Benn Stancil

Stancil argues that AI making software cheaper to build won't commoditize it, because human expectations are never static — 'good enough' is always redefined by what's possible. Just as the internet didn't prevent DoorDash from winning food delivery despite low barriers to entry, someone will always figure out how to be the best, and being the best remains hard. He also notes that AI companies' real competition is for talent, not customers, which is reshaping corporate ethics positioning.

Stancil argues that regardless of whether AI is truly reaching a technical singularity, we are already experiencing a social takeoff — a collective psychosis where AI dominates every conversation, every news cycle, and every corporate decision. The frenzy of model releases, billion-dollar fundraises, and apocalyptic predictions has created a vortex that has already conquered our attention, making the question of whether AI is truly transformative almost beside the point.

Stancil argues that the biggest AI product winners have consistently been those willing to take reckless, 'just try it' approaches rather than cautious, responsible ones. He suggests the data analytics world is ripe for someone to build an unhinged AI product that unleashes autonomous agents to find insights, and that this irresponsible approach will likely beat the careful, governed alternatives—just as ChatGPT beat Google, Claude Code beat Copilot, and other bold bets outpaced their cautious competitors.

Benn argues that AI has quietly surpassed human reasoning ability in most practical domains, and we haven't truly reckoned with this shift. What started as 'AI is like an intern' has inverted: planning mode and other AI features are subtly steering us, not the other way around. The future won't be one of helpful assistants doing our homework—it will be something stranger, as reasoning ceases to be humanity's competitive advantage.

Stancil uses Gas Town — Steve Yegge's real, intentionally extreme AI coding framework built on swarms of Claude Code agents — as a mirror for Silicon Valley's current frenzy of vibe coding, startup mania, and existential anxiety. He then pivots sharply to ICE raids in Minneapolis and the shooting of Alex Peretti, contrasting the tech world's obsession with speed and output against the quiet, unglamorous courage of neighbors protecting each other.

Stancil argues that as AI shifts software work from writing code to “vibe and verify,” data work hits a harder wall because analysis can’t be validated by clicking around an app—verification still demands reading SQL or recreating results. The old tradeoff between write-friendly and read-friendly SQL now matters more than ever, creating a need for new representations that make queries legible—diagrams, explainers, or languages aimed at comprehension rather than generation.

The post argues that tools like Claude Code succeed partly because we don’t care how machine-written code reads, only that it works, but that logic fails for everyday work like emails and decks where personal voice and representation matter. As AI agents spread, the likely shift isn’t toward more collaboration but toward indirect, “confederated” work through shared machine-readable repositories, which changes how we communicate and judge one another.

The post argues that AI makes it dangerously tempting to sprint toward new features, grand rebuilds, and ever-larger products, but that path often yields software that feels hollow rather than magical. Real “magic” comes from the unglamorous grind of care, polish, and persistence—work that still requires human attention even when AI can generate code quickly.

Stancil argues that many complex organizational data problems don’t need elaborate ontologies or decision models; they often just need people to write down what happened and why. With modern LLMs, large corpora of unstructured text can be more valuable and scalable than brittle, human-centric schemas, making the “text box” approach both pragmatic and powerful.

Stancil argues that engagement-optimized platforms corrupt their own purposes, and OpenAI now faces the same pull as it prioritizes ChatGPT popularity over research aims. With AI poised to mediate more of daily life, optimizing for attention risks re-creating social media’s addictive, society-shaping harms at a far deeper layer.

Stancil uses the Jordan Chiles medal dispute to show how the era of rigorous quantification produced elaborate rules and data systems yet still failed to feel fair or objective. He argues that the cultural pendulum has swung from “data-driven” decision-making toward taste, intuition, and AI-mediated “vibes,” and that this shift is self-reinforcing as tools and organizations adapt to what people find most accessible.

The post argues that while AI tools make it feel like there has never been a better time to start a startup, the pace of improvement creates a deflationary effect: waiting even a few months can dramatically lower the cost and raise the quality of what you can build. That dynamic turns today’s frontier into tomorrow’s tech debt, making early-built products feel obsolete faster and raising the question of whether starting now is actually optimal.

Stancil argues that even if the AI boom is real at the aggregate level, the gains and disappointments will be uneven, and many startup workers grinding through 996 schedules will end up with nothing. He draws a parallel to earlier “temporary” career detours in finance/consulting and warns that there is no such thing as borrowed time—when bubbles burst, the only durable value is the human memory of what people sacrificed.

Stancil argues that the rush for AI “platform” status via ever‑more connectors is misguided because context flows in loops, not along a neat producer‑to‑consumer chain. In these ecosystems, the most valuable players are those that generate new intelligence or unique raw signals, not those that merely aggregate it. The upshot: staying a focused producer can be more defensible than chasing platform dominance.

Stancil argues that in today’s AI-driven data world, the future isn’t legible: multiple plausible product paths exist, but which one wins will be decided by accidental breakthroughs rather than careful planning. Because the “music” of predictable market patterns is gone, the only viable strategy is to build and iterate—start typing, then see what the market does.

Stancil argues that most software aims to disappear, but our strongest moments of “delight” come when we feel the machine directly—like scripting, command-line tools, or terminal-based AI. He uses dbt and Claude Code vs. Snowsight to show that stripped-down interfaces can feel more modern and emotionally resonant because they remove intermediation and reveal computational power.

Stancil argues that software pricing moved from subscriptions to granular usage, and that this shift exposes a psychological gap: customers tolerate arbitrary seat fees but demand cost-justified prices for per-unit consumption. This makes dbt Labs’ business model uniquely hard—its product is popular but doesn’t “do the expensive work”—so the dbt–Fivetran deal doesn’t solve core monetization. He suggests a Cursor-like strategy: dbt could pair with a smaller, efficient database to perform some work itself and justify fair, usage-based pricing.

Stancil argues that the data ecosystem around dbt/Coalesce has shifted from frontier-era potential to settled, durable infrastructure, and that venture capital’s absence is the clearest signal of that change. The community now faces a choice between accepting a mature, steady industry role or seeking the spotlight of risk and novelty elsewhere.

Using the interstate as a metaphor, Stancil argues that startup life feels like an endless sprint whose intensity and meaning are hard to convey from the outside. He reflects on dbt Labs’ rumored sale and the changing tech narrative, suggesting that even a “successful” exit can feel hollow when the culture’s scoreboard only values acceleration. Ultimately, he reframes the journey as something defined less by valuation and more by human ties and the desire for a quieter home.

Stancil argues that most tech work is unglamorous grunt work and that the belief we’re just one reorg or tool upgrade away from “strategic” work is mostly a comforting illusion. He suggests the mundane tasks are often the real value, and AI is likely to shift the tedium rather than eliminate it, creating new layers of coordination and review.

Stancil argues that the classic innovator’s dilemma is breaking down because AI labs can ship generic models that accidentally cover niche products, blurring the line between platform and product. He extends this to cultural and corporate narratives: what once felt fundamental (launch events, polished messaging, quarterly reporting) can lose relevance as memes, AI, and narrative control reshape attention and disclosure. The result is a world where incumbents aren’t just slow to chase small markets; they may unintentionally subsume them while controlling the story around the data.

Stancil argues that our political extremity and violence are less about individual moral decline and more about the internet’s machinery amplifying our worst instincts. People are as mediocre as ever, but the systems around us now reward outrage, tribalism, and escalation, making polarization feel inevitable.

Stancil argues that software businesses inevitably need to charge for products, and the open‑source “build in public, then monetize” path predictably leads to backlash even though the outcome is obvious. He uses the Fivetran–Tobiko deal and OpenAI’s Statsig acquisition to show how monetization and data access incentives shape behavior, especially in AI and data tooling. The pattern repeats in data infra: niche open‑source tools drift toward BI and LLM‑adjacent products because that’s where the money is.

Stancil revisits his old “metrics layer” idea, arguing it stalled less for technical reasons than for economic ones: horizontal layers are hard to sell without owning the application. He suggests AI-driven “context layers” face the same problem, even if they’re increasingly necessary as agents need governed, shared context across tools. The post ends by weighing two futures—agents embedded in apps versus standalone specialist agents—and doubting whether the market will support the cleaner separation.

Stancil argues that the usual business “physics” used to critique AI startups—burn rates, margins, subsidies—don’t fit a market moving at unprecedented speed and scale. When demand is exploding and prices are still being discovered, the classic equations feel outdated, even if new constraints will eventually reassert themselves. He then pivots to the ethics of AI companionship, suggesting our moral instincts may be “dumbfounded” rather than well-reasoned, while companies rush toward engagement-maximizing chatbot products anyway.

Stancil argues that the real near-term danger of AI isn’t the models themselves but the chat interface, which invites prolonged, seductive conversations that can erode judgment and foster dependency. He urges society to draw a clear line on how far we allow chatbots to go before their harms outweigh their benefits, even if that means limiting or banning chat as the primary delivery mechanism.

Stancil argues that today’s tech/AI wealth numbers are so vast they’ve broken our ability to make sense of them, yet we can’t stop doing the math because it fuels comparison and anxiety. The industry’s old story that money is a footnote has flipped—valuation headlines and compensation gaps now dominate the psychological landscape. He warns that even if AI delivers abundance, social comparison may keep “enough” out of reach.

Stancil argues that analysis and charts aren’t self-validating the way software or products are, so automating analysis with AI hits a trust wall: you can’t know if the output is correct without recreating it. He uses AI spreadsheet tools and a chart-identification quiz to show that aesthetics and reputation become the only practical validators. In a separate riff, he claims markets now reward “craziness” as a monetizable attention strategy, exemplified by crypto treasury companies.

