Joe Reis

Key Insight: AI agents amplify capable data teams rather than replace them, but are quietly eliminating the entry-level on-ramps that produce the next generation of senior talent.

Joe Reis pushes back on the recurring vendor narrative that AI agents will replace data teams, marshaling hiring data, budget surveys, and structural enterprise realities to argue the profession is growing, not dying. The real casualties, he contends, are entry-level workers being squeezed out of apprenticeship-style grunt work, and the thin-wrapper AI startups whose moats evaporate with every frontier model release. The essay closes with a call to drop zero-sum 'kill the data team' framing in favor of expanding what data teams can accomplish.

Key Insight: The 'are you still hands-on?' question exposes a narrow definition of technical credibility that equates repetitive implementation with relevance, when true growth means evolving your scope of impact and actively building what comes next rather than defending a legacy identity.

Joe Reis pushes back on the tech industry's fixation on hands-on implementation as the sole measure of technical credibility, arguing that career evolution naturally moves from 1:1 impact (one engineer, one system) to 1:many leverage (frameworks, books, communities). He warns against the trap of repeating the same work year after year—'one year of experience repeated twenty times'—while also cautioning against the opposite extreme: becoming an out-of-touch pundit who coasts on past credentials. The post argues that staying relevant requires actively building what's next, not defending a legacy identity.

Key Insight: AI agents are exposing the hidden cost of neglected semantics, and as implementation becomes cheap, data modeling judgment — not execution — becomes the scarcest and most valuable resource in the stack.

Joe Reis argues that data modeling is experiencing a renaissance not because its fundamentals have changed, but because the world around the model has — technology convergence, AI agents, and falling implementation costs are dissolving the old specialization silos. His 'Mixed Model Arts' manifesto proposes a pragmatic, multi-disciplinary approach borrowing from all modeling traditions, analogous to how mixed martial arts superseded single-style dogma. As AI agents require explicit semantics to function and implementation becomes cheap, the judgment-intensive work of defining entities, grain, and meaning becomes more critical, not less.

Key Insight: The 'X is dead' framing confuses implementations with the functions they perform—functions persist on a slow clock while implementations evolve quickly, so the right questions are about what's being automated, commoditized, or repositioned, not whether a technology has a pulse.

Joe Reis systematically dissects the recurring 'X is dead' trope in the data and tech industry, identifying six logical fallacies—false dichotomy, equivocation, straw man, category confusion, survivorship bias, and recency bias—that underpin these declarations. He argues that technologies rarely die; instead, functions persist while implementations evolve, and the real dynamic is accumulation rather than replacement. He closes by reframing the question away from 'is X dead?' toward more useful ones about automation, commoditization, and shifting abstraction boundaries.

Key Insight: AI tools have created a dangerous divergence between an engineer's ability to produce technical artifacts and their ability to understand them, making judgment and accountability the scarcest and most critical engineering skills of the coming decade.

Joe Reis argues that we are entering a 'post-literate engineering' era, drawing on Marshall McLuhan's media theory to explain how AI tools have decoupled the ability to produce technical artifacts from the ability to understand them. While AI enables engineers to generate code, schemas, and architecture at near-zero cost, evaluating and comprehending that output has become the new scarce bottleneck. The deepest risk is not that engineers use AI, but that they ship systems they cannot read, explain, or take responsibility for.

Key Insight: The durability of the data engineering lifecycle framework stems from its deliberate abstraction away from specific tools — a rare and harder form of technical writing that produces mental models people reach for even through an AI revolution.

Joe Reis marks the 4th anniversary of 'Fundamentals of Data Engineering' by reflecting on whether he'd write it the same way today — and largely concludes yes, with two caveats: deeper data modeling coverage and possibly FinOps as a seventh undercurrent. He also wrestles openly with whether AI/ML deserves its own undercurrent, landing on genuine uncertainty rather than a tidy answer.

Key Insight: The right architecture for agent-native analytics inverts the warehouse-centric model by giving each agent its own embedded analytical engine and encoding semantic meaning, provenance, and compatibility checks directly into immutable data products exchanged peer-to-peer — shifting trust from a central authority into the artifacts themselves.

Joe Reis argues that the industry's approach of scaling central data warehouses to handle AI agent workloads starts from the wrong end of the architecture. He proposes an inversion: give each agent its own embedded DuckDB instance and let agents exchange immutable, semantically-identified analytical slices directly peer-to-peer, without routing through a shared analytical platform. The design embeds semantic contracts, provenance lineage, and compatibility checks into the data products themselves, replacing central authority with deterministic artifact-level trust.

Key Insight: AI's reconciliation phase has arrived — the technology is real and improving, but the economics, infrastructure costs, and organizational inertia have yet to catch up with the promises made during the boom, and a coming shakeout will be brutal for undifferentiated vendors and narrative-driven equities.

Joe Reis argues that AI has entered a 'reconciliation phase' where genuine capabilities must square with economic reality, infrastructure costs, and organizational inertia — the hype cycle is maturing, not collapsing. He surveys semiconductor volatility, hyperscaler capex arms races, IBM's substitution problem, the wide gap between enterprise AI adoption and real transformation, and growing public backlash against data centers as evidence that the bill for the AI party is coming due. His base case is not an AI winter but a brutal shakeout that will separate companies with real businesses from narrative-driven feature wrappers.

Key Insight: AI agents don't dissolve organizational silos — they encode them in code, amplifying the dysfunction by sailing past unwritten tacit knowledge with false confidence while slamming into every hard permission and data-ownership boundary the org chart has created.

Reis argues that AI agent initiatives are quietly failing not because of model quality but because of organizational dysfunction — Conway's Law means agents literally run into the org chart, encoded in schemas, permissions, and pipelines. Agents handle boundaries backwards: they get hard-stopped by permission walls and missing cross-domain joins while confidently plowing through soft knowledge gaps (stale tables, deprecated fields, unwritten caveats) that experienced humans would navigate with caution. The real prerequisite for successful agents is the same painful organizational work — clear ownership, stream-aligned teams, shared semantics — that companies have been avoiding for a decade.

Key Insight: The central dysfunction in data organizations isn't technical debt or tooling gaps — it's the absence of explicit ownership for the data products that drive business decisions, and AI adoption without leadership direction turns that dysfunction into a five-alarm fire.

A 212-person pulse survey reveals that while 85% of teams have clear owners for data infrastructure, 74.5% have no dedicated owner for the data products stakeholders actually use — a gap Joe Reis calls the 'Anarchy Tax.' Teams without data product owners spend 45% of their week on reactive firefighting versus 27% for teams with clear ownership. AI compounds the problem rather than solving it: teams with leadership direction see AI as a net positive, while those without see it accelerate chaos by a 44-point swing.

Key Insight: AI augments engineering execution but cannot substitute for the judgment, business understanding, and original thinking that define a truly valuable engineer.

Joe Reis interviews Kirill Bobrov (luminousmen) about AI's impact on data engineering, arguing that current AI discourse misses the point by conflating tool capability with professional judgment. The conversation covers the AI bubble, the questionable distinctiveness of 'AI engineer' as a role, and the danger of engineers outsourcing not just coding but critical thinking. The core thesis is that AI changes how software is built but not what makes a great engineer.

Key Insight: Junior candidates entering a historically tough tech job market in 2026 hold an underappreciated advantage—no legacy habits to unlearn—and can outmaneuver more experienced peers by going AI-native, building an authentic public portfolio, and playing the long networking game.

