. A co-host says 20 to 30 AI guests have consistently named communication and business context as the durable skills.
• Curiosity is a trainable muscle and the hedge against displacement. Sadie says even if AI could do her job, her open questions would keep her working. To build curiosity, she suggests breaking habits, for example a "color walk" hunting for one colour, because people suffer their own "model collapse". Her new Human Machine Collaboration Institute pursues questions such as what consciousness is, builds AI and robotics ecosystems for smaller cities, and has a partnership with Nvidia and the city of Rancho Cordova that she traces to a former Coursera student.

2025-12-03 · open the original ↗

• Laughs per minute is the metric that transcends comedic taste. Beasley, who started stand-up about 10 or 11 months before recording, says professional full-time comedians hit four to five laughs per minute, so he set out to write material at that rate and says he has largely done so. He records every set, has Gemini count laughs per minute, the only browser chatbot he found that would accept video (he says GPT and Grok wouldn't as of recording), and logs each performance in a spreadsheet with what worked, audience size and money made.
• AI as a writing coach modelled on a comedians' workshop. Beasley attended about 10 comedy writers' groups, modelled how the professionals gave each other feedback, and turned that into a saved prompt that suggests other perspectives on a joke, directions it could go and nearby double-meaning words. He adds that all laughs are not equal: duration and relative intensity matter, though he keeps his own measurement basic.

2025-12-02 · open the original ↗

• Laughs per minute, measured by AI, is Beasley's comedy benchmark. He says professional comedians hit four to five laughs per minute, records every set, has Gemini count laughs from the video, and logs each show's laughs, audience and pay in a spreadsheet. He turned his notes from about 10 comedy writers' groups into a saved writing-coach prompt, and credits this approach with an invitation onto Dry Bar comedian Jeff Allen's tour, about 20 dates through February, roughly 10 months after his first set.
• Amazon's AI-attributed layoffs are honest, and trimming is healthy. On the news that Amazon was targeting 30,000 corporate job cuts, which the host estimates at about 10% of its roughly 300,000 corporate staff, Beasley praises Amazon for saying outright that AI was a cause, when executives he interviews avoid it. He likens cuts to trimming a garden and says a firm Amazon's size likely has 10% or even 30% bloat, though he notes conflicting reports of 14,000 versus 30,000.
• People accept human error but not machine error. Beasley relays a point from former OpenAI go-to-market head Zack Kass: car crashes kill about 1.2 million people a year without outrage, yet one serious Waymo crash would bring "pitchforks in the streets." The host links this to a 2015 paper titled "Algorithm Aversion."
• Bias is inherent to intelligence, so mandate model transparency instead. Beasley argues that complaints about LLM bias are really arguments over whose group preferences get enacted. He favours legislating a "nutrition label" disclosing what a model was told after training, so buyers can choose among differently biased models. The host reports, unverified on air, that Grok's prompt was changed to align it with Elon Musk's views on certain topics; Beasley says it's too early for him to judge.
• In chaos, keep your world small; then audit your time. Beasley ranks astronaut and surgeon Tom Marshburn's advice first: focus only on the next stitch or latch amid chaos. Second is Verizon executive Kyle Malady's quarterly comparison of what he does daily against what matters, to which Beasley adds checking what you dislike doing so you can hand it off. Third is dbt Labs CTO Mark Porter's rule of drafting angry emails in Evernote and waiting, which Beasley says leaves almost all of them unsent.

2025-12-01 · open the original ↗

• Generative AI's next step is becoming social, not just smarter. Ackerman argues that investment has focused on replacing whole careers with AI, but the industry is slowly learning that results are usually better with a human involved. She cites image inpainting, which regenerates only a highlighted section, and Suno making its tool more interactive after learning that its best users want to make music themselves. Today's AI, she says, is like brilliant people who can't interact with others, and she hopes it becomes better at collaborating with humans.

2025-12-01 · open the original ↗

• List the parts of your job you enjoy, then search for them. The speaker found that what they liked about lab work was collecting data, analysing it and designing experiments. A Google search for those turned up "data science", a term they had never heard. They quit the lab job, took early Coursera courses and got a job working with data.

2025-12-01 · open the original ↗

• Computing is shifting from deterministic to probabilistic, demanding conductors, not players. The speaker says AI long ran behind the scenes in operations and is now in front of the general public. Their analogy is moving from playing in the orchestra to conducting it. The clip ends before the analogy is developed.