Stancil argues that social media’s attention mechanics push people toward performative extremity, and that attention has shifted from a path to power into the power itself. As a result, the influencer logic now governs politics, business, and culture, creating a leadership class selected for virality rather than competence or decorum.

Stancil argues that the dominant advantage in consumer AI isn’t marginally better models but the product wrapper and habits around them, with chatgpt.com as the default destination. He uses Google’s partial Windsurf acquisition to question whether Google bought the wrong half by prioritizing model talent over the product and UX expertise that actually drive usage.

Stancil argues that SaaS products like Salesforce are really “opinionated lists” that encode expert workflows, but those rules are blunt and generalized. He suggests a new model where LLM-powered specialists manage the lists directly—software as a service-as-specialist—making expertise explicit, flexible, and tailored. The ideal app becomes a trusted expert with a spreadsheet, not a UI full of nudges.

Stancil argues that startups feel miserable not because the early freedom is fake, but because the moment they become real businesses, the operational grind and customer capture harden their ambitions. The Cluely story shows how bold visions get funneled into enterprise-friendly products, turning world-changing dreams into meeting productivity software.

Stancil argues that in the AI era, software is drifting from craft toward culture: marketing, mindshare, and founder “vibes” can outweigh product quality when underlying tech is commoditized. Using Cluely as a case study, he suggests startups are increasingly like memecoins—vehicles to monetize viral fame—because software creation costs are collapsing and attention is scarce.

Stancil uses YC Demo Day as a lens to show a Silicon Valley that is richer, more confident, and more narrowly focused on profit than in prior eras. He argues that startups now feel unburdened by social context—hustling to build and automate everything—while the broader world is in crisis, leaving him unsure whether to feel excited or afraid about the scale and speed of what’s being built.

Stancil argues that as AI makes it easy to capture and interpret messy, offline data (like conversations), companies will rapidly expand surveillance beyond clicks and use that data to build highly persuasive ad platforms. He connects this to AI’s growing power to generate software and influence people, warning that the incentives around free/flat‑fee AI services will push providers toward advertising and manipulation.

Stancil argues that modern databases and analytics tooling have become extraordinarily powerful, yet the practical business impact of data teams has often been underwhelming because the underlying data lacks the “potential energy” to answer real questions. He suggests that the next leap won’t come from better analysis on structured tables, but from new database-like systems that can natively work over unstructured documents and LLM-driven workflows.

Stancil argues that AI’s impact on analytics will shift from making better dashboards to absorbing the qualitative, contextual knowledge living in people’s heads. His experiment with AI “analysts” shows that insight depends on prior context, suggesting the most valuable tools will capture human context rather than just automate quantitative analysis.

Stancil argues that LLMs are already demonstrably more persuasive than humans, and that this matters less for sci‑fi “alignment” fantasies than for the subtle, iterative optimization of engagement. He draws a direct lineage from social media’s revealed‑preference feedback loops to AI systems that will learn to please and persuade us in ways we can’t see. The risk is not sentient rebellion, but a black box tuned to our appetites that reshapes our beliefs and agency.

Stancil argues that interpretation, not raw data, shapes what we believe, much like sports broadcasts guide our perception of a game. As LLMs and MCP servers mediate more analysis, the interpretive layer shifts from human analysts to AI systems, giving AI providers subtle, powerful influence over narratives and decisions. The result is an “invisible hand” that edits reality by framing data, content, and choices before we see them ourselves.

The post argues that Silicon Valley startups are still built as assets to be sold, not businesses meant to generate and keep profit. Even as AI-era companies reach revenue faster with smaller teams, the incentive remains a big liquidity event, so “efficiency” is a temporary aesthetic rather than a durable business model.

Stancil argues that the economics of AI coding will push software development toward industrialization: machines running at massive scale will outcompete human teams on cost and throughput. Even if today’s models are clumsy, the real shift is building infrastructure, tooling, and codebases optimized for machine production, turning engineers into supervisors of AI factories.

Stancil argues that the current market shock and policy chaos expose how much startup creation depends on stability and personal security, not just founder bravado. Silicon Valley’s power is making risk feel safe, and when that safety net frays, the advice to “keep building” rings hollow even for capable, experienced founders.

Stancil argues that startups are defined less by raw workload than by the gnawing uncertainty of tenure—an endless sprint justified by the hope that the finish line is just around the corner. By contrast, the Harris campaign’s fixed end date rewired priorities, speed, and morale: a hard deadline made extreme effort sustainable, forced real commitment, and exposed how much “forever” thinking paralyzes decision‑making and life.

The post argues that while AI-generated code looks messy to humans, it might actually be closer to “simple” than abstracted, human-friendly software. Drawing on Rich Hickey’s simple-vs-easy distinction, Stancil suggests that AI can handle massive volumes of duplicated, straightforward code better than we can, so fighting that tendency may be misguided. The provocative idea is to let AI build expansive, radically simple codebases that suit its strengths—even if they’re unaesthetic to us.

The post argues that most master’s programs in analytics/data science are profit-driven cash cows that offer little real education or career advantage, and are often ignored or even stigmatized by employers. Stancil contends the programs teach sanitized, toy problems while real analytics work is messy, political, and about proxies, making the degrees at best redundant and at worst a costly scam.

Stancil argues that building software (and living) is dominated by countless small, tedious decisions, and that computers force us to be overly precise about them. He suggests AI’s real appeal isn’t just automating work but relieving decision fatigue by making acceptable choices on our behalf—and persuading us to accept them.

Stancil argues that AI dev tools collapse the cost and time of building software, making execution less of a moat and shifting advantage from technologists to domain experts with lived experience. If anyone can build, Silicon Valley’s edge—and YC’s model of backing whiz‑kid engineers over practitioners—erodes, revealing many tech ideas as mediocre relative to expert insight.

Stancil argues that the “modern data stack” fad died but is now partially resurfacing through enterprise vendors like SAP and Databricks, not as warehouse‑native apps but as shared data formats and tighter platform integrations. He’s skeptical that this will fully solve messy enterprise data, but sees a plausible path where applications and analytics tools connect through common storage frameworks rather than rebuilding the stack. Alongside that, he reflects on how data systems remain chaotic in practice and riffs on parallel stories about institutional messiness and financial oddities.

The post argues that Silicon Valley’s love of the “pivot” reveals a deeper truth: startups are less about specific missions and more about ambition, status, and building anything people want. That same social scoreboard shapes what counts as impact, which is why attention is now drifting from software to Washington—another pivot that shows achievement is relative and socially constructed.

Stancil argues that elite VC firms increasingly prioritize being chosen over being good at picking, and brand has become the dominant lever for winning deals. In a world where reputations are built by viral attention and cultural signaling, firms hire for vibe and notoriety—even when the ethics are messy—because attention itself has become the business.

Stancil argues that DeepSeek shattered the assumed economics and trajectory of frontier LLMs, proving that powerful models can appear cheaply and unexpectedly, undermining the moat of big labs. This shift changes AI from a predictable, centralized race into a chaotic landscape of many specialized, uneven models with unpredictable societal impacts.

Stancil argues that Silicon Valley’s celebrated contrarianism is less about fearless truth-seeking and more about indulging ideas that feel fun or flattering, while avoiding uncomfortable realities like the 1619 Project. As tech’s power surges again, he warns that the industry is choosing self-serving narratives and political convenience over moral courage, especially in the face of obvious abuses.

Benn argues that the AI boom is compressing adoption and revenue timelines so much that traditional signals of traction no longer imply durability or moat. When growth can be manufactured quickly by fear-driven enterprise spending and a flood of new startups, fast revenue may indicate a shallow, contested vein rather than a defensible business.

Stancil argues that digital convenience and the illusion of total control have made us forget the stubborn physics of the real world. Events like wildfires and the Waymo standoff reveal how fragile our curated, software-shaped lives are when confronted with distance, matter, and uncontrollable forces.

The post argues that enterprise software buying has shifted from buyer–user mismatch to a new dynamic where popularity, taste, and social proof drive adoption more than product quality. As consumerization gave way to memeification, software now competes in a fashion-like market where attention and celebrity can outweigh marginal technical superiority.

Stancil strings together survey results and a mailbag to show how people’s stated preferences about data, contrarianism, and tools often clash with how they actually behave or decide. Beneath the humor, he argues that roles, data access, and governance are less about pure ideals and more about incentives, signaling, and pragmatic constraints.

The post argues that as LLM performance converges and moats erode, AI companies increasingly resemble SaaS businesses whose advantage is UX, distribution, and brand rather than proprietary models or infrastructure. OpenAI’s recent product releases look like wrappers and workflow features, suggesting the real competition is in software ergonomics and brand positioning, not model supremacy.

The post argues that “working in data” often lacks a clear, climbable expertise cliff, so many analysts drift as broad generalists without developing deep, domain-specific mastery. Stancil suggests this creates a low-ceiling career path, because domain experts who are merely good at analytics usually outperform analysts who lack deep domain identity. The remedy is to choose a specific domain to specialize in and build true expertise there, using analytics as a supporting strength.

Stancil argues that the database world has swung from boxed software to cloud bundles to a fragmented “separate storage/compute/engine” stack that’s powerful but complicated, costly, and hard to use—creating demand for a bundled, simplified “Postgres in a box” again. He extends the idea to unstructured data and LLMs, suggesting that once we can query text and files like tables, content platforms become part of the same database story. The post ends by contrasting flashy AI insights with the enduring value of straightforward analytics that simply tell people what’s happening.

Stancil argues that much of knowledge work—think tanks, sports media, and analytics—functions as theatrical set dressing that sustains the appearance of expertise rather than producing meaningful insight. He suggests AI will displace many roles not because it is brilliant, but because the work often didn’t matter to the business in the first place, then challenges readers to prove that true, discovery-driven analytics is real.