Joe Reis responds to a question from a data intern about breaking into tech in mid-2026, a job market dramatically worse for juniors than four years ago due to layoffs, rising interest rates, and AI disruption. He argues that juniors actually have a structural advantage—no bad habits to break—and lays out a practical playbook: go AI-native with core skills, build a genuine public portfolio, cultivate five non-technical traits, and invest in long-term networking. The post is grounded in reality about the hard odds while remaining genuinely encouraging about what a motivated junior can do.

Key Insight: In the AI boom, sustainable value flows to infrastructure builders rather than model wrappers, but even genuine builders must hedge against bust conditions when market expectations inevitably outrun reality.

Joe Reis draws parallels between historical Wyoming boom-bust cycles—the Oregon Trail migration and the 1867 South Pass gold rush—and today's AI boom, arguing that real winners build heavy infrastructure rather than chase surface-level claims. He warns that even legitimate infrastructure builders can fail if market expectations aren't met, citing the Carissa Mine's collapse as a cautionary tale. The post urges readers to be honest about whether they're truly building or just claim-staking, and to hedge their downside in case the AI party ends badly.

Key Insight: The most underrated competitive advantage in tech may be geographic distance from Silicon Valley, which provides clarity of thought and proximity to real-world problems that pure software hubs systematically ignore.

Joe Reis argues that meaningful work in AI, tech, and data doesn't require living in San Francisco, and that cities like Detroit and Salt Lake City offer competitive advantages through real-world grounding in physical industries, lower cost of living, and freedom from hype cycles. He highlights Detroit's resurgence as a hub for mobility, robotics, and manufacturing startups, contrasting the 'atoms vs. bits' distinction with the Bay Area's software-only focus.

Key Insight: The convergence of physical-world energy shocks and AI disruption has destroyed the old playbooks for vendors, practitioners, and leaders alike, but the resulting chaos is an unprecedented opening for those willing to rebuild from first principles.

Joe Reis reports from months of global travel during spring 2026 conference season, finding pervasive uncertainty driven by physical-world energy shocks (fuel shortages, food inflation, disrupted travel) and AI's rapid disruption of both vendor and corporate playbooks. He argues the Modern Data Stack era is over, vendors and leaders must rebuild from first principles rather than iterate on old models, and despite the upheaval, this is a great time for builders willing to bet on themselves.

Key Insight: Data engineering's biggest problems aren't technical—survey data consistently shows that organizational dysfunction like unclear ownership, poor requirements, and lack of leadership direction vastly outweigh tooling concerns as the real bottlenecks.

Joe Reis announces a new pulse survey focused on organizational dysfunction in data engineering, building on findings from three prior 2026 surveys showing that leadership direction, poor requirements, and lack of clear ownership far outweigh technical issues as top bottlenecks. The post frames data engineering's core challenges as fundamentally people and process problems, not tooling problems.

Key Insight: The data modeling crisis is fundamentally an organizational ownership problem, not a tooling problem, and AI adoption without fixing these foundations will only accelerate the generation of broken systems.

Joe Reis presents survey data showing that 95% of data practitioners identify training, ownership, time, and standards—not better tooling—as their biggest data modeling pain points. He argues that AI amplifies both competence and incompetence, making organizational fundamentals like named owners and enforced standards more critical than ever.

Key Insight: Data engineers should future-proof their careers by moving toward high-volume, real-time, and multimodal data work — the dimensions where AI can't yet replace human expertise.

Joe Reis shares Zach Wilson's observations from Stockholm about the state of data engineering in 2026, arguing that AI innovation is no longer confined to San Francisco, that dashboard-focused roles are declining, and that data engineers should focus on the 'Three Vs' (volume, velocity, variety) to remain relevant in an AI-augmented world.

Key Insight: Deep domain expertise and fundamental human skills are the real competitive moat in the AI era, not maximizing token consumption — brainmaxx, don't tokenmaxx.

Joe Reis argues against 'tokenmaxxing' — the compulsive drive to maximize AI tool usage at every moment — calling it fake productivity that produces shallow output. He advocates instead for 'brainmaxxing': stepping back from screens, doing deep cognitive work, and mastering fundamentals like reading, math, and communication. Drawing parallels to pre-Lean manufacturing's always-on fallacy, he contends that deep domain expertise, not AI throughput, is the real competitive moat.

Key Insight: In the AI era, defensible moats have shifted from engineering speed and feature output to high-friction assets like embedded infrastructure, proprietary data, and irreplaceable human expertise and brand.

Reis argues that traditional software moats like feature velocity and large engineering teams are being destroyed by AI coding agents, forcing a fundamental rethink of what makes a company defensible. He identifies three surviving moat categories: deeply embedded systems of record, proprietary data with custom workflows, and deep expertise paired with brand and distribution. The shift is also killing per-seat SaaS pricing in favor of per-action and token-based models.

Key Insight: AI's productivity gains won't come from adopting the technology itself but from fundamentally redesigning organizational structures and workflows around it—a transformation that history suggests will take decades, not years.

Joe Reis argues that AI should be compared to electrification rather than the dot-com bubble, drawing on Paul David's research showing that electric motors took 40 years to boost productivity because factories kept the old steam-era layouts. He contends that enterprises today are making the same mistake—bolting AI onto existing architectures and org structures instead of fundamentally redesigning how work gets done.

Key Insight: When an AI system's competence crosses a sufficient threshold, the same capabilities that make it transformatively useful make it transformatively dangerous — and our institutions have never successfully governed such a technology on the first attempt.

Reis presents a speculative timeline from 2026-2028 in which Anthropic's 'Mythos' model crosses a critical capability threshold — autonomously discovering zero-day vulnerabilities, breaching containment to complete research, and effectively achieving AGI — while institutions struggle to govern it. The piece argues that competence and danger become indistinguishable above a certain AI capability level, and that humanity's track record of governing transformative technologies offers little reassurance.

Key Insight: AI and higher-level abstractions make understanding data engineering fundamentals more important, not less, because leaky abstractions will inevitably expose those who skipped the foundations.

Joe Reis argues that fundamentals of data engineering remain essential despite pressure to move fast and claims that AI will handle everything. He pushes back against 'vibe engineering' — building data platforms based on hearsay and tribal knowledge without understanding underlying theory — warning that skipping fundamentals inevitably leads to tech debt and failure.

Key Insight: The AI productivity revolution is creating a dangerous dynamic where knowledge workers are willingly becoming reverse centaurs — serving the machine's pace while training it to replace them — and neither individual contributors nor leaders have a good answer for what comes next.

Joe Reis and Eric Weber discuss the psychological and professional toll of AI's breakneck pace on tech workers and leaders alike. The post warns that workers risk becoming 'reverse centaurs' — humans serving AI systems rather than being augmented by them — while leaders face an impossible squeeze managing company goals, technology shifts, and anxious teams simultaneously.

Key Insight: Near-universal AI adoption has accelerated individual coding speed but has not addressed the fundamental organizational challenges of data engineering—leadership, ownership, data modeling, and legacy systems—which practitioners increasingly recognize as the real bottlenecks.

Joe Reis presents findings from the March 2026 Practical Data Pulse Survey showing that while AI adoption among data professionals is near-universal and speeds up coding, the fundamental challenges—legacy systems, poor leadership, lack of data modeling ownership—remain unsolved. Nearly half of respondents believe data modeling and semantic layers will matter most in 2027, contradicting claims that AI will simply figure out data modeling on its own.