2025-12-01 · open the original ↗

• You must show up about ten times before people trust you. The speaker ran a Women in Data event every month for 3 years, doing a demo of something recently learned when no speaker was available. People come to rely on an event that is always there even if they miss a couple of months. The consistency built the community's trust and gave the organiser practice.

2025-11-30 · open the original ↗

• GenAI products that leave users with nothing to do don't retain them. Ackerman calls this the "one hit wonder" problem: an app that transforms your photos may be enjoyed once or twice, but without control over the output people rarely come back, because they want to express their own ideas. She says investors and industry have been "very very slow" to see this.
• The leading chatbot wins through power users, not replacement. Ackerman argues ChatGPT's success comes from users who go deep and get better by using it; she reports hearing of people whose vocabulary expanded and writing style diversified. Her advice for convincing product managers is to explain why successful products succeed, and to show where the replacement paradigm fails.
• Optimizing AI to replace workers fails collectively. Ackerman argues that if everyone optimizes for themselves and there are no more human workers, "the entire economy collapses", so the "greedy principles" guiding the economy should not be assumed to keep working.
• Mainstream chatbots are convergent by design, not inherently uncreative. Ackerman frames creativity as a search over billions of possibilities that machines explore well, but says science fiction pushed labs toward an all-knowing oracle that returns the most expected answer. Creativity has to be built in from the start; her original three-person team at Wave AI managed it, so in her view OpenAI, Microsoft and Google could too, but their goal is to replace search rather than open minds.

2025-11-29 · open the original ↗

• Machine-generated variations unlocked her songwriting. Ackerman says that, trained like many musicians to play other people's music, her songwriting "blew up" once an early research prototype, built by her collaborator David, offered many ways to sing a short lyric. Wave AI followed in late 2017, and its third product, Lyric Studio, became the successful one; the host describes Wave AI's tools as used by millions.
• The lyrics tool suggests rather than writes, to build skill. Ackerman describes Lyric Studio as a writing box with suggestions on the side from Wave AI's own lyrics LLM, built from scratch and tuned to the user's topics and style. She says the aim is never the "correct" lyrics but widening options, and claims it makes users better pen-and-paper songwriters, an effect that lasts beyond using the software.

2025-11-28 · open the original ↗

• Google's new model has retaken the frontier, at least for now. The host reports Gemini 3 Pro debuted first on LMArena across text, vision, coding and web development, ahead of xAI's Grok 4.1 and OpenAI's GPT-5-class models, and that the LMArena team called it the first model to break an Elo of 1500. He ties this to the time of recording, after Google trailed OpenAI and then Anthropic for about three years.
• The biggest gains are on the hardest reasoning benchmarks. As the host relays them: Humanity's Last Exam 38% with no tools against 27% for GPT-5.1; MathArena Apex about 23% against about 1% for GPT-5.1 and Claude 4.5; ARC-AGI-2 31% against 18% (GPT-5.1) and 14% (Claude 4.5). He adds new highs on AIME, GPQA Diamond and MMMU-Pro.
• Its agentic edge is in long-horizon planning and screen understanding. The host reports parity with Claude and GPT on SWE-bench Verified but a clear lead on LiveCodeBench Pro, a simulated profit of over $5,500 on Vending-Bench 2.0 (more than Claude Sonnet 4.5 and GPT-5.1 combined), and 73% on ScreenSpot Pro against 36% for Sonnet 4.5. On that last figure he says it may be the first LLM you would trust to act autonomously from what is on screen.
• Google is deploying it broadly and early users report gains. Google has revealed little architecture beyond a million-token context window, which the host says is about eight novels. It is rolling out across Search, the Gemini app and Vertex AI. He relays that Wayfair's CTO is piloting it to turn support documents into infographics, and that GitHub reported a 35% boost in code accuracy over Gemini 2.5 in an early test.
• The host's lesson is that frontier competition benefits builders. He argues that whichever lab leads in a given month is hard to predict, but practitioners deploying rapidly improving models all benefit. He also points to Google's Nano Banana Pro image model, which he says runs on Gemini 3 Pro.

2025-11-27 · open the original ↗

• Hallucination is the mechanism of creativity, in humans and machines alike. Ackerman argues that people hallucinate constantly, perceiving reality differently from one another, and that on certain psychedelics humans generate images and video faster and better than text-to-image models do. Recognizing that shared root, she says, means letting go of demands for control and precision all the time and leaning into how much brains rely on imagination.