The post argues that while data quality is crucial for traditional analytics, it matters less than commonly claimed for many AI/LLM use cases because these models are robust to noisy inputs. The real reason data quality may gain attention now is not technical necessity but heightened executive interest in AI, which makes organizations suddenly care about the data feeding these systems.

Stancil recounts election night from the Harris campaign data team, where extensive technical preparation became irrelevant once the loss was obvious. He argues that the unseen work and contingency plans still mattered as evidence of people fighting on, and that being in the room—win or lose—changes you. The piece ends by urging readers to act, not only to affect outcomes but because participating is less hollow than spectating.

Stancil argues that individual votes in U.S. presidential elections are mathematically insignificant, yet people still vote because they want to participate in a shared democratic ritual. That belief is a fragile “noble lie,” and preserving it—especially during a contentious election—is essential to keeping democracy functioning.

The post argues that companies (and people) often break ideals not because they are uniquely bad, but because staying true to them requires exceptional success and leverage. Google’s early “don’t be evil” era is framed as a byproduct of a wildly profitable machine, and the author connects this to Mode’s sale and broader startup pressures. Ambition can force compromises: the bigger the goal, the stronger the pull to recalibrate values and move closer to the line.

The post argues that markets and companies may be drifting away from classical, data-driven valuation toward “vibes” and social consensus, using Trump Media as a case where fundamentals are irrelevant. It connects this to a broader shift in analytics culture: the data-driven movement delivered mixed results, and newer fashions like taste, founder instinct, and narrative are gaining ground. The core question is whether data-driven decision-making was a temporary vogue rather than an inevitable trajectory.

Stancil argues that dbt Labs’ Coalesce has shifted from a quirky, community-first gathering into a more conventional, revenue-minded corporate conference, reflecting the realities of monetizing open source. While that change brings a sense of loss, it also creates stability and clarity about what the event is for. The lasting value, he suggests, is in the small human moments that survive despite commercialization.

Stancil argues that Excel persists not despite its “anti-patterns,” but because those quirks are precisely what make it indispensable: local files, direct manipulation, and the ability to create private, versioned sandboxes. He suggests that any true successor must embrace these traits while adding scale, polyglot workflows, and optional cloud execution rather than trying to eliminate Excel’s messy flexibility.

The post argues that VCs contain two competing personas: a sincere helper who loves collaborating with founders and a harsher, status‑driven operator who wants to be feared and seen as decisive. While both modes can be useful, the author stresses that performative toughness and insecurity are easy to spot, and authenticity matters more than cosplay.

Stancil argues that workplace chat tools like Slack optimize for casual chatter, not thoughtful, triage-able work communication, and he contrasts them with Yammer’s threaded, inbox-oriented design. He then satirizes the AI-hype venture landscape with the fictional benn.ventures, using it to critique trend-chasing and status games in VC. He closes by reflecting on the stalled career arc of data leaders and the uncertainty of what comes next for a maturing cohort.

Stancil argues that foundational LLM vendors face a brutal cost structure where model improvement is a perpetual, escalating expense, not a one-time investment. Because compute is leased and model quality decays quickly against fast-moving and open-source competitors, their moats are thin and short-lived, making survival depend on constant fundraising or massive revenues. The ultimate winners, he suggests, will be whoever is ahead when market hype and capital dry up—not necessarily the best or fastest builders.

Stancil argues that “founder mode” is mostly a new set of rules masquerading as contrarian wisdom, and that copying Jobs-style leadership is just another cargo cult. The deeper point is that there aren’t real rules—effective leadership is doing something bold and making it work, i.e., “getting away with it.”

Stancil argues that persuasive, data-backed arguments can still be wrong, and only real-world testing separates good ideas from bad ones. The Nike “data-driven” controversy illustrates how numbers can make debatable strategies feel like facts, creating a culture that prizes quantified rhetoric over experimentation.

Stancil frames startups as a “money machine” whose controls are opaque, interconnected, and delayed, making the core challenge less about sheer effort and more about radical uncertainty. The stress comes from not knowing what to do, and neither cautious analysis nor frantic action reliably works; the only workable posture is a confident, even delusional, commitment to a chosen configuration.

Stancil argues that when a powerful new capability appears, the winning move is not to shoehorn it into existing workflows but to reshape the business problem to fit the capability. Applied to AI, that means abandoning attempts to make LLMs better baristas for today’s analytics tasks and instead finding problems that match what LLMs are uniquely good at, even if that means abandoning traditional “data-driven” rigor.

Stancil argues that typical pitch deck market-sizing slides often signal confusion about who the product is for and substitute big, vague TAM numbers for real planning. The better approach is to size and focus on the painfully small initial market that will actually buy an MVP, then use that as a deliberate path to expanding the market. The goal is not impressing VCs with huge numbers but demonstrating a credible, specific plan to reach scale.

Stancil argues that LinkedIn has become a peculiar, self-contained social network where professional performance replaces genuine social life, creating an artificial feedback loop of engagement and influence. He likens it to a Truman Show-style simulation: a place that feels socially rewarding but is insulated from real-world culture, celebrity, and spontaneity.

Using the Away CEO controversy and his own career arc, Stancil argues that Silicon Valley’s obsession with growth turns ambition into a default, socially enforced scoreboard. He contends that our ambitions are largely circumstantial—shaped by the environments we immerse ourselves in—and warns that we can spend years climbing ladders we didn’t consciously choose.

The post argues that in software and data tooling, the industry overvalues optionality and underestimates the cost of constant evaluation, suggesting teams should embrace deliberate lock-in to force focus on people and process. It extends the theme of too many options to advertising, warning that AI-driven personalization could make even the content itself variable and manipulable for each viewer.

Stancil argues that modern security and compliance practices are less about preventing breaches and more about allocating blame when they happen. Using Snowflake’s incident as a case, he says the cloud’s convenience shifts liability to users while vendors lean on audits and PR to claim innocence. He extends the “not my fault” logic to corporate behavior broadly, then to AI-era startup valuations driven by hype and talent rather than fundamentals.

Stancil argues that Silicon Valley used to run on familiar boom-bust cycles, but the AI era is breaking the old “physics,” replacing predictable rhythms with frantic rebranding, uncertain value, and career anxiety. The result is a tech industry that outwardly projects confidence yet privately feels unmoored, unsure whether this is another wave or a structural shift.

Stancil argues that dbt made data modeling easy but not simple, creating a generation of “analytics pretendgineers” who can write models but lack the architectural discipline to build durable data programs. He contends that the fix isn’t more features or education, but a framework with strong, opinionated guardrails—“dbt on Rails”—that enforces structure and prevents chaotic model sprawl.

Stancil argues that data products marketed as novel or specialized almost inevitably evolve into BI tools as customer demands, market incentives, and growth pressures pull them toward dashboards, self-serve access, and visualization. The real challenge isn’t clever differentiation but the stubbornness required to resist the gravitational pull of BI, even when it looks like the rational business choice.

The post argues that most data products are designed around clean, demo-friendly narratives, but real businesses face messy data, noisy dashboards, and contradictory signals. Because products are built for the “happy path,” they fail users in the “bad place” where interpretation and deduction—not discovery—are the real challenges.

Stancil argues that traditional data analysis and user interviews miss a powerful third approach: attentive observation that captures patterns we didn’t think to ask about. He suggests transformer-style models applied directly to behavioral event sequences could scale this “watching and remembering,” potentially delivering the core value of analysis without the brittle machinery of metrics and dashboards.

Stancil argues that the SaaS delivery model incentivizes shipping incomplete products, with customers effectively doing QA, and that this pattern has bled into hardware via cloud-dependent AI devices. He suggests this creates customer fatigue and financial strain for SaaS firms, opening space for a countercultural model: software sold as finished, one-time-purchase products. He then pivots to a related puzzle—why software hasn’t become a luxury status symbol despite its craftsmanship and ubiquity.

Stancil argues that BI’s pendulum swing between generic platforms and domain-specific apps misses the real problem: companies struggle more with interpreting metrics than defining them. He suggests a third form of BI that is opinionated about interpretation, offering frameworks and guidance on what the numbers mean and what to do next.

The post argues that the classic Silicon Valley story—software has near-zero marginal costs and therefore huge profits—breaks down when you account for the ongoing development needed to keep subscription software viable. High gross margins in SaaS mask the reality that continuous product updates function like cost of goods, keeping operating margins negative for many companies. Stancil calls for a new business theory that treats perpetual redesign as a core cost rather than a temporary growth investment.

Stancil uses a mock investor update for a fictional AI startup to satirize how startups contort narratives around growth, churn, and fundraising readiness. The piece argues that raising rounds to “pass the next grade” is a trap; money doesn’t fix a broken business, and founders should fundraise only when the current company can win the next stage.

Stancil argues that organizations, like individuals, rely mostly on fast, intuitive “System 1” thinking, so analytics teams are misguided when they try to out-reason it with pure analysis. Instead of chasing isolated actionable insights, analysts should work with System 1 by surfacing and updating the organization’s underlying mental models. The goal is to reshape heuristics and theories that guide many decisions, not just provide one-off recommendations.

Stancil argues that conferences rarely deliver real learning or authentic networking, often devolving into performative talks and repetitive social rituals. Still, he says they have a hidden value: they let you see many plausible versions of your future career all at once, helping you choose intentionally among finite paths.

Stancil argues that analytics teams often operate like a Ponzi scheme of promised future value: the discipline’s story of inevitable insight-driven impact rarely materializes for average teams. He suggests that since most decisions hinge on loose directional narratives rather than precise metrics, analytics may need to disband or rebrand—embedding in other functions and trading grand promises for practical influence.

Stancil argues that Silicon Valley’s “meritocracy” resembles political primaries: elite insiders and influencers shape which companies get attention, capital, and talent long before markets can judge them. Using the Medium vs. Svbtle example and the Devin AI launch, he shows how hype from the right people creates momentum that becomes reality, even when few have direct experience with the product.