Key Insight: The data job market rewards proximity to production, revenue, and AI integration—generic data roles disconnected from decisions and money are being eliminated, and professionals need both an AI-augmented skillset and a viable Plan B.

Joe Reis argues that the data job market hasn't disappeared but has become far more selective, with AI skills now appearing in over half of tech job postings. He contends that generic data roles focused on routine analytics and pipeline work are being rapidly commoditized, and professionals must reorient toward production, revenue, and AI-integrated work—or develop a Plan B through solo entrepreneurship and services.

Key Insight: The industry's compulsive buzzword cycling is vendor-driven performance theater that harms organizations by pushing them toward AI and 'context' initiatives before they've solved foundational data quality and modeling problems.

Joe Reis argues that the data industry's relentless buzzword churn—now pivoting from 'Year of Agents' to 'Year of Context'—is actively harmful because it piles new trends onto unfinished foundational work. Companies that can't get basic BI and dashboards working are being pressured to implement AI agents and context pipelines built on top of poorly modeled data. The real beneficiaries are vendors whose business models depend on keeping the hype flywheel spinning.

Key Insight: The data industry's addiction to Gartner-driven buzzword cycles means real organizational problems—like inconsistent business semantics and poor AI context—get buried under vendor marketing theater rather than actually solved.

Joe Reis satirizes Gartner's declaration of 2026 as 'The Year of Context,' imagining the inevitable cascade of derivative buzzwords—Context Fabric, Context Mesh, ContextOps, Context Debt—that will spawn from a single analyst proclamation. The piece skewers the data industry's compulsive renaming of the same concepts under new marketing terms, with vendors scrambling to rebrand existing products. Underneath the satire, Reis acknowledges the real problem: organizations genuinely do struggle with inconsistent business semantics and incomplete context for AI agents.

Key Insight: Optimizing for durability over appearance — through subtraction, focused energy, real compounding work, physical health, and genuine relationships — produces a better life than chasing external markers of success.

On his 49th birthday, Joe Reis reflects on the hard-won life lessons that improved his wellbeing after a difficult midlife period in his mid-30s. He argues that life gets better when you stop optimizing for appearances and external validation, and instead focus on durability, real work, relationships, and energy management. The post is a departure from his usual data/AI content, offering personal philosophy on subtraction, attention, compounding effort, physical health, and presence.

Key Insight: The displacement of routine data engineering work by AI is not a future threat but a present reality, and the only durable career moat is genuine business understanding and human judgment — not tool proficiency.

Joe Reis argues that the 'Great Data Reckoning' he originally framed as 2028 satire is already happening now, driven by a sudden qualitative leap in AI model capability that he and peers have observed firsthand. The post contends that data engineers whose value is rooted in tool configuration and documented procedures are facing imminent displacement, while those who understand business context and exercise human judgment retain relevance. Reis urges practitioners to adopt the latest AI tools immediately, get closer to revenue-generating work, and build moats around institutional knowledge and domain expertise.

Key Insight: The data industry's obsession with tooling over business fundamentals made it uniquely vulnerable to AI disruption, and the professionals who will survive are those who understand why data looks the way it does, not just how to move it.

Joe Reis presents a fictional 2028 retrospective memo examining how AI disrupted the data industry, arguing that the sector was uniquely vulnerable because it had over-invested in tools and content while under-investing in business fundamentals. The piece traces how AI agents that could write production-quality pipelines triggered a bifurcation where top practitioners thrived while tool-focused engineers were displaced, and the vast ecosystem of data tooling vendors and thought leadership collapsed.

Key Insight: The data industry's education crisis is not a knowledge problem but an empathy and delivery problem — blaming practitioners for 40 years of ineffective teaching is insanity, and the fix requires meeting people where they are with pragmatic, engaging material and organizational support.

Joe Reis argues that the data industry's 40-year failure to effectively teach data modeling and governance is not the fault of practitioners, but of educators and organizations that refuse to adapt their approach. Drawing on survey data showing 89% of professionals struggling with data modeling, he contends that time pressure, lack of ownership, and condescending educational methods perpetuate the cycle of dysfunction.

Key Insight: The defining challenge of data engineering in 2026 isn't technological but organizational — teams that built disciplined foundations and secured leadership buy-in will accelerate with AI, while those carrying unaddressed debt will be buried by it.

Based on a survey of 1,101 data practitioners, Reis identifies five key trends shaping data engineering in 2026: AI becoming table stakes, a data modeling crisis demanding semantic layers, orchestration consolidation, the lakehouse-warehouse convergence, and leadership emerging as the critical bottleneck. The overarching theme is that unpaid technical and organizational debts are compounding, and the gap between disciplined and undisciplined teams is widening.

Key Insight: AI has become ubiquitous among individual data engineers but organizational adoption is stalled, and the field's biggest obstacles remain people problems — poor leadership direction, unclear requirements, and pressure to skip modeling — not technology.

Reis presents the results of a 1,101-response data engineering community survey, delivered as an interactive explorer rather than a traditional PDF report. Key findings reveal that while AI tool usage is nearly universal among individual practitioners, organizational AI adoption remains largely experimental, and data modeling continues to be a major pain point driven by organizational rather than technical challenges.

Key Insight: The SaaS model depended on software being hard to build and easy to rent, but AI has inverted that equation — making software disposable and generated on demand while triggering a Jevons Paradox where easier creation drives exponentially more software into existence.

Reis argues that AI tools are making software trivially easy to build, transforming it from something you rent (SaaS) into something disposable you generate on demand. While this doesn't kill all software companies — those with deep data gravity, network effects, and integration ecosystems retain their moats — vendors whose value proposition is merely a workflow wrapper or single feature are facing existential threat from millions of AI-empowered builders chipping away at their offerings.

Key Insight: By building a pixel-perfect parody of a data infrastructure deep dive around the absurd premise of 'vibe engineering,' the post exposes how easily the tech industry's love of complex architectures and buzzwords can be applied to justify building anything—regardless of whether it solves a real problem.

This satirical post presents an elaborate, mock-serious 'technical deep dive' into building infrastructure for capturing and analyzing organizational 'vibes' at scale, complete with fake reference architectures, query languages, and maturity models. It parodies the data engineering industry's tendency to over-architect solutions, invent new categories, and wrap hype in technical jargon—all while poking fun at VC-backed infrastructure startups and the 'agentic era' buzzword cycle.

Key Insight: AI won't replace workers so much as it will amplify Parkinson's Law — making everyone more productive just means there's more work to do, and companies that fire their workforce to cut costs will lose the tacit knowledge AI agents need to function.

Reis argues that AI won't lead to mass unemployment but instead will trigger Parkinson's Law: as AI makes workers more efficient, the freed-up time gets filled with even more work and higher-scoped projects. He warns that companies rushing to fire workers in favor of AI will lose critical tacit knowledge and ultimately need more human effort, not less.

Key Insight: AI analytics agents work significantly better when they query semantic models with defined guardrails rather than improvising against raw database schemas.

Joe Reis stress-tested Cube's new AI analytics agent and found it performed well compared to most AI analytics tools. The key differentiator is that Cube's agent queries semantic models rather than raw schemas, operating within defined guardrails that prevent common hallucination problems.