2025-11-26 · open the original ↗

• Implicit trust in AI, not AI itself, is the main danger. Ackerman says the science-fiction image of an all-knowing oracle, even comparisons of LLMs to god, leads young and older users to trust ChatGPT implicitly. She argues a healthy level of doubt is enough to undo much misinformation damage and to stop people blindly taking its advice on personal problems.
• No process, human or machine, yields an infallible oracle. Ackerman rejects the idea that prediction or dependence on data is a design flaw, since humans also learn from sensory input and predict constantly. Her point is to separate process from results: however it works, it will never be perfect, just as the smartest humans are not.

2025-11-25 · open the original ↗

• Machine creativity is undeniable and decades old. Ackerman says David Cope's Experiments in Musical Intelligence produced Bach- and Vivaldi-style compositions in the 1980s that listeners could not tell apart from the real thing in a discrimination test, yet "knew all along" once told a machine made them. She calls the swing between "AI will replace all artists" and "AI isn't really creative" a denial that blocks working out how to live with these systems.
• The recent leap came from size and money, not new ideas. Ackerman dates convincing generated audio, as opposed to scores, to about 2022, and says Suno and Udio's direct sound generation is meaningful but smaller than the public believes. What changed was the funding to build bigger "brains", which she likens to OpenAI's Microsoft investment, plus marketing; because we build brains rather than programs, she argues control over them is inherently limited.
• Creativity should be judged by outcomes, not process. Ackerman grants that most human art carries authentic emotion, but notes commissioned professionals deliver on deadline whether they feel it or not, and a photographer friend invents stories for galleries. Her point is that critics of AI art should know when they are criticising the process rather than the product.
• Generative models expose a culture's collective beliefs and biases. Drawing on Carl Jung's collective consciousness, Ackerman says models trained on Western data reveal Western assumptions more honestly than any person would: Midjourney once returned a Christmas tree for "Hanukkah" (since fixed, she says) and bagels for sufganiyot. Her research showed image models reproduce "brilliance bias", the belief that genius is male; the host cites a version presented at GHTC in 2022.
• Industry's oracle-and-replacement goal is the wrong target for GenAI. Ackerman argues products work better with humans involved, pointing to inpainting and to Suno making its product more interactive after finding its best users want to make music. She expects AI to become "more social" and collaborative, the approach she says Wave AI's Lyric Studio and Melody Studio take by suggesting lyrics, chords and melodies rather than writing for the user.

2025-11-24 · open the original ↗

• Open interoperability is meant to lower the bar for enterprise agent adoption. Pandey says that was AGNTCY's goal from the start: agentic workflows run into "brown field heterogeneity", with agents from Salesforce, Cisco, Microsoft and ServiceNow sitting in different clouds with different intents, even for a simple sales funnel. He names four steps enterprises struggle with daily: discovery, identity, collaboration and evaluation, with identity the big stumbling block.
• Agents need shared meaning, not just connectivity. Pandey's example: when an agent returns "the best X with 90% confidence", it is unclear whether "best" and "90%" mean the same thing across all agents or vary vendor by vendor. He agrees with the host that frameworks such as zero-trust agency and task-based access control are aimed at helping practitioners get agentic projects through security review.

2025-11-23 · open the original ↗

• Zero trust for agents means permissions scoped to one task, then revoked. Pandey defines it as trusting no agent until it is shown it should do a specific task, granting access just in time for that task, tool or transaction, and revoking the token the moment it finishes. His analogy: role-based access lets him open a safe, but task-based access lets him withdraw only $10 in the next 10 seconds and then shuts the door.
• Making task-based access control work takes three pieces of infrastructure. Pandey lists identity providers and authorization servers that support it; a semantic parser that reads agent-to-agent and agent-to-human communication to work out what task is actually being requested; and an ephemeral, sandboxed runtime that executes only that task. A short recap that follows, apparently the host's, also lists human-in-the-loop approvals.
• Agents earn trust through directories, then through evaluation. Pandey says the simplest step is a directory that lets you discover agents by capability, reputation and trust, for example the best-of-breed financial agent from a reputable vendor. Crowdsourced ratings, like an app-store score, are one option; for more rigor, ongoing evaluation of agents and multi-agent workflows feeds back into the directory's reputation score, valid until something in the system changes.