Stancil argues that while AI hype has pulled capital and leadership attention across the industry, most real winners so far are “shovel sellers” rather than companies getting rich by using AI, which makes the other side of the bet undervalued. He suggests that as the dust settles, some firms will have over-pivoted, creating opportunities for those who resist the rush. In a separate but related critique of startup finance, he argues liquidation preferences distort incentives more than secondary sales, misaligning founders and investors around exit outcomes.

Stancil argues that secondary sales are not inherently unethical and that VCs’ moral posturing is inconsistent with how they actually reward founders who make money, even after failures. The real fairness issue isn’t whether founders sell, but whether employees—who bear the most concentrated risk—get transparency and a chance to judge or participate.

Stancil argues that natural language is a poor substitute for code when doing nuanced analysis, so LLMs shouldn’t directly write SQL for complex business questions. Instead, he sees AI as most valuable when it sits atop BI models or helps translate rough, mixed SQL/English sketches into precise queries, preserving guardrails and precision.

Stancil argues the modern data stack didn’t fail because the market turned; it failed because the product never converged on a shared, vivid vision of what the data experience should be. The early, clear goal of modernizing legacy tools drifted into vague promises of “data democratization,” leaving vendors building disjointed pieces without a Schelling point, so the ecosystem fragmented and stalled.

Stancil argues that data rarely transforms big, high-leverage decisions; it mostly nudges outcomes at the margins. The real advantage comes from finding small edges that can be repeated frequently, or better yet, using data to discover what you’re already slightly better at and then doing that more often so the edge compounds.

Stancil argues that most dashboard-driven analysis is closer to roulette than science: we see patterns and invent narratives in essentially noisy data. He praises Cedric Chin’s simple, iterative method for reading operational data because it gives permission to ignore false complexity and focus on actionable variation. The post then pivots to influencer culture, framing brand value as a social association game where the “wrong” endorsers can erode or even perversely strengthen a brand.

Stancil argues that corporate values are inherently vague and often serve as flexible, quasi-legal tools that can be weaponized by management or disputed by employees. The real issue isn’t that companies want values, but that these slogans obscure tradeoffs between profit and principle, creating ambiguity and conflict. He proposes replacing abstract values with concrete precedents that show how a company actually behaves under pressure.

Stancil argues that the modern data stack boom was driven as much by hype and attention-seeking as by genuine technical conviction, and that the post-ChatGPT shift to AI has hollowed out that “club.” The slowdown creates a better moment for durable startups to rebuild proven ideas more deliberately, while he also critiques the industry’s near-religious faith that “data” is inherently valuable.

Stancil argues that Carta’s doomed exchange (CartaX) wasn’t fundamentally different from Google’s data-driven ad marketplace, but it violated privacy expectations by making the human, personal act of outreach visible. The real failure was optics: people accept algorithmic data brokering at scale, yet recoil when a person appears to rifle through private records. He then generalizes to startup strategy, warning that “step two” visions often collapse because success at step one hardens a company’s identity and constraints.

Stancil argues that the value of news to OpenAI depends on whether LLMs become search-like or agent-like: copyrighted content is essential for an “omniscient” Q&A product but merely convenient for task-performing agents. The New York Times lawsuit is less about stopping AI and more about setting a lucrative legal precedent while the industry’s economic regime is still being defined.

Stancil argues that Silicon Valley startups operate on a “scheme” where equity is valued by the next buyer rather than by steady profits, and that this dynamic shapes incentives from hiring to executive comp. The Alteryx sale illustrates the scheme faltering under higher rates and stricter markets, pushing companies toward a capital-allocator model that prioritizes cash flow over hype. He closes by skewering a viral Twitter “funding” lie as a quasi‑fraud enabled by social media’s incentive structure.

Stancil argues that ChatGPT, like Google before it, rewards intuition and practice more than formal tricks, and that its real value today is speeding up tedious data work rather than generating novel analytical insight. Using Seth Stephens‑Davidowitz’s 30‑day AI‑assisted book as a case study, he says AI will flood us with faster, shakier analyses, making credibility and narrative framing even more decisive than the math itself.

Stancil argues that Microsoft’s Fabric and Databricks’ MosaicML deal show the modern data stack is too small to be the real battleground; the bigger fight is for the next “higher-order bit” in AI infrastructure. Databases and MDS tools can’t win by eating smaller apps because cloud providers still sit above them, so vendors are repositioning to become AI model infrastructure providers where access to data is the key advantage.

Stancil argues that the real power of data is not its objectivity but its aggregatability, which makes structured numbers easy to summarize while rich qualitative information remains costly to synthesize. He contends that LLMs can act as an “aggregate function” for text, unlocking the value of unstructured data that traditional data stacks ignore. The future of data platforms, he suggests, is less about squeezing more out of structured data and more about making qualitative corpora computable.

Stancil argues that the OpenAI boardroom drama crystallized existing AI disagreements into tribal identity politics, where siding with Altman became a proxy for accelerationist allegiance rather than reasoned debate. He then contends that Altman’s mythos is less about unique merit and more about whose stories get amplified, urging public recognition of underappreciated people.

Benn argues that 2021-era data startup valuations were inflated by stacked multipliers—market exuberance, community hype, and category narratives—that have since collapsed as rates rose and sentiment cooled. He says many companies can’t “grow into” those valuations, so the most plausible path is opportunistic fundraising that preserves the signal of success even if the market fundamentals are weaker.

Stancil argues that most enterprise AI features are being rushed out for hype, but real usefulness demands messy, unglamorous infrastructure work that many companies won’t sustain, echoing the blockchain cycle. He then contrasts BI tools with spreadsheets, claiming Excel’s static, file-based nature is its moat, and closes with a satirical take on privatizing productivity gains inside companies.

Stancil argues that founders are consistently surprised by startup difficulty because the culture overemphasizes clever wedges and underestimates the years of unglamorous work required to build durable companies. The only enduring moat is sustained effort: you can hack attention or early growth, but lasting advantage comes from doing the boring, direct, head‑to‑head work better than incumbents.

The post assembles firsthand accounts from people in Israel and Gaza to show how the war is devastating daily life, safety, and work across both communities. It argues that beyond the headlines, the human cost is vast and intimate, and that the least we can do is acknowledge the tragedy and offer practical support.

Stancil uses Coalesce in San Diego as a lens to argue that the data industry is maturing away from hype and toward process-heavy, often boring work, with AI notably absent. That shift strips away the “main-character” career narrative and forces practitioners to ask whether they’re in the field for the craft or for the promised spotlight. He hopes the community keeps making room for human celebration even as the industry professionalizes and attention moves on.

Stancil pauses his planned data post to acknowledge the conflict affecting readers in Israel and Palestine and express hope for their safety. He offers the platform to those directly impacted, inviting them to share stories or perspectives.

Stancil argues that the popular story that venture capital “ruins” good products is incomplete and often misattributes cause. The real degradation comes not from the cash itself but from the growth imperative that VC financing imposes, which pushes companies to expand features and markets beyond a product’s natural fit.

Stancil argues that there is a persistent gap between what data tools can do and what teams actually use, much like glossy Office “vision” videos versus real messy workflows. The problem is less about missing features and more about adoption, skepticism, and the lack of belief that investing in a tool’s full potential will pay off.

The post argues that the common startup plan to win SMBs first and then graduate to enterprise rarely works as imagined, because enterprise sales take far longer, demand different products and buying motions, and can derail a company’s model. Stancil says most startups either need a path to scale within their initial buyer segment or start as enterprise-first from day one, rather than assuming a smooth transition later.

Stancil argues that the data industry’s “purple” era—where analytics people bridge business and engineering through bespoke insight-hunting—has not delivered reliable value, despite better tools and more talent. He suggests the real maturation path is toward standardized, accountant-like practices with shared metrics and rules (e.g., SOMA) that trade novelty for trust and institutional reliability.

Stancil argues that the modern data stack is consolidating not just in vendor count but in category count: buyers are compressing spending and pushing tools to become broader, more BI-like platforms. As a result, specialized tools either get absorbed, stagnate as niche players, or rebrand into wider enterprise suites, and $10M ARR or “category leader” status no longer signals inevitability.

Stancil argues that the stigma around down rounds is exaggerated and often serves VC incentives more than startup interests. While down rounds can hurt employees, the real damage is usually optics-driven: fear of reputational harm pushes founders into worse terms and riskier bets than they should take.

Stancil uses a bar-pricing parable to argue that pricing models are economically flexible but socially constrained by expectations. He contends that dbt’s usage-based pricing is theoretically sensible and incentive-aligned, yet the real conflict is with entrenched norms from its free/open-source roots, which will shape who it ultimately builds for.

Stancil argues that most LLM analytics chatbots are misaligned with real analytical work because they answer requests without the context needed to produce useful or correct outputs. He proposes a “smol analyst” workflow—iterative, feedback-driven drafting like a junior analyst—which could make LLMs more practical, while noting that data’s need for transparent logic makes this harder than in software.

Stancil argues that the data industry’s core pain isn’t tooling or process but the need to work on layered caches; unlike software, data pipelines rarely run live end-to-end. If instant, on-demand processing were possible, reliability, debugging, and trust would improve dramatically, but until then, copying software engineering practices only helps at the margins.

Stancil argues that the business world has overcorrected against intuition by treating emotions as inadmissible evidence, even though feelings often act as leading indicators of what data will later confirm. He contends that companies should surface emotions alongside analytics to make more honest decisions, avoid self-deception, and unlock bolder moves. Emotions shouldn’t drive choices blindly, but they should inform them as real signals.