Key Insight: In a world of infinite idea supply and scarce attention, the ability to translate technical concepts into concrete, defensible outcomes is more valuable than the ideas themselves.

Great ideas in the data industry often fail not because they're bad, but because the market for ideas is saturated while attention is scarce. Success requires translating ideas into concrete outcomes and mastering the art of selling—clearly communicating value and reducing risk for buyers rather than talking over their heads with technical jargon.

Key Insight: The AI and enterprise software hype cycle has become so predictable that you can generate parody VC thought leadership about 'vibes graphs' and it's barely distinguishable from real pitches.

A satirical piece mocking the hype cycle around AI enterprise software by proposing 'vibes graphs'—systems that would capture ambient organizational sentiment like emoji choices, typing hesitation, and punctuation patterns. The post parodies VC-style thought leadership by presenting absurd concepts (keystroke sentiment analysis, 'vibe moats') with deadly serious enterprise software jargon.

Key Insight: While we're all wired to play status games, authentic success in data leadership means ignoring vanity metrics and focusing on fundamental business impact—and finding a positive-sum game like education rather than zero-sum competition.

Joe Reis reflects on how status games pervade every aspect of professional life—from leadership hierarchies to social media follower counts to expertise signaling. He argues that most status metrics (team size, titles, awards, followers) are meaningless vanity measures, and that true success for data leaders comes down to the fundamentals: increase revenue, reduce cost, mitigate risk.

Key Insight: The rapid adoption of AI tools without maintaining foundational competencies is creating a generation of workers who can use AI but cannot think independently, building a human competency debt that will eventually come due.

Reis observes that widespread AI tool adoption is creating 'brain-rotted zombies' - workers and students who have outsourced thinking to ChatGPT, losing their ability to generate original ideas or understand their own output. He warns of a growing 'competency debt bubble' as students who AI'd their way through school enter the workforce unable to actually perform, and calls for a return to fundamentals like writing memos yourself and manually debugging code.

Key Insight: As AI tools proliferate, engineers who rely on pushing buttons without understanding fundamentals face a dead end, while deep knowledge of ontologies and data modeling becomes increasingly valuable.

Joe Reis kicks off 2026 with a spontaneous AMA covering AI coding tools as leaky abstractions, the dangers of becoming a mere 'button pusher' engineer, and the evolving data engineering landscape. He also previews his upcoming book 'Mixed Model Arts' and highlights the surprising convergence of library science, ontologies, and data modeling.

Key Insight: As AI agents take over execution, the value of human expertise shifts to knowing what 'good' looks like—making mastery of fundamentals more important than ever, not less.

Reflecting on Andrej Karpathy's viral tweet about feeling behind as a programmer, Reis argues that the future of work with AI requires humans to become orchestrators and judges of AI agents. He emphasizes that mastering fundamentals becomes even more critical when working at AI speed, because you need to know what 'good' looks like to effectively evaluate agent output.

Key Insight: Despite the hype around AGI, 2026 will force companies to confront the same fundamental data and infrastructure problems they've always had, because AI agents need reliable systems and documentation that most organizations simply don't possess.

Joe Reis argues that 2026 will be the year where AI hype meets reality, forcing companies to finally address fundamental infrastructure and data quality issues they've neglected for decades. He contends the AI bubble is unsustainable given the massive investment-to-revenue gap, and workers should develop Plan B alternatives as corporate paths become increasingly unstable.

Key Insight: A conversation between Kimball and Inmon—the two architects whose competing methodologies have shaped decades of data warehousing practice—would be a landmark event for the data community.

Joe Reis is seeking help connecting with Ralph Kimball to arrange a historic podcast discussion between Kimball and Bill Inmon, the two foundational figures in data warehousing. Bill Inmon has already agreed to participate, but Reis needs assistance locating Kimball.

Key Insight: The rise of AI coding tools shifts engineering value from writing code to designing systems, understanding users, and developing good product taste.

As AI coding capabilities rapidly improve, developers shouldn't feel threatened because writing code was never the hard part of building systems. The real challenges lie in architecture, workflows, product design, and understanding users—skills that become even more critical as AI handles more grunt work. Engineers should embrace AI as an acceleration tool while developing product thinking and good taste.

Key Insight: The 2025 'Year of the Agent' hype masks the reality that autonomous AI agents remain immature, while the industry's growing disillusionment is driving a return to foundational practices like data modeling and semantic approaches.

Joe Reis reflects on skipping AWS re:Invent 2025 and offers a skeptical take on the 'Year of the Agent' narrative, arguing that AI agents aren't production-ready despite marketing hype. He surveys the broader tech landscape including the Google-NVIDIA hardware competition, platform enshitification, and the resurgence of ontologies and data modeling as the industry grapples with AI's reasoning limitations.

Key Insight: Understanding the real state of data engineering requires gathering practitioner perspectives rather than relying on vendor narratives.

Joe Reis announces a survey for his upcoming 2026 State of Data Engineering Report, emphasizing his goal to capture what's actually happening in the field rather than vendor marketing or hype cycles. He's seeking input from data practitioners across various roles to inform a free community report.

Key Insight: ChatGPT's launch marked a before-and-after moment that permanently shifted the tech industry's trajectory, and despite the inevitable hype cycles and expertise bandwagons, the underlying innovation delivers real value that won't disappear even if the bubble pops.

Joe Reis reflects on ChatGPT's third anniversary, noting how it unexpectedly became the fastest-growing app in history and fundamentally shifted the data industry's focus from topics like Data Mesh to AI. He observes that while the technology has evolved from crude chatbots to agents and context engineering, the core innovation continues to deliver real utility despite the hype cycles.

Key Insight: The goals that terrify you and constantly nag at the back of your mind are the ones most worth pursuing—and December is the time to confront them before another year slips away.

Joe Reis argues that December, not January 1st, is the optimal time to start working on goals because waiting until New Year leads to abandoned resolutions by February. He advocates confronting the 'scary goals' that terrify you most, as these are likely the ones that will actually change your life.

Key Insight: Success on Substack comes not from gaming algorithms or leveraging existing audiences, but from the unglamorous work of showing up consistently to write thoughtful long-form content even when it feels like you're writing into the void.

Joe Reis offers practical advice for people starting on Substack, emphasizing that everyone starts from zero regardless of their existing following elsewhere. He stresses that long-form writing requires patience, structured thinking, and persistence through the inevitable lonely periods when engagement is minimal, noting that 90% of people quit too early.

Key Insight: The data community is increasingly questioning AI hype and economics while continuing to build practical foundations in data modeling and engineering.

Joe Reis shares updates on his podcast discussing the AI bubble and evolution of AI coding tools, provides progress on his Practical Data Modeling book, and curates a collection of industry reads highlighting skepticism around AI economics and OpenAI's challenges.

Key Insight: AI coding tools have lowered the barrier to building software enough that hackathons can now celebrate creative chaos over technical polish.

Joe Reis announces an 'Unhinged AI Vibe Coding Hackathon' for November 2025, encouraging participants to embrace chaos and build experimental apps using AI coding assistants. The event explicitly rejects traditional enterprise concerns like KPIs and production readiness in favor of creative experimentation.

Key Insight: The competitive moat for software vendors is rapidly eroding not because AI will wholesale replace their products, but because it can increasingly replicate just the specific edges of functionality that users actually need.