2025-11-22 · open the original ↗

• The framework embraces rival agent protocols to avoid fragmentation. Pandey says AGNTCY, which works alongside MCP and A2A, moved to the Linux Foundation with Cisco, Dell, Google, Red Hat and Oracle as formative members and about 80 other organizations contributing or deploying it. Cisco is a foundational member of Google's A2A project there, and Google of AGNTCY, because he argues multiple competing standards would confuse an enterprise market already struggling to deploy agents.
• Agent communication today resembles networking before a common standard. Pandey describes mid-1990s "islands of connectivity", DECnet, AppleTalk and IBM SNA, that did not interoperate until the industry standardized on the open-sourced BSD TCP/IP stack, a move he credits to Vint Cerf and says Cisco backed. He sees agents at a similar juncture, where some systems talk to each other and some don't.
• Many agent protocols will appear, so the scaffold around them matters more. Pandey points out that TCP/IP was not the end: QUIC later combined TCP's delivery guarantees with UDP's low latency. He expects the same proliferation beyond MCP and A2A, and argues the lasting need is a protocol-independent scaffold: discovery (a DNS for agents and their capabilities), identity, messaging and evaluation.

2025-11-21 · open the original ↗

• Expert forecasters expect AI-assisted work to jump within five years. The host relays the Longitudinal Expert AI Panel (LEAP), a monthly survey of over 300 experts and superforecasters: its median forecast is that AI will assist roughly 18% of US work hours by 2030, up from about 2% today. The same panel expects AI training and deployment to use about 7% of US electricity by then, more than all US data centers combined today.
• The panel rates AI as at least a technology of the century by 2040. On Nate Silver's 10-point "technological Richter scale", LEAP's modal forecast is level 8, the tier of electricity or the automobile. The experts give about a 1 in 3 chance of level 9, the tier of the printing press or the industrial revolution, within 15 years.
• Capability jumps keep outrunning expectations, which policy must match. The host argues LEAP's strength is short-horizon questions that let it weight the most accurate forecasters, because progress is lumpy. His examples: Gemini 2.5 Deep Think's record 29% on tiers 1–3 of FrontierMath in October, and a virology test in which top models, with OpenAI's o3 highlighted, beat PhD virologists at troubleshooting wet-lab tasks, prompting calls for guardrails. His lesson is that governance must keep pace, not panic.
• The host expects gradual job pressure, not sudden collapse. He cites the IMF estimate that about 60% of jobs in advanced economies are exposed to AI, roughly half likely complemented and half at risk of substitution, and reads LEAP as pointing to slower growth in some white-collar categories. His advice is to treat 18% AI-assisted work by 2030 as a planning baseline, invest in skills and process change now, and assume pressure on power and infrastructure.

2025-11-20 · open the original ↗

• Real workflows chain very different agents across vendors, clouds and the physical world. Pandey's example, from a pharma company, runs from an LLM that plans the drug-discovery goal, to a scientific foundation model such as a protein-folding model that explores candidates, to robotic wet labs that test them physically, then to animal and human trials. Underneath run agents checking efficacy, cost and compliance.
• The internet of agents spans software, social and embodied agents. Pandey says it covers social interaction, scientific discovery, B2B services and physical AI robotics, and that no platform let such heterogeneous agents collaborate, which is the problem he says Cisco's open-source AGNTCY targets. Beyond communication, it must handle discovering capabilities, identity, composing agents into self-forming teams, and evaluating whether they serve a common goal.