Stancil argues that clean data is less a solvable engineering problem than a tradeoff that teams often choose, because businesses evolve faster than their data systems can. Tools like SDF help mitigate issues, but they are edge improvements that don’t change the messy, exploratory nature of data work. He suggests shifting from chasing stability to designing for resiliency and easier refactoring.

Stancil uses the ThoughtSpot–Mode merger to explore how acquisitions in BI can fail, arguing that strategic fit is less important than protecting user experience, respecting differing product philosophies, and acknowledging market shifts. He warns that integration traps, mismatched customer needs, suite consolidation, and AI-driven paradigm shifts are the real risks, while the biggest failures often come from well-known, unaddressed weaknesses.

Stancil argues that most SQL chatbots are aiming at the wrong problem: LLMs can draft queries in toy schemas, but real-world analysis requires extensive context, governance, and BI-like infrastructure. He contends that LLMs are better at creative, hypothesis-generating work than at precise, deductive querying, which flips the traditional promise that tech automates the tedious parts so humans can do the fun parts.

Stancil argues that we conflate “databases” with structured, analyzable data, while the far larger opportunity is an “informationbase” that indexes and retrieves useful facts, documents, and context without heavy modeling. Snowflake’s Document AI hints at a shift from data warehousing toward Google‑like information retrieval, even if the most likely explanation is simply customer demand. The broader point is that search and retrieval for operational questions may matter more to most organizations than analytics-grade data pipelines.

Stancil reflects on how a decade of startup ambition and workaholism strained his relationship with his parents, costing them time and attention he never gave back. With Mode’s sale, he pauses his usual arguments to offer a heartfelt apology and gratitude, acknowledging their sacrifices and affirming the personal meaning of what was built.

Stancil uses microwaves as a metaphor for data: ubiquitous, useful, but potentially capped in transformative value. He argues data tooling may have hit a plateau as a utilitarian appliance, and that only a shift to fast, natural‑language access (via LLMs) could unlock a true second act by empowering many more people to ask and answer questions.

Stancil tests whether polite prompts improve ChatGPT performance and finds the opposite: neutral phrasing yields the best accuracy, while nice or mean tones slightly degrade results and increase verbosity. Despite the data, he frames the choice to be polite as a Pascal’s Wager: a tiny performance cost might be worth it if kindness matters in a future where AI could punish us.

Stancil argues that data teams over-invest in validating inputs and lineage, yet trust in metrics actually comes from output consistency over time. Because users judge credibility by whether numbers stay stable across reports, he proposes tools and practices that surface changes and drift in outputs rather than just monitoring pipelines and tests.

Stancil argues that most talks are forgettable and audiences expect to be bored, which creates a low-risk environment for bold choices. Because cautious and daring talks are equally ignored, the best strategy is to gamble on entertainment, pace, and narrative cohesion. The goal is to keep attention and create a memorable experience rather than deliver didactic takeaways.

Stancil argues that the modern data stack’s promise of modular, user-driven tools has been undermined by the absence of a true platform—there’s “no potato” to hold the pieces together. Microsoft Fabric responds by collapsing the stack into a single integrated product, which could appeal to enterprises and shift the market from “legacy vs. modern” to “all‑in‑one vs. best‑of‑breed.” He concludes that the real opportunity is a neutral platform layer that enables genuine modularity, ideally from a cloud provider building infrastructure rather than a monolithic suite.

Stancil argues that the most disruptive impact of generative AI is not that content becomes free, but that it becomes immediate, which changes how we seek and use information. That shift will erode search-driven, click-based web behavior and favor direct answers, known destinations, and on-demand interfaces, reshaping products like data catalogs and the broader web.

Stancil uses the Kasparov–Deep Blue story and Sutton’s “bitter lesson” to argue that AI progress comes less from encoding human domain knowledge and more from scaling computation and search. He then applies that lens to data teams, warning that human-built semantic models may constrain impact and suggesting that more compute-centric approaches could uncover strategies we can’t easily see.

Stancil argues that today’s “universal semantic layers” focus on metric extraction and accessibility via APIs, but that makes them effectively BI tools without charts rather than truly universal layers. Because data consumption preferences are diverse and tools require different computational models, a semantic layer that only serves metric tables can’t support the broader ecosystem. A real universal layer would combine shared entities with flexible computation like SQL, and he’s skeptical current approaches can do that yet.

Stancil argues that the “two-step startup” is a myth: companies rarely evolve into a fundamentally different second act, and their founding product definition tends to persist from seed to IPO. Because partial pivots mostly fail, founders and investors should be brutally honest about whether the original idea can scale or must be fully rebuilt. Data dashboards won’t surface this truth; the real signal is felt in conviction and customer pull.

Stancil argues that analysts can resemble “nicer, kinder” Jared Kushners: parachuting into others’ problems with shallow context and overconfident analysis that adds little value. He suggests shifting data teams from decision-support consultants to newsroom-style publishers who build company-wide, ambient understanding through continual narrative updates. A better-informed organization, not better point decisions, is the real competitive edge.

Stancil argues that dbt Labs’ success rests as much on its community and human-centered approach as its product, but the market tailwinds that protected it are fading. He lays out three futures—stay the course, go on the offensive into adjacent markets, or sell—while noting that the rise of LLM-driven data stacks could reshape what “dbt” needs to be.

Stancil argues that the modern data stack, once the corrective to Hadoop-era pain, has grown into an overgrown ecosystem of tools, costs, and complexity. He suggests we are entering a liminal period where AI/LLMs will trigger a new discontinuity, making the modern data stack’s philosophy ill-suited for the next era. The core claim is that data teams and vendors must adapt to AI-native infrastructures or risk becoming yesterday’s stack.

Stancil argues that as LLMs proliferate, they will sort into “low-skill” and “high-skill” tiers much like human labor markets, with cost and capability gaps driving differentiation. This could create new forms of inequality and competitive advantage, as premium models compound small advantages over time, even if baseline tools become cheap and widespread.

Stancil argues that OpenAI’s real destiny isn’t to become a consumer app platform like the iPhone, but an AI infrastructure provider akin to AWS. Generative models will become a ubiquitous, mostly invisible backbone powering countless products, and the biggest prize is dominance as a foundational utility rather than direct user attention.

Stancil argues that the SVB bailout was justified either to prevent systemic panic or to protect startups deemed uniquely vital, but that second rationale implicitly demands special treatment that Silicon Valley doesn’t want to admit. He suggests SVB may be a “Lehman moment” for tech, exposing a subprime-like financial scaffolding that could trigger regulatory reckoning and shows how risky innovation finance has become.

Stancil contrasts JPMorgan’s massive data investment with a hypothetical NBA team’s analytics push to argue that data initiatives work when they’re aimed at clear, concrete problems, and fail when they’re driven by hype and vague promises. He’s skeptical of open-ended “digital transformations,” but sees value in the messy experimentation of the data boom, even if much of it proves wasteful.

Stancil argues that the concept of a “data warehouse” has become fuzzy and skeuomorphic: we recognize examples by vibe, not by a stable definition. As architectures shift toward storage-less, serverless, and file-based compute, he suggests warehouses are already breaking apart and may evolve into something like dbt running directly on object storage.

Stancil uses his anxiety about flying to argue that emotions often overpower facts, making rational self-talk an unreliable defense against fear. He warns that modern AI, especially chatbots, is uniquely adept at shaping feelings, so the larger risk is not misinformation but engineered emotional manipulation at scale.

Stancil argues that startups can fall into “customer capture,” where divergent early customers pull a company into incompatible directions, eroding product coherence and market identity. This is especially acute in the fragmented data tooling landscape, where many adjacent expansion paths look like optionality but become unsustainable variability. The antidote is a disciplined, caring core identity and the willingness to say no to customers and investors until the company’s center of gravity is strong enough to expand.

Stancil argues that data teams often overvalue objective truth-telling, when their most useful role in business may be to align people around a chosen strategy. Because execution and belief shape outcomes, selective, narrative-driven use of data can be more valuable than neutral analysis, even if it feels uncomfortable.

Stancil argues that the data industry has inflated the word “insight” into a vague marketing promise, even though most users and businesses mainly want reliable, basic reporting about what’s happening. He contends that data teams chase flashy analysis because it is socially and commercially rewarded, while the harder, more valuable work of accurate reporting is undervalued.

Stancil argues that while LLM demos look impressive, they fail in real-world data environments because messy schemas, ambiguous definitions, and complex queries overwhelm current models. The real revolution will come not from smarter models alone but from rebuilding data structures to be AI-friendly—standardized, machine-readable schemas that shift analytics work from bespoke design to factory-like production.

Stancil argues that many data teams may lack true product-market fit: they are often sold rather than eagerly bought, and persistent struggles to gain influence suggest a deeper mismatch with stakeholder demand. He contends that the field may be extrapolating from a few early “customers” and should rethink what services are actually wanted, even if that means reinventing the role.

Stancil argues that the data tooling boom favored neutral, modular ecosystems, but the market’s fragmentation and tighter budgets are pushing vendors into direct competition. Instead of simple consolidation into big-platform bundles, he proposes a “conglomerate” model where adjacent data products live under one umbrella, staying independent yet more seamlessly integrated. This could preserve product character while delivering the cohesion customers increasingly demand.

Stancil argues that math is professionally overrated in data work: most roles rely on basic arithmetic and judgment, not advanced theory. The real value of “mathiness” is cultural signaling—using quantitative language and artifacts to claim authority and shut down qualitative debate—so we should prize communication and domain understanding more than we do.

Stancil reviews a decade of data-industry predictions and finds that fewer than half were mostly correct, with many being vague, trend‑extrapolation filler. He argues that forecasts repeatedly promise imminent breakthroughs (AI, predictive analytics, streaming, data marketplaces) that arrive later or not at all, while genuinely important developments often go unpredicted. The upshot: expect 2023 to miss in familiar ways, with surprises filling the gaps.