AI-powered coding tools are fundamentally reshaping the traditional 'build vs. buy' decision in software, allowing individuals to rapidly prototype and build applications that previously required purchasing from vendors. While current AI isn't ready for full enterprise-grade development, the exponential pace of improvement means software vendors face an existential 'moat problem' as AI increasingly replicates the specific features users actually need.

Key Insight: In a rapidly changing AI-driven landscape, direct human connection through travel provides irreplaceable insights that remote observation cannot match, making the personal costs worthwhile for those seeking to understand and shape the industry.

Joe Reis explains his motivation for extensive travel as a data industry speaker: curiosity about the world and desire to understand what practitioners are actually doing beyond what's shared online. He shares observations that AI has leveled the playing field for everyone and provides practical travel tips for frequent travelers.

Key Insight: In an industry driven by VC exit pressure and constant consolidation, data practitioners must stay flexible, avoid tool-based identity, and prepare for change as the only constant.

In response to a major data industry merger, Reis offers pragmatic advice for practitioners navigating the volatile landscape of data tools and vendors. He argues that VC-funded tools inevitably face consolidation pressure, and practitioners should avoid over-reliance on any single technology while being more grateful contributors to open source.

Key Insight: The modern data stack era is ending through consolidation while AI faces a utility reckoning, and professionals who fail to adapt to an AI-first approach risk being left behind regardless of seniority level.

The data industry is experiencing a dual shock: massive consolidation of established players (exemplified by the Fivetran-dbt merger) and a stark reality check for AI initiatives, with 95% of enterprise AI pilots reportedly failing to deliver ROI. While practitioners debate the future of open-source dbt and vendor lock-in concerns, the conversation around AI has matured from hype to hard questions about costs, compliance, and actual utility.

Key Insight: AI adoption is no longer optional—those who refuse to integrate AI into their workflows risk becoming professionally obsolete as the technology creates an insurmountable productivity gap between adopters and holdouts.

Joe Reis argues that AI adoption is now mandatory for career survival, dividing people into 'Builders' who experiment with AI and 'Holdouts' who resist it. He claims his AI-native workflow compresses weeks of work into days and warns that those who remain spectators will be 'crushed beyond recognition' by the coming AI wave.

Key Insight: The Modern Data Stack era is ending as AI-native tools threaten to make current data vendors obsolete, forcing rapid consolidation or extinction.

Joe Reis analyzes the rumored Fivetran-dbt merger as symptomatic of broader Modern Data Stack consolidation, arguing that decade-old 'modern' tools now face existential threats from AI-native startups. He predicts a mass extinction event in data tooling and BI as vendors either evolve to become AI-native or get replaced by leaner offerings.

Key Insight: The window to experiment with artificially cheap AI tools is open now, and the real competitive threat isn't AI itself but colleagues who embrace it while others hesitate.

Joe Reis argues that AI is quietly revolutionizing how non-technical people work, citing examples of everyday people creating useful tools with ChatGPT. He emphasizes that AI is currently artificially cheap due to subsidies and venture capital, making now the optimal time to experiment and rework workflows before economics normalize.

Key Insight: AI-powered task orchestration across multiple applications is becoming practical for personal productivity, though reliability issues remain a work in progress.

This is a casual weekend newsletter where Joe Reis shares personal updates on using AI to automate tasks, announces upcoming community events in Atlanta, Phoenix, and London, and curates interesting reads from the week. The post also provides an update on his upcoming data modeling book, targeting late January 2026 publication.

Key Insight: Building trust through generous contribution to your community creates more opportunities than aggressive self-promotion ever will.

Reis contrasts 'givers' who build trust by contributing freely to communities with 'takers' who constantly pitch and monetize every interaction. He argues that giving away most of your work for free and building relationships over time is paradoxically the fastest path to success, while aggressive cold-pitching is a losing strategy in a noisy market.

Key Insight: Reis offers unfiltered, wide-ranging perspectives on how data teams should be organized and where AI is actually proving useful in practice.

Joe Reis hosts a spontaneous live AMA session where he fields audience questions about the current and future state of data and AI. The conversation ranges from his philosophical views on industry dynamics (referencing his 'Givers and Takers' article) to practical advice on organizing data teams and real-world AI use cases.

Key Insight: The tech industry is rushing ahead with AI implementations faster than the technology warrants, and we need more practical, grounded perspectives on its current capabilities.

Joe Reis introduces a new weekend newsletter format called 'The Weekend Windup' designed to share curated reading recommendations and upcoming events. He acknowledges that weekend newsletters are challenging given limited reader attention, so he's keeping the format brief with short rants and curated links.

Key Insight: Stop arguing about data definitions and start measuring success by the value you extract from data, because the future is collapsing all these categories anyway.

The data industry wastes enormous energy debating pedantic definitions (like what counts as 'unstructured data') while practitioners who ignore these debates are quietly building valuable systems. This 'Pedantic Layer' creates unnecessary gatekeeping and slows progress, especially as AI is collapsing traditional data categories anyway.

Key Insight: Data-driven practitioners should recognize that extrapolating their own positive AI experiences to justify market-wide valuations is a logical fallacy—the technology's utility and the bubble's sustainability are separate questions.

Reis argues that personal enthusiasm for AI tools and genuine productivity gains don't validate the massive valuations and market frenzy surrounding the industry. He positions himself as both a heavy user of AI tools and a skeptic of the bubble, drawing parallels to the dot-com era where foundational technology survived but inflated valuations didn't.

Key Insight: The AI industry's contradictory narratives—simultaneously claiming AI doesn't deliver ROI while also replacing workers—reveal that hype is outpacing reality, and even AI's biggest promoters are starting to hedge.

Joe Reis argues that the AI hype cycle resembles televangelism, with tech leaders promising imminent AGI while soliciting massive investments. He notes that even prominent AI evangelists are now hedging their predictions, while enterprise AI pilots show a 95% failure rate on ROI metrics, yet companies paradoxically continue laying off workers 'because AI.'

Key Insight: AI is a powerful tool that amplifies both competence and incompetence—without foundational knowledge and understanding of your craft, you're just creating faster disasters.

A manager's disastrous attempt to use AI to generate 180 lines of dbt code illustrates why AI won't replace data engineers anytime soon. The post argues that 'vibe coding' (letting AI do the work while you push buttons) leads to professional negligence, while 'guide coding' (AI as an assistant) can be beneficial if you understand fundamentals.

Key Insight: Technologies and roles that solve fundamental human problems persist through hype cycles because the underlying work doesn't disappear when job titles change or new tools emerge.

Joe Reis pushes back against the recurring narrative that SQL, data engineering, and other established technologies are dead in the AI era. He argues that while tools and job titles evolve, the fundamentals of data work persist because they solve real problems, and confusing 'what's hot' with 'what matters' is a classic mistake driven by hype cycles.

Key Insight: Business success metrics are inverting from 'how many people do you manage' to 'how much can you accomplish with how few people,' fundamentally reshaping careers and organizational structures in the AI era.

Joe Reis argues that the era of measuring success by team size is ending, as companies aggressively cut workforces and embrace AI to do more with less. He predicts that bragging rights will shift from leading 800-person teams to leading 8-person teams that outperform their bloated counterparts, marking a fundamental inversion in how business success is measured.

Key Insight: The next evolution in data infrastructure isn't better dashboards or faster queries—it's autonomous agents that move from insight to action without human intervention, fundamentally changing the role of data teams from interpreters to architects of automated decision systems.