2025-11-19 · open the original ↗

• Self-serve BI failed on adoption, not on the tools. Lei Tang, CTO and co-founder of Fabi, describes running a data science team (captioned "lift") with hundreds of tickets a week in the backlog. Mode dashboards he shared went untouched for three months ("a dashboard graveyard"), and weekly SQL training for growth marketers, with office hours and homework, left very few able to write their own queries. He says semantic layers and human-curated documentation cost too much upfront and cannot keep pace with a fast-changing organization: "any tool that requires human curation" fails, except Wikipedia.
• Vibe analytics means the human forms hypotheses and AI implements. Lei's example: when activation drops 10% in a week, the analyst proposes explanations, such as product lines or user segments, and AI writes the window functions and plots that confirm or reject them. A domain expert who looks at the business daily can tell when an output is wrong and can iterate or ask the data team to check it.
• AI should learn the semantic layer rather than wait for humans to write it. Lei says Fabi crawls schema metadata (keys, uniqueness, nulls) and learns from two places: the organization's most heavily QA'd KPI dashboards and users' own interactions. It shows what it has learned as readable markdown for a quick human yes or no, instead of YAML or JSON. A co-host adds that missing logging and tracking, plus undocumented business context, still break AI analysis.
• Assume data quality is bad and begin with guardrails. Lei says no head of data among hundreds of customers and prospects has ever called their data good, and 3–6 month single-source-of-truth projects tend to stall. He recommends seeding a few dozen core tables, which he bets cover about 70% of requests, and giving all tables to technical users while non-technical users see only datasets a data person has published. His warning: a Fabi agent given past conversation histories and tool results made up a successful "dry run" and its preview.
• Critical thinking, not coding, is what protects analysts' jobs. Lei says AI writes SQL that runs cleanly but has the wrong logic, because business logic is undocumented and spread across people. He argues analysts won't be replaced if they focus on what matters most to the business. Deep ML and GPU work will still need strong coding, AI acts as a multiplier that makes analysts "full stack", and Fabi lets candidates use AI in coding interviews.

2025-11-19 · open the original ↗

• Analysts who pick the right problems won't be replaced. The speaker says AI will not replace data scientists or analysts as long as they know what matters most to the business, apply critical thinking to identify and ask the right problem, and then use AI to get more done and deliver a larger impact.

2025-11-19 · open the original ↗

• An agent-human collaboration platform should be open, like the original internet. Pandey says the work began about two years before recording with the view that no platform let agents and humans collaborate on business, social, physical or service outcomes. Since Cisco was behind the original open internet, the team framed the thesis as the "internet of agents" and open-sourced it as AGNTCY, arguing "open source are the new standards".
• Today's business and coding agents are a myopic view of what's coming. Pandey forecasts multi-agent human societies tackling the biggest problems: drugs, materials, how the universe formed. He also expects agents for social interaction and embodied agents for physical work, arguing humans alone will not suffice; he wants agents to do his dishes, not to paint or play guitar for him.

2025-11-19 · open the original ↗

• Domain experts can check AI analysis without technical skills. The speaker says someone who knows no Python, SQL or data modeling, but looks at the business every day, can tell at a glance whether an AI-made chart of transactions is reasonable. If it looks wrong, they can iterate on the prompt or bring in the data team to double-check.

2025-11-18 · open the original ↗

• Agents need an open, protocol-agnostic scaffold, like the early internet's. Pandey says Outshift, Cisco's internal incubator, open-sourced its "internet of agents" thesis as AGNTCY, now in the Linux Foundation with Cisco, Dell, Google, Red Hat and Oracle as formative members and about 80 other organizations involved. He likens today's agents to pre-TCP/IP networking islands and expects many protocols beyond MCP and A2A, so the lasting layer is discovery, identity, messaging and evaluation.
• Probabilistic AI works best blended with deterministic tools. Pandey argues the LLM craze is making people ignore deterministic tools that work well, and points to a resurgence of neuro-symbolic AI. His example is Outshift's network configuration and validation pipeline, which pairs a digital twin of the network with generative agents to get speed and accuracy together.
• Identity and access management breaks for agents. Pandey says IAM was built for humans or deterministic software, while agents talk like humans, give different answers to the same question and act at machine speed and scale, sometimes for one person and then another a minute later. Role-, attribute- and relationship-based access control assume stable roles that agents lack.
• Task-scoped, just-in-time permissions are the proposed fix. Pandey's task, tool and transaction-based access control (TBAC) grants a token for one task in a sandbox, then revokes it; it needs identity-provider support, semantic parsing of agent communication and ephemeral runtimes. Role-based control stays for humans, and the two must be bridged because humans will stay in the loop for approvals "for a long time". Agents earn trust through a reputation directory fed by evaluation.
• The next problem is shared meaning, not shared syntax. Pandey says current frameworks and protocols such as LangGraph, CrewAI and Google's Agent SDK solve only syntactic interoperability. Agents must also agree on what "best" or "90%" means, which takes the work into knowledge and cognition and, he says, will spawn many more protocols.