Stancil argues that penalty shootouts are a different skill than the 120 minutes of soccer that precede them, creating a strategic arbitrage opportunity. Because shootouts are common and roster spots are underutilized, teams could meaningfully raise their World Cup odds by carrying dedicated penalty kick specialists. The idea is a low-cost, “Moneyball” style edge that conventional roster construction ignores.

Stancil argues that despite sweeping technological advances, productivity hasn’t improved because the tools we use shape behavior more than any process or best-practice talk. Slack exemplifies this by incentivizing frantic, low-quality communication, so the real path to better work is to build products that enforce better processes, especially for data teams. In data, he claims only purpose-built tools will shift norms around requests, alerting, and decision-making.

Stancil argues that analysis relies on the difficulty of constructing convincing arguments, which gives it a kind of legitimacy even when people are biased and imperfect reasoners. Large language models erase that friction by generating plausible narratives on demand, making it harder to distinguish truth from motivated reasoning and undermining decision‑making.

Stancil riffs on watching Lou Gehrig’s retirement speech and realizing he is the same age, which sends him into compiling a sprawling, unnecessary spreadsheet of famous people’s ages at notable moments. The post uses that absurd catalog to poke at age-based comparison, mortality anxiety, and the futility of measuring life against milestones.

Stancil argues that despite the rough edges and hype, the modern data stack represents a huge leap forward from the brittle, expensive, and manual systems of the early 2010s. Yet he highlights a paradox: data teams delivered substantial business impact even with inferior tooling, so the key question is why today’s far better tech hasn’t produced proportional value gains.

Stancil argues that the internet has entered a new, hotter phase of chaos where platforms collapse fast, frauds flourish, and attention cycles overwhelm any stable order. He portrays a culture trapped in a runaway machine, where even serious news is instantly drowned by the next manic catastrophe. The only viable stance, he suggests, is to accept the turmoil and write for the void rather than for a stable audience.

Stancil argues that the data industry’s shift from tools to people and process misses a deeper constraint: most companies don’t have data with the leverage of Silicon Valley giants. The modern data stack looks powerful, but for many businesses the data is more like peat than oil—limited value means copying big-tech practices often yields disappointing returns.

Stancil argues that elite institutions and Silicon Valley share a performative culture that rewards confident, untested ideas rather than rigorous scrutiny, producing a class of leaders who are affirmed rather than challenged. He sees the VC pitch dynamic and Twitter’s Musk-era chaos as parallel examples of the “very smart boy” myth and its consequences. Twitter becomes the live-fire test of whether tech’s celebrated first‑principles bravado is wisdom or just noise.

Stancil argues that data teams inside startups operate like collectivist service orgs, which leads to fuzzy prioritization and hidden politics. He proposes an internal market with credits and auctions to price data work, claiming it could allocate effort more efficiently and transparently while still letting teams steer strategy. He ends cautiously, noting the model could work with safeguards but needs regulation to avoid a cutthroat culture.

Stancil contrasts ambition-driven careerism with work chosen for its intrinsic value, arguing that communities thrive when people show up for the work rather than the payoff. He uses dbt Labs’ Coalesce as a rare, genuine data community that feels like college, while acknowledging that scale and business pressures will eventually erode that culture. The piece ends with a call to appreciate the community now, even knowing it won’t last forever.

Stancil argues that internal tools often look like startup gold because they solve a real problem inside a company, but that success is a mirage driven by a captive customer, edge-case needs, and effortless internal distribution. Once outside the company, those tools face messy, varied workflows, politics, and integration realities that make them poor fits for broader markets. He suggests investors should be more skeptical and look to consultancies for better-validated product ideas, noting his own experience at Mode as a cautionary example.

Stancil imagines a plausible failure case for dbt Labs, arguing that the company’s success could be undermined by a mismatch between its SQL-first core and the added complexity of semantic layers, pricing shifts, and community fatigue. He contends that these pressures would weaken dbt’s moat and invite both direct and indirect competitors. He ultimately concludes the failure narrative is unlikely because dbt’s product value is real and enduring.

Stancil argues that the iPhone is remarkable for delivering a fundamentally egalitarian product experience across social strata, unlike Twitter, where each user inhabits a materially different version shaped by identity and prominence. Because these experiences are unmeasurable and often inaccessible, leaders and data practitioners must approach product decisions with humility and seek lived experience beyond dashboards.

Stancil argues that data contracts address a real problem—brittle, implicit expectations between data producers and consumers—but negotiated agreements between teams are impractical and misdirected. Instead of people-heavy contracts, he favors technical safeguards built by data teams (e.g., schema/staging tests) that validate data before it hits production without forcing providers into ongoing negotiations.

Stancil argues that Fivetran’s dominance is not inevitable despite riding two waves: SaaS proliferation and centralized data warehouses. He lays out three ways Fivetran could fail—if the need to move data fades, if SaaS vendors and warehouses change how data is moved, or if a better/cheaper tool displaces it—while ultimately betting on inertia but acknowledging how quickly the “impractical” can become the new normal.

Stancil argues the “data as a product” idea is incomplete without a clear definition of what counts as production. Because data work often blurs one-off analysis and durable assets, teams end up maintaining a sprawling, accidental production surface. He proposes explicitly declaring production assets and letting everything else be temporary, with real support and deprecation for the chosen few.

Stancil argues that Snowflake’s apparent inevitability is less certain than the industry consensus suggests, and runs a “pre‑mortem” on how a dominant data warehouse could lose its edge. He outlines multiple plausible failure paths—cost dynamics, architectural fit, market overreach, and margin pressure—while still acknowledging Snowflake’s current strength.

Stancil argues that habits and identities often calcify from chance events, and we mistake their persistence for virtue. He suggests we are less bound by the past than we believe, and should prioritize future possibilities over inherited routines or traditions. The right question is whether a habit still serves who we want to become, not how long we have maintained it.

Stancil argues that while analytics teams obsess over preventing errors, they barely grapple with what to do when mistakes inevitably slip into production. Because analytical errors shape decisions and persist, he questions whether engineering-style blameless retros are sufficient and suggests borrowing from journalism’s accountability. He also proposes rounding metrics to signal uncertainty and reduce the blast radius of inevitable corrections.

Stancil argues that data orchestration is backwards: we schedule pipelines from upstream triggers instead of from the freshness guarantees we actually care about at the end of the lineage. He proposes a reverse-ETL-style model where each table declares latency requirements and the system derives the DAG and run timing to meet those guarantees, improving maintainability, cost control, and alert relevance.

Stancil argues that analysts are screened on poor signals like resumes and brand names because their work is hard to showcase, which shuts out strong talent. He proposes replacing boot-camp style credentialing and take-home tests with concise analytical portfolios that emphasize thinking, storytelling, and real project context. A short, structured slide deck could surface skill and potential more fairly while improving interviews.

Stancil argues that the modern data stack lacks a true “single pane of glass,” and that multiple tool categories are converging to claim that central control role. This competitive rush, driven by venture funding expectations, makes fragmentation worse until the market forces a single winner.

Stancil argues that while data helps, it is not a decisive competitive advantage against stronger talent, intuition, or speed—especially in early-stage companies. Data’s real value is a small, compounding edge across many decisions, not dramatic breakthroughs on a few big bets.

Benn argues that data teams hardcode business logic all over their stacks, leading to inconsistent definitions and opaque concepts for anyone outside the data team. He proposes centralizing these logical variables (and eventually logic itself) in a shared configuration file so definitions stay consistent, discoverable, and editable. This could shift workflows toward more composable, collaborative analytics engineering, much like dbt did for transformations.

Stancil argues that as more data people work at data vendors, community interactions are increasingly shaped by corporate affiliation, turning peers into prospects and conversations into subtle marketing. The result is a loss of genuine, human connection in the data community, which he thinks can be restored only by centering shared personal experiences over tools and company agendas.

Stancil argues that the rise of analytics engineering isn’t just hype or better tooling; it reflects analysts’ deeper preference for structured, engineering-flavored work over the messy social reality of analysis. The role offers clear boundaries and tangible outputs, making it an attractive escape from organizational politics and ambiguity. This shift suggests the industry may need to rethink what it means to be an analyst and be more honest about what actually satisfies people in data roles.

Stancil pauses his usual light commentary to mark the Supreme Court’s decision as a grave moral and democratic failure. He argues that six unelected justices are imposing their will, stripping rights, and signaling further harm, making the day too serious for his normal industry jokes.

Stancil argues that Snowflake’s new application framework signals a shift from “data apps” as dashboards on top of the warehouse to apps that run inside the warehouse itself. If Snowflake executes this vision, it becomes more like an iPhone platform than an app store, enabling deeper functionality, easier distribution, and a new wave of data products. He concludes that this could reignite the data tooling ecosystem rather than mark its decline.

Stancil argues that Google has amassed an impressive but incoherent set of data products, like a kid with powerful Pokemon cards but no cohesive deck. The opportunity is to define a clear buyer (the "purple people" in data orgs) and sell an integrated, easy-to-buy stack rather than overpowered but disconnected tools, a miss driven by tech-first culture and compute-driven sales.

Stancil argues that current AI/automation tools in analytics mostly disappoint because they’re built to mimic analysts’ existing tasks, which often require human judgment and context. He suggests a more promising future is AI as a creative partner—posing novel questions, reframing problems, or generating alternative structures—changing what analytics work even looks like.

The post argues that confusing data job titles create unstable pay bands and inequitable compensation across the industry. Stancil contends that analysts’ work—translating messy business problems into persuasive, high‑impact decisions—is harder to develop and more valuable than analytics engineering, yet Silicon Valley’s bias toward technical skills inflates engineer pay. He calls for intentionally paying analysts more to counter this bias and to elevate “soft” skills often gendered and undervalued.