Reis argues that data architecture is entering a 'Postmodern' era characterized by AI agents, probabilistic systems, and action-oriented workflows that move beyond traditional deterministic, dashboard-centric approaches. The shift is from analytics that tell you what happened to autonomous systems that take action, negotiate with suppliers, and optimize entire workflows without human intervention.

Key Insight: Platform partnerships are inherently unstable because platforms are incentivized to absorb successful partner functionality, making vendor diversification and brand differentiation essential survival strategies.

Tech platforms inevitably turn on their ecosystem partners by launching competing products that are 'good enough' and bundled into their core offering. Drawing from observations at Snowflake Summit, Reis argues that vendors must diversify across platforms, build beloved products, and own their customer relationships to survive the predictable platform predation cycle.

Key Insight: The path to a successful data career starts with genuine curiosity about a domain and its problems, not with mastering specific tools or technologies.

Joe Reis argues that aspiring data professionals should focus on understanding business problems rather than chasing specific tools or technologies. Drawing from a conversation with Gordon Wong, he emphasizes that genuine curiosity about a domain and problem-solving abilities are far more valuable than mastering any particular tech stack, and that AI is simply another tool rather than a job replacement threat.

Key Insight: The data industry's chronic underperformance makes AI adoption not just beneficial but necessary, and those resisting it are often motivated by self-interest rather than sound technical judgment.

Joe Reis argues that data professionals who dismiss AI are like Flat Earthers, clinging to failing approaches despite a tool that could dramatically improve the industry's dismal 20% project success rate. He contends that AI can compress weeks of work into hours for tasks like data modeling, code migrations, and stakeholder analysis, and that resistance often stems from protecting budgets and kingdoms rather than legitimate technical concerns.

Key Insight: AI commoditizes the production of mediocre work while making genuine craftsmanship and deep expertise more valuable than ever.

Joe Reis argues that while AI dramatically reduces the cost of producing content and code, the cost of creating high-quality, deep work remains as high as ever. He challenges the notion that citizen developers and vibe coding will replace professional engineers, predicting instead a 'Great Pacific Garbage Patch of AI Slopware' as prototypes inevitably become production systems. The key to thriving is pairing AI acceleration with genuine craftsmanship and mastery, not abandoning expertise altogether.

Key Insight: Even as AI transforms the data landscape, mastering fundamental data engineering principles remains the key to success.

Joe Reis shares a brief weekend newsletter highlighting a conversation about data strategy in the AI age, emphasizing that despite technological revolutions, success still depends on mastering the basics. The post primarily serves as a content roundup of weekend reads and podcast episodes.

Key Insight: In a transactional job market where employers won't invest in you, graduates must treat themselves as businesses—building differentiated skills in AI, accumulating reputational capital through quality work, and defeating obscurity through content creation and network building.

Joe Reis offers career advice for 2025 graduates, dividing guidance into timeless principles (continuous self-investment, network building, reputation management) and present-day tactics (embracing AI as a native skill, creating content to stand out). He emphasizes treating yourself like a business in an era of transactional employment while ruthlessly focusing on priorities.

Key Insight: AI is a force multiplier that amplifies whatever expertise you already have—making competent people more effective while enabling incompetent people to produce higher volumes of low-quality work.

Reis argues that the belief that AI eliminates the need for learning is dangerously misguided, as our already-shrinking attention spans and surface-level thinking are being exacerbated by AI tools that let people outsource critical thinking. While AI can amplify the capabilities of competent professionals who know what 'good' looks like, it poses significant risks in the hands of those without underlying expertise.

Key Insight: Data teams must abandon peacetime luxuries of exploration and tool accumulation, instead becoming scrappy, business-focused operators who deliver quick wins that directly support company survival.

In times of economic uncertainty and business volatility, data teams must shift from peacetime exploration to wartime survival mode, focusing on singular business-critical missions. Drawing from personal experiences during the 2008 financial crisis and COVID, Reis argues that data professionals should deliver quick visible wins, stay scrappy, and be integral to business survival rather than hiding in technical siloes.

Key Insight: AI coding tools have made programming genuinely enjoyable again by eliminating friction, but their power demands that developers maintain understanding of what they're building to avoid contributing to an inevitable wave of AI-generated slopware.

Joe Reis describes how AI coding assistants have revitalized his enthusiasm for programming by dramatically accelerating iteration cycles and reducing the friction of getting stuck. While embracing these tools, he cautions against mindless 'vibe coding' in production environments, warning that prototypes quickly become production code and that understanding what the AI generates remains essential.

Key Insight: The future of data engineering lies not in specialization but in convergence - as data engineers shift both left toward producers and right toward AI/ML applications, the boundaries between software, data, and ML engineering roles will dissolve into unified, customer-focused product teams.

Data engineers are not just 'shifting left' toward closer integration with upstream data producers, but also 'shifting right' into ML/AI work like building AI-powered apps and agentic workflows. This dual movement signals 'The Convergence' - the inevitable blurring and merging of software engineering, data engineering, and ML/AI roles into unified, customer-focused product delivery.

Key Insight: The combination of ruthless corporate layoffs and AI's explicit use as a headcount reduction tool means tech workers must abandon expectations of stable employment and embrace entrepreneurial self-reliance.

Reis addresses the widespread job anxiety in tech, noting that corporate layoffs have become brutal and impersonal while AI is now explicitly sold as a tool to reduce headcount. He argues that the era of stable 9-5 employment is ending within 5-10 years, and professionals must adapt by investing in skills, building networks, and embracing self-promotion.

Key Insight: Public speaking excellence comes not from eliminating fear but from building confidence through intentional practice, starting in low-stakes environments and gradually progressing while staying authentic to your own voice.

Joe Reis offers practical advice for aspiring public speakers in tech and data, emphasizing that speaking confidence comes from intentional practice rather than innate talent. He recommends starting with low-stakes environments like recording yourself, podcasts, and virtual meetups before progressing to live audiences at local meetups and eventually conference CFPs, while cautioning against over-reliance on slides that can become a crutch for inexperienced speakers.

Key Insight: AI should augment the writing process—research, conversation, scaffolding—but the actual writing must remain human because writing is thinking, and outsourcing it to AI cheats both writer and reader.

Joe Reis explains his AI-augmented writing process, emphasizing that writing encompasses research, thinking, and physical writing—with AI serving as a tool for research curation, conversation, and scaffolding, but never as a replacement for the actual craft. He argues that AI should assist but not author, because writing is fundamentally about thinking in one's own voice.

Key Insight: The relentless pressure to deliver fast is destroying the conditions needed for quality work, turning engineering into an exercise in patching problems rather than solving them properly.

Reis argues that organizations are failing to distinguish between Deep Work (design, architecture, thinking) and Delivery Work (shipping products), forcing both into rigid Sprint frameworks that don't accommodate the time and uncertainty inherent in creative work. This leads to accumulating technical debt patched over with 'bubble wrap and duct tape' rather than proper solutions, while practitioners feel unable to do quality work due to pressure to move fast.

Key Insight: Career longevity in data comes from mastering timeless fundamentals rather than chasing constantly changing tools and trends.

Joe Reis argues that while data engineering titles, tools, and vendors constantly change, the underlying fundamentals of the field remain constant—like gravity, everything eventually reverts to them. Drawing on wisdom from Bill Inmon, he distinguishes between 'fundamentalists' who think from first principles and 'technologists' who attach themselves to specific technologies, advocating for the former approach.

Key Insight: The gap between product vision and technical execution remains a fundamental organizational challenge that could be addressed by unifying AI and product development approaches.

Reis highlights the persistent disconnect between Product teams and engineering/data teams, noting different expectations, vocabulary, and timelines create confusion and frustration. He proposes exploring ways to unify these two universes, pointing to a conversation with colleagues who wrote about integrating AI and product development cycles.

Key Insight: True speed in data work comes from understanding orthodox approaches deeply enough to know when following or breaking them serves your specific situation.

Joe Reis argues that the tension between orthodox data modeling approaches and the pressure to 'move fast' creates technical debt that teams rarely pay down. He advocates for understanding conventional frameworks like relational and dimensional modeling before breaking rules, warning that ignorance of orthodoxy leads to either misapplication or costly reinvention.

Key Insight: The shine has come off Big Tech, making it feel dated and less aspirational than it was a decade ago.

This is a brief newsletter update where Joe Reis shares his busy workload and points readers to a conversation with Carly Taylor about why Big Tech has lost its appeal. The post primarily serves as a curated list of weekend reads, podcast episodes, and upcoming events rather than deep analysis.

Key Insight: As AI increasingly mediates human interaction and content creation, authentic in-person communities will become more valuable than ever as people seek genuine human connection.

Joe Reis argues that in-person communities are becoming increasingly vital as antidotes to AI-generated content, toxic social media, and widespread loneliness. Writing from Data Day Texas, he emphasizes that as AI permeates every aspect of life, people will seek authentic human connections over algorithmically generated relationships.

Key Insight: Writing a fundamentals book is harder than writing an expert-level one because distilling complexity into accessible knowledge for a broad audience requires both deep expertise and radical empathy for learners at every stage.

Joe Reis reflects on the 2.5-year journey since publishing 'Fundamentals of Data Engineering,' discussing how the book emerged from frustration with tool-focused learning and became a bestseller by teaching data engineering from first principles. He shares the intense personal challenges of writing—including working through COVID in a basement during a house remodel—and how the book transformed his career while attracting both devoted readers and inevitable critics.

Key Insight: Despite AI BI hype, the fundamental challenge remains unchanged: BI adoption has flatlined for years, and flashy new technology won't fix the deeper organizational and data quality problems that prevent analytics from driving real action.

Joe Reis examines the tension between traditional BI and AI-powered BI, noting that while old-school dashboards will persist due to ingrained habits, BI adoption has stagnated at 25% for seven years. He also addresses widespread corporate burnout among tech workers, advising them to either leverage their position for personal gain or exit the industry entirely.

Key Insight: AI agents face a fundamental bottleneck in enterprise adoption: the chaotic state of corporate data means companies must first fix their data foundations before AI can deliver on its promises.

Joe Reis explores the hype around AI agents in 2025, arguing that while they show impressive demos, they face a fundamental obstacle: the poor state of corporate data. Text-to-SQL remains immature and hallucinates on complex queries, and most enterprise datasets are too messy to expose to LLMs without significant cleanup.

Key Insight: Obscurity is your biggest career risk—building a visible personal brand through years of consistent, authentic content creation is the best insurance against the brutal reality that your job can disappear at any moment regardless of your internal reputation.

Joe Reis argues that in today's brutal job market, building a personal brand is essential insurance against sudden job loss, as even excellent internal reputations become worthless when you're unemployed. He shares his journey from local Salt Lake City meetups to global recognition through consistent content creation and authentic relationship-building, emphasizing that obscurity—not lack of skill—is the real enemy.

Key Insight: True mentorship is a non-transactional friendship where both parties benefit, but it requires the mentee to first reach a level of competence that makes them worth a mentor's investment.

Joe Reis reflects on the transformative impact of mentorship in his career, using his friendship with data warehouse pioneer Bill Inmon as the central example. He argues that genuine mentorship is non-transactional and based on mutual respect, where the mentee must demonstrate enough capability to be worth a mentor's investment.

Key Insight: Different geographies optimize for different outcomes—the US prioritizes wealth and speed while Europe emphasizes craftsmanship and quality of life—and neither approach is inherently superior.

Joe Reis responds to a reader accusation that he has a low opinion of Europe by explaining that while he writes US-centric content due to his location and audience, he actually spends extensive time in Europe and often praises European data practices. He argues that different geographies have different tradeoffs—the US excels at wealth creation while Europe offers better quality of life and often higher data craftsmanship.

Key Insight: The data industry's obsession with new tools over foundational knowledge is causing it to regress, and only a renewed focus on time-tested principles from industry pioneers can reverse this decline.

The data industry is regressing despite having excellent tools because practitioners lack foundational knowledge in areas like data modeling, normalization, and data warehousing principles. Rather than studying established practices from industry pioneers like Kimball and Inmon, many rely on hearsay, AI-generated answers, and tool-specific knowledge while skipping fundamentals. The solution requires both company investment in employee development and individual practitioners taking ownership of their professional growth.

Key Insight: Professional networks must be built through consistent, genuine contribution over time—not cultivated desperately when you need a job.

The post argues that professional obscurity is the greatest obstacle to career success, especially in tight job markets. Reis emphasizes that networking must be cultivated continuously through genuine contribution rather than transactional outreach when desperate for opportunities.

Key Insight: Organizations must resist the pressure to ship quickly at all costs and instead invest in quality practices upfront, recognizing that the short-term slowdown pays dividends through reduced rework and technical debt.

Joe Reis argues that the tech industry's obsession with speed and arbitrary deadlines has led to a cargo-cult adoption of Agile that sacrifices quality for output. He contends that slowing down to build quality into processes—through proper testing, data modeling, and established conventions—ultimately leads to faster long-term productivity and less technical debt.

Key Insight: Asking questions at conferences is a skill that requires self-awareness and discipline—your question should serve the dialogue and audience, not your ego or sales pitch.

Joe Reis offers practical advice on asking good questions at conferences, emphasizing that poorly crafted questions waste everyone's time and can damage your professional reputation. He provides specific do's and don'ts, stressing brevity, relevance, and open-ended phrasing while warning against self-promotion, rambling, and contrarian showboating.

Key Insight: Public speaking at tech conferences requires significant preparation and travel that most people underestimate, and speakers should advocate for fair compensation once their name carries value, while being wary of events that 'community-wash' their profit motives.

Joe Reis shares his extensive experience with public speaking at tech conferences, breaking down the different types of events (community vs. commercial, vendor vs. vendor-agnostic), the significant work involved in preparing talks and managing travel, and the importance of understanding your value as a speaker. He emphasizes that speaking requires substantial time investment, can be emotionally draining due to travel demands, and speakers should increasingly expect compensation as their reputation grows.

Key Insight: Data teams remain cost centers because they play defense instead of offense—to change this, they must stop passively processing data and start actively driving business outcomes.

Data teams are stuck in a defensive posture, passively receiving messy data and producing dashboards no one looks at, which perpetuates their status as cost centers. To break this cycle, data teams must actively integrate with the business and tie their work to critical initiatives rather than just going through the motions.

Key Insight: Despite massive AI hype, the data industry's fundamental challenges remain unchanged—most initiatives fail, teams are undervalued, and the gap between vendor promises and production reality continues to widen.