2025-11-17 · open the original ↗

• Enterprise AI projects fail when goals are vague and humans are absent. Responding to the MIT claim the host covered in episode 924, that 95% of enterprise AI projects fail to deliver a return in production, Gupta says success starts with clear goals, down to the desired type and accuracy of responses. He argues humans must stay in the loop in training and also in production, as built-in feedback to catch drift.
• Don't apply GenAI where traditional machine learning already works. Gupta says he has seen "plenty of misuse" of GenAI on problems well solved by solid, deterministic machine learning. His sequence is problem, goal, then the right tool, followed by use-case-specific best practices.
• Model choice can dead-end when scaling across devices. Cox says that with two million models on Hugging Face, Dell's tooling offers a curated set of enterprise-ready models checked for performance across silicon types. His reason is that teams often start with one model and hit a dead end when scaling up or across device categories.

2025-11-17 · open the original ↗

• High-impact problems often need only simple analysis. The speaker's advice is to keep asking what matters most to the organization and to look for value there. Not every high-impact problem needs sophisticated methods: sometimes putting the data on a line chart gets you 70 or 80% of the way.

2025-11-16 · open the original ↗

• State space models are an old control technique retooled for language. Cox explains they map an input signal onto a hidden state with one set of equations and turn that state into an observable output with another, and were used in 1960s spaceflight control, population studies and economics. Researchers at Carnegie Mellon and Princeton adapted them to sequence modeling in papers from 2021 to 2025, running from S4 to S6 and then to Mamba and Mamba 2.
• The hybrid design trades attention for far lower long-context memory. Cox says IBM's Granite 4.0 H models use a 9:1 ratio of Mamba to attention layers with no positional embedding. Training data reaches 512k context and the models are validated to 128k. He cites IBM's release notes: eight sessions at 128k context on the 3-billion-parameter Micro model use about 15 gigabytes of memory versus about 80 for a pure transformer, freeing usable context for RAG and multi-turn work on constrained edge devices.
• The small models score with frontier-scale ones on tool use. Cox says Granite ranks in the top five of the Berkeley Function Calling Leaderboard (BFCL v3) at the time of recording, alongside frontier models with hundreds of billions of parameters, at a 32-billion-parameter footprint. Gupta had earlier pointed to IFEval for instruction following.

2025-11-15 · open the original ↗

• Critical reasoning alongside AI is the most underrated skill. The speaker names the intuition and creativity to reason, contextualize and synthesize information together with AI, rather than relying on AI entirely, because human judgment is still required and needs to be developed.

2025-11-15 · open the original ↗

• LLMs on structured prediction problems add complexity without value. The speaker says traditional ML still wins where inputs, ground truth and outputs are well defined, as in recommendation and personalization: it is more transparent, can be measured with established metrics, and is faster, cheaper and more sustainable. LLM and agentic approaches win in open-ended, reasoning-heavy or interactive settings, but predicting cancellations or disengagement with a large language model is "overkill".

2025-11-15 · open the original ↗

• Evaluation is the main obstacle to launching autonomous agents. The speaker's biggest worry is how to know agents are ready for the real world, since they will interact with many other agents (citing A2A), make mistakes and work on complex problems. Being ready means running without oversight while preserving privacy, keeping memory correct and maintaining long-term context.

2025-11-15 · open the original ↗

• Vibe coding lets non-front-end developers build websites quickly. One speaker says that two years ago they would not have believed they could go from an idea to a vibe-coded website so fast without being a front-end developer. A second voice calls it a big leap beyond Cursor-style coding, since something appears and "kind of mostly works", and says it was addictive.

2025-11-15 · open the original ↗

• Many people who bridge AI and outward-facing roles are ex-theater kids. The speaker reports this from a conversation with someone else in the field, while saying they don't want to stereotype: AI people tend to split into outward-facing people and deep engineers. Of those who bridge both, especially in external-facing roles, an "overwhelming amount" the speaker has met were theater kids.

2025-11-15 · open the original ↗

• The system around an LLM is what makes it valuable. The speaker says people used to treat LLMs as magical and gave too little credit to everything around them. The LLM can serve as the brain or focal point for decisions, but the surrounding system is what creates the value.

2025-11-15 · open the original ↗

• The role turns new research into applied product. The speaker describes a product incubation team at IBM that takes research from IBM and other labs, finds use cases for it, and builds first-of-their-kind prototypes and early use cases. The team also forms partnerships with others who can build on the work, or open-sources it.