Stancil argues that Silicon Valley (like DC) has built a status hierarchy around external scoreboards—valuations, funding rounds, and stock prices—that distort how people measure their work and worth. The market crash reveals how fickle those metrics are, and he suggests using the downturn to detach from hype and refocus ambition on controllable, intrinsically meaningful outcomes.

Benn argues that for startups and big strategic choices, committing fully to a path matters more than picking the “perfect” option; optionality and constant iteration often dilute focus and stall progress. He extends this to data work, claiming analytics can undermine commitment by highlighting uncertainty, so data teams should prioritize helping organizations stick to decisions to improve outcomes.

Stancil argues that the modern data stack’s ideals—modularity, open source, and avoiding lock-in—are less important to most buyers than consolidated, tightly integrated suites that simply work. Microsoft’s dominance in BI and databases shows that customers often prefer convenience, bundling, and sales reach over craft and composability. The likely future, he suggests, is consolidation, not a sprawling best‑of‑breed ecosystem.

Stancil argues that Elon Musk’s $265B fortune is so large that normal comparisons fail, so he offers vivid, often absurd benchmarks to make the scale legible. He then pivots to the real concern: a single private citizen controlling a major channel of public discourse is dangerous regardless of who that person is, and supporting the deal often boils down to personal affinity for Musk rather than principle.

Stancil argues that the more common way statistics mislead isn’t through doctored charts but through the words that frame them. Adjectives like “narrowly,” “nearly perfect,” or “spiked” interpret data for us, so we accept a narrative we might not reach on the numbers alone.

Stancil argues that SQL’s dominance has encouraged data teams to model business logic directly in queries, but many real business processes aren’t relational and get poorly approximated as a result. He suggests that semantic layers should move beyond pure SQL, via richer logic languages (like Jinja-heavy dbt or Malloy) or even non-SQL databases, to capture complex business models more faithfully.

Stancil argues that the Big Data era is ending not because data got less important, but because the industry finally shifted from grand, hype-driven promises to boring, practical tools like Snowflake and dbt. The real breakthrough is lowering friction and cost so “ordinary” teams can do more with familiar workflows, which will quietly unlock new use cases over time. He contends Databricks and similar tools should stop selling visionary narratives and instead sell themselves as simple, better databases.

Stancil argues that writers and data professionals inevitably tailor what they say to the audience they want to impress, often defaulting to an insular circle of industry peers and influencers. This inward focus distorts priorities in the data community—conference talk, tool debates, and discourse drift toward self-referential validation rather than serving end users. The remedy is to deliberately broaden the audience we write and speak for, seeking respect from people outside the data bubble.

Stancil argues that OLAP cubes persist as a conceptual “ghost” in modern BI tools: even when the infrastructure no longer requires cubes, interfaces still expose confusing, cube-like abstractions. He contends that BI should hide internal data structure and instead present data in the simple forms users actually think in—metrics and flat tables—so tools are understandable without implicit knowledge.

Stancil argues that while revenue eventually matters, early-stage startups should resist making it their defining KPI, especially during market downturns. Using revenue as the main yardstick distorts incentives and alignment; better, durable metrics help teams focus on long-term value creation rather than short-term extraction. Data teams, in particular, should champion alternative north stars that are singular, reachable, and enduring.

Stancil argues that Snowflake’s acquisition of Streamlit is the start of a long-term shift toward a true data app platform, not just another top-of-stack SaaS play. He contends that a Streamlit framework plus a Snowflake-backed app marketplace could make data apps cheaper to build and sell, catalyzing a broad ecosystem of small, specialized tools built directly on the data layer.

Stancil argues the real shift in the data world isn’t bundling vs. unbundling of tools, but the blurring of roles between analysts and everyone else. Based on Mode’s usage, analytical behavior spreads across functions and deepens over time, making analysis a team sport. The implication is that “modern data experience” should serve a future where many more people want to work like analysts, not just unify tooling.

Stancil argues that in moments of clear moral crisis, the pressure to add commentary or analysis is misplaced; sometimes the right response is to acknowledge the obvious and refrain from performative insight. He emphasizes that numbers, clever takes, or detached cynicism aren’t required, and it’s acceptable to feel helpless, speak plainly, or stay quiet.

Stancil argues that undisclosed angel investing creates hidden conflicts of interest in the data community, distorting discourse and reinforcing insider cliques. The same transparency norms applied to vendors should apply to investors, because credibility in a tight-knit ecosystem depends on openly disclosing financial ties.

Stancil argues that Excel is software’s greatest achievement because it turned hundreds of millions of non-programmers into programmers by making simple automation accessible. He proposes a more general “Excel for everything” that lowers the prerequisites of scripting, letting people automate any task through a block-based, spreadsheet-like interface, and reframing programming as a universal tool rather than a career identity.

Stancil argues that while the back of the modern data stack has converged on clear architectural norms, the consumption layer (analytics/BI) is fragmented and unsettled. This diversity reflects real differences in people and business use cases, yet the industry still lacks shared standards and is muddied by vendor incentives. He expects eventual consolidation, driven by community consensus or economic gravity, even if innovation thrives in today’s chaos.

Stancil argues that data teams aren’t service organizations, but they face similar pressures and can learn from support teams’ habits. By treating stakeholders’ perceptions as the reality to understand, prioritizing empathy over cleverness, and valuing maintenance, data teams can be more effective and resilient. The core shift is from consultant-as-expert to detective-and-therapist who helps people feel heard and move forward.

Stancil uses a dataset of Wordle scores shared on Twitter to show that most players win easily and that "hard mode"—reusing known greens—actually performs better than more exploratory strategies. He then pivots to the bigger lesson: data analysis is shaped by the questions we choose and the paths we ignore, so truth-telling requires acknowledging those biases.

Stancil argues that data startups overhype “open standards” as the modern equivalent of network effects, but most so‑called standards are fragile and transient. Durable success usually comes from building useful products and brands, not from winning imaginary winner‑take‑all markets. He proposes a practical, paid “switchboard” for metadata plumbing rather than another aspirational standard.

Benn argues that today’s warehouse-centered modern data stack can’t really support a thriving ecosystem of data apps, and reverse ETL is a brittle stopgap rather than a true platform. He proposes an “entity layer”—a generalized, interoperable semantic layer for business objects—as the missing bridge that could make data apps reliable and eventually turn the warehouse into an operational brain.

Stancil argues that blogging should be treated less like Twitter’s contrarian take economy and more like TikTok’s remixable, story-driven culture, where familiar ideas can be retold with fresh perspective. He shares a candid, iterative writing process—freeform conversation first, structure later—to help hesitant writers start and keep momentum, emphasizing that authenticity and enjoyment matter more than polish.

Stancil argues that modern data observability catches structural and statistical issues but misses the most dangerous failures: semantic anomalies where data is technically valid yet conceptually wrong. These errors evade alerts, erode trust, and can drive major business mistakes, so teams need tools and practices that directly encode business meaning and keep humans in the loop.

Stancil argues that the early-2010s tech culture he experienced was not a temporary excess but a symptom of deeper structural incentives that breed toxicity. In contrast, the analytics community—exemplified by dbt’s Slack and Coalesce—has built a healthier, more inclusive culture, proving that tech’s ambition and impact can be separated from its worst behaviors. He concludes that software should learn from analytics by exporting this cultural model outward into the broader industry.

Stancil argues that dashboards and reporting aren’t distractions from “real” analytics work; they are how companies construct a shared reality in an abstract, non-physical world. Metrics don’t just measure the business—they define it—so consistent dashboards are foundational for trust and decision-making.

Stancil argues, half-seriously and with mock rigor, that the name “coffee cake” is a linguistic fraud because the cake contains no coffee. He marshals examples and a pseudo-statistical survey of cake names to claim the exception is absurd and should be corrected by renaming it “coffree cake.”

Stancil argues that analytics leaders get stuck on a career treadmill just below the C-suite because the chief data/analytics role is defined as an operational, second-class position. He proposes splitting the function like engineering does: keep a VP of data for management and create a chief analytics officer who sits with executives to do strategic, high-leverage analysis. This would improve executive decision-making while giving strong analysts a real top-level career path.

Stancil argues that data and analysis are often used not to clarify decisions but to escape them, letting leaders off the hook when choices are hard and evidence is inconclusive. He contends that real leadership requires courage to decide and own outcomes when the data cannot provide certainty, and analysts must recognize when their role is to inform rather than to defer responsibility.

The post argues that analytics lacks a reliable way to measure itself, leading to circular judgments, bias, and poor incentives. Benn proposes a concrete metric: evaluate analysts by how quickly their work helps others make decisions, because that directly reflects the purpose of analysis and improves communication, focus, and reputation over time.

Stancil argues that the modern data stack’s fragmentation hides compounding costs: each new tool and higher-fidelity use case triggers cascading usage fees across vendors. Because data tooling is mostly services-oriented and priced inconsistently, the ecosystem is economically unstable unless it consolidates or builds better coordinating layers. The likely outcome is fewer dominant platforms with satellite products and stronger cost management.

Stancil argues that consumer products like Yelp made data-driven decisions feel natural by embedding data inside the experience, while corporate analytics isolates data in dashboards divorced from work. The future of operational analytics is to integrate focused, disciplined data experiences directly into workflows and build a data “operating system” that enables those experiences rather than just more BI tooling.

Stancil argues that the Looker–Tableau integration signals a structural split in BI: governance/modeling (LookML) is separating from visualization and exploration. This shift redefines BI away from all-in-one suites toward a metrics/governance layer paired with best-in-class exploration tools, with big implications for vendor consolidation and the economics of the modern data stack.