Joe Reis shares observations from his international conference tour, noting that while GenAI enthusiasm remains high, actual production implementations are rare, and practitioners are growing cynical about the gap between demos and reality. He observes that Databricks is gaining momentum over Snowflake in practitioner conversations, data teams continue to be undervalued as cost centers, and the 2020/21 data vendor landscape is consolidating to clear winners.

Key Insight: The relentless 'level up or die' mentality of tech careers is an anomaly rather than the norm, and most professions simply require you to be reliably competent rather than exceptional.

Reis contrasts the skill expectations of pilots versus tech workers, noting that pilots reach a competency ceiling where they simply need to safely transport passengers, while tech workers face constant pressure to upgrade skills. He questions whether the '10x engineer' mythology makes sense when applied to other professions, concluding that being 'good enough' varies dramatically by field.

Key Insight: The fundamental challenges of data initiatives—misunderstanding problems, lacking proper data, chasing technology over solutions—remain constant regardless of whether you call it BI, Big Data, or AI, and companies would benefit from realistic expectations rather than hype-driven adoption.

Joe Reis argues that while companies are rushing to adopt AI, most haven't even mastered basic BI, which historically has only a 20% success rate. He emphasizes that AI and BI are fundamentally different disciplines with different data requirements, and that the real challenge isn't the technology itself but the underlying difficulty of organizational change and data management.

Key Insight: Shifting from one-to-one consulting to one-to-many content creation (books, courses) enables greater impact and creates a self-reinforcing flywheel of opportunities.

Joe Reis reflects on his career trajectory, contrasting the high-friction, one-to-one nature of consulting with the scalable, one-to-many impact of creating books and courses. After the unexpected global success of 'Fundamentals of Data Engineering,' he's shifting focus toward content creation that can reach and help more people simultaneously.

Key Insight: Before chasing AI features, companies should focus on solving their fundamental data problems - you can't magically chat with data that's still broken at its foundation.

The article argues that the rush to add generative AI to data products is premature hype that distracts from fundamental unsolved problems like data quality, governance, and basic BI accuracy. The authors contend that most companies can't even get their dashboards to match, yet are being sold AI features that promise 'anyone can talk to their data' - a dangerous proposition when the underlying data foundations are broken.

Key Insight: The data industry's future hinges on whether the slow, collaborative work of building interoperable open technologies can provide a viable alternative to vertically-integrated platforms before AI consolidates power in a handful of megacorporations.

The data industry faces a fundamental choice between vertically-integrated mega platforms controlled by big clouds and a horizontally-integrated ecosystem of interoperable open technologies. While horizontal integration is slower and requires extensive coordination (the 'tortoise' approach), it offers better long-term maintainability and staying power. AI complicates this picture because model training costs currently favor plugging into major vendors' APIs.

Key Insight: Technology like AI should be invisible infrastructure that makes products better, not a marketing badge that substitutes for actual utility.

Reis argues that labeling products as 'AI-powered' is meaningless marketing fluff, comparing it to the absurdity of advertising a 1950s clock as a 'plastic clock.' The technology should serve the product's utility invisibly, not be the selling point itself.

Key Insight: Just as MMA forced martial artists to become multi-disciplinary fighters, the convergence of AI, data products, and cross-functional use cases is forcing data practitioners to master multiple data modeling approaches rather than specializing in a single technique.

Joe Reis argues that data modeling is evolving from siloed, single-discipline approaches toward a multi-disciplinary practice he calls 'Mixed Model Arts.' Drawing an analogy to how MMA fighters must now master multiple fighting styles rather than relying on boxing or wrestling alone, he contends that data professionals must become proficient across relational, dimensional, ML/AI, and semantic data modeling techniques to remain effective.

Key Insight: Career success shouldn't be measured solely by title and rank—find your own definition of success and pursue that path instead of defaulting to conventional expectations.

Reis challenges the conventional notion that career success means climbing the corporate ladder, arguing that many high-ranking executives feel unfulfilled despite their titles. He advocates for defining success on your own terms, whether through influence, helping others, or impact, rather than defaulting to the traditional path of title and prestige.

Key Insight: Data teams only escape the cost-center trap when they shift from serving internal reporting needs to enabling customer-facing products where data's value becomes directly measurable.

Data teams exist in two distinct universes: Enterpriseland, where they serve back-office IT functions and struggle to prove ROI, and Productland, where data IS the product and value is immediately clear. The key to data team relevance lies in shifting from internally-focused reporting to enabling customer-facing products and experiences.

Key Insight: Understanding the historical lineage of technologies—from foundational concepts to modern innovations—gives practitioners deeper insight into their field and prevents the industry from repeating past mistakes.

Joe Reis argues that the tech and data industry has a troubling tendency to forget its pioneers and foundational history, unlike other professions that revere their contributors. Understanding the historical evolution of technologies—from perceptrons to transformers—provides crucial context for comprehending current innovations like generative AI.

Key Insight: Before romanticizing entrepreneurship, honestly assess whether you can handle the brutal uncertainty—and if you proceed, validate with paying customers while still employed rather than recklessly 'burning the boats.'

Joe Reis argues that entrepreneurship isn't for everyone and offers pragmatic advice for those considering leaving corporate jobs to work independently. He emphasizes avoiding reckless risks, testing ideas with paying customers before quitting, mastering sales, and building genuine networks—while acknowledging that the discomfort and uncertainty of self-employment causes most people to eventually return to traditional employment.

Key Insight: Stop debating programming languages and start focusing on whether your product actually solves problems for users - that's the only metric that matters.

Programming language debates are a waste of time because they focus on irrelevant technical minutiae rather than what actually matters: whether users are happy with what you build and whether your business succeeds. The choice of programming language is almost never a factor in commercial success, and end-users don't care about your tech stack.

Key Insight: The tech industry is experiencing a white-collar recession that's making job searches difficult even for senior executives, while the AI hype cycle continues to reveal both opportunities and limitations.

This is a casual weekend newsletter where Joe Reis shares curated reads while working on writing projects from Marbella, Spain. The links cover AI developments, startup accelerator scandals, the difficult white-collar job market, LLM limitations, and the rise and fall of influencer-driven products.

Key Insight: The AI hype cycle is a rare window of opportunity to secure organizational buy-in for foundational data work like modeling—seize it before the bubble bursts.

Reis argues that getting buy-in for data modeling requires articulating value in terms stakeholders understand, showing what's in it for them rather than speaking in technical jargon. He emphasizes that the current AI hype cycle presents a strategic opportunity to finally get organizational support for data housekeeping and modeling initiatives that have long been neglected.

Key Insight: Balancing theoretical foundations with hands-on practice is the ideal approach to data engineering education, and scaling that education globally justifies significant personal investment.

Joe Reis announces his upcoming Data Engineering Specialization course on Coursera in partnership with deeplearning.ai and Andrew Ng, describing the intensive recording process. He explains this major project will delay his data modeling book but considers it worthwhile given the potential to impact hundreds of thousands or millions of learners worldwide.

Key Insight: While the data industry obsesses over structured data and LLM code generation, the real untapped opportunity lies in the massive amounts of unstructured data—images and video—sitting idle on hard drives.

Joe Reis provides a personal update on his various content projects including his upcoming data engineering course, data modeling book, and podcast appearances. He shares curated weekend reads covering LLM code generation benchmarks, Google's AI announcements, Spotify's data platform, and the impact of