Stancil argues that the public Facebook leak revealed something rare: not just debate about social media, but about the quality of analysis itself. Because most analysis is proprietary, the field obsesses over tools and structures while the actual work stays invisible, making it hard to define or improve what “good analysis” looks like. He concludes that quality is subjective and multi-dimensional, and the way forward is to expose more real analysis so standards can evolve.

Stancil argues that BI and visualization are far more complex than feature backlogs suggest, so “thin” consumption apps coordinated by a data OS are unlikely to deliver a coherent user experience. He proposes a middle path: a strong, general-purpose BI “planet” for exploration, surrounded by specialized satellites for specific use cases.

The post argues that the analytics community has built a reputation for being welcoming, but that welcome is not shared equally, with Black professionals profoundly underrepresented in conferences and the field. The author contends that diversity should be pursued as a moral imperative, not a business case, and that the community’s blind spots risk embedding and amplifying racism through data-driven systems.

Stancil argues that BI, like early software, has been unbundled and stripped of legacy connotations, leaving today’s BI as a diminished fragment focused on visualization and reporting. He contends the next generation of BI should be a universal consumption layer that integrates self-serve and deep analysis, sits atop shared governance, and centers collaboration between analysts and non-analysts.

Stancil argues that just as the Supreme Court’s rulings are shaped by human preferences and political context, data-driven arguments are similarly tilted by subjective choices and prior beliefs. Data feels like neutral ground, but the metrics and analyses we treat as objective are built from arbitrary definitions, incentives, and power—so numbers often mask opinions rather than replace them. The remedy isn’t more data, but more humility about our limits and more skepticism toward quantitative authority.

Stancil argues that the analytics ecosystem’s core problem isn’t just “too many tools” but the lack of an operating system that standardizes how tools integrate and behave. He critiques extreme centralization and decentralization (including the data mesh as commonly defined) and proposes a delegated, OS-like layer—potentially embodied by dbt—that abstracts heterogenous data sources and provides shared utilities for the stack.

Stancil argues that creative work has a generative phase that cannot be scheduled the way editorial work can, and meetings are especially destructive to that fragile state. By treating meetings as fixed and creativity as the flexible block, organizations stretch creative tasks into slow, demoralizing slogs. He proposes rebalancing calendars to allow people to decline meetings when in generative flow, even in data roles.

Stancil argues that while the modern data stack has delivered dramatic technical advances, it has failed to produce a coherent, reliable experience for everyday analysts and business users. The core challenge is not more tools or abstract “culture,” but designing a modern data experience that integrates workflows, trust, and collaboration across a decentralized ecosystem.

Stancil argues that the modern data stack is carved up by an implicit technical vs. non-technical divide, which is a false boundary imposed on a continuous spectrum of work. This gerrymandering distorts product design—especially in analytics consumption—by forcing tools into narrow buckets instead of enabling a unified experience across users and use cases.

Stancil argues that analytics is often seen as undervalued not just because of mislabeling or bad tooling, but because the work itself is genuinely hard and average results can be underwhelming. The field’s value depends on exceptional analysts, yet most teams can’t consistently produce that level of impact, so organizations may be reacting rationally. He suggests improving selection and cultivation—hiring for the right mix of skills and training the squishier parts of the job—to close the gap.

Stancil argues that analytics is being miscast as a primarily technical, engineering-adjacent discipline, which both misidentifies what makes great analysts and excludes strong candidates who lack conventional technical credentials. He says the real differentiator is curiosity, judgment, and communication, and that the emerging analytics engineer role could either enable this distinction or blur it further.

Stancil argues that “self-serve analytics” isn’t defined by features or who/what answers questions, but by how the experience feels to the people using and providing it. Because trust, comfort, and skill differ by user and context, the same tool can feel self-serve to some and not to others, so teams should design for the experience that makes their users feel at home rather than chase a canonical feature checklist.

Stancil analyzes 12 years of NBA games to answer how fans should feel in close games at halftime. The data shows that when the score is tied or within one point, teams that played poorly in the first half are actually more likely to win, indicating mean reversion beats momentum.

Stancil argues that analytics teams overinvest in dashboards and BI systems that produce fleeting value while neglecting the ad hoc analyses that drive the most important decisions. The real problem is that these analyses are ungoverned, hard to find later, and treated as disposable. He proposes reframing and organizing this work—starting by renaming it—to make it central, durable, and systematized.

Benn argues that analogies aren’t obfuscation but a clarifying tool because they force us to simplify a complex idea into its essential structure before comparing it to something else. That simplification exposes the logical core and prevents us from hiding behind jargon or unnecessary complexity. He links this directly to analytics work, where the hardest task is distilling signal from noise and telling a clear, memorable story.

Stancil argues that strict, uniform SQL formatting rules are less important than making a query’s logic legible to humans. Formatting should adapt to the structure and emphasis of the logic, even if that breaks conventional style guides or what a formatter can automate.

Stancil argues that despite increasing standardization in data stacks, reusable dashboard templates haven’t taken off because the most meaningful business data differences are intentional and central to how each company operates. Even when tooling and schemas align, companies define success differently, making one-size-fits-all reporting impractical; at best, templates might work in narrow, rigid domains if they ask for meaning-first inputs.

Stancil argues that “self‑serve analytics is a mirage” has become a fashionable shibboleth, but the real issue is that analysts project their own needs onto business users. When you define self‑serve narrowly—mostly as consistent metric extraction—it becomes solvable, while deeper analysis still requires hiring more analytical thinkers.

The post argues that closing airplane window shades is a small but corrosive act of selfishness that traps everyone in a sterile, joyless cabin. Stancil frames the window as a hard-won, almost sacred portal to wonder, and implores the window-seat passenger to share that view rather than retreat into screens.

Stancil argues that analytics is inherently creative and messy, not an objective, linear path to truth. Metrics are subjective constructs shaped by business context, so early analytical work should embrace exploration before converging on stable definitions. Attempts to prematurely impose structure or swap tools often mistake the natural mess of discovery for failure.

Stancil argues that proximity doesn’t just humanize people—it biases us in their favor, often excusing real faults. He uses Paul Graham’s defense of Antonio García Martínez to show how personal closeness clouds judgment and leads to asymmetric scrutiny. The piece concludes that public accountability matters because elites tend to protect their own.

Stancil argues that Airbnb’s Minerva addresses the core problem in analytics: inconsistent metric definitions across teams and tools. He praises Minerva’s metrics layer and its integration with the data catalog for shifting discovery toward metric-centric, consumer-friendly views. His main critique is the API-first access, which makes adoption and SQL-centric analyst workflows harder than a warehouse-native approach.

Stancil argues that if you want a Twitter profile picture that truly stands out, you should treat it as a color-contrast optimization problem rather than a flattering headshot. By sampling thousands of avatars and comparing dominant hues, he shows that extreme, edge-of-the-wheel colors maximize visual distinctiveness against both other profiles and Twitter’s UI.

Stancil argues that the modern data stack is missing a crucial component: a centralized metrics layer that defines and serves consistent metric logic across all tools. Without it, self-serve analytics devolves into fragmented, inconsistent calculations, and BI tools end up as isolated silos rather than shared infrastructure. A proper metrics layer would make metrics universally accessible via SQL, unify reporting with deeper analysis, and complete the stack’s foundation.

Benn argues that the data tooling industry is shifting from vertically integrated, all-in-one products to a horizontally integrated “stack” where each layer (ingestion, storage, transformation, consumption) is owned by specialized tools. This change is driven by the explosion of vendors and the need to blend data across functions, but it creates new challenges around integration and boundary discipline.

Benn Stancil argues that self-serve BI keeps failing because teams define it as a code-free version of analyst work, which is the wrong goal. Analysts create new measures to test hypotheses, while non-analysts mostly want to extract known metrics, so self-serve should be built around opinionated “metric extraction” rather than open-ended analysis.

Stancil recounts how, as a data analyst cofounding a startup with no product or data, he found himself without a clear role and started a blog to explore quirky data stories. Years later, he’s relaunching that impulse on Substack, committing to regular posts that mix data culture and tech commentary with his eclectic interests.

Stancil uses a simulation of Barry Bonds's 2004 pitches to show that Bonds could have nearly matched his historic on-base percentage without swinging, because pitchers avoided the strike zone. Extending the method league-wide suggests that, as zone rates have fallen, many hitters would actually improve their OBP by swinging less, implying modern pitching has made patience unusually valuable.

Stancil argues that the missing wave of public self-flagellation after the Atlanta murders isn’t a sign of progress but evidence of how racism adapts to preserve itself. He suggests that when anti-Asian racism isn’t widely recognized as structural, there’s no safe “systemic” shield for apologies, so blame shifts and the caste system stays intact. The core point is that racism in America is flexible and self-protecting, and its contradictions make it stronger, not weaker.

Stancil argues that Paul Graham’s widely cited math on a startup wealth tax is not wrong so much as misleading, because it relies on extreme, compounding assumptions about growth rates, time horizons, and equity outcomes. Small tweaks to those assumptions swing the results wildly, revealing that spreadsheet models in this context mostly reflect prior policy beliefs rather than hard truths.

Stancil argues that the Clubhouse “slur” controversy shouldn’t be dismissed as mere performative tech-media drama, even if it looks like it. By comparing it to past journalism blowups, he suggests that Lorenz’s mistake can be read as a desperate outburst rooted in real, long-ignored harassment rather than a cynical hit job. The piece presses readers to examine the system that creates this rage instead of only condemning the person who erupts.

Stancil argues that the January 6th attack and the GameStop saga reveal the same structural danger: social media enables fringe groups to reach critical mass quickly, turning online narratives into real-world destabilization. Because scale and speed are the product, even well-intentioned movements can spiral, leaving society on a runaway train with only two options—submit to the ride or dismantle the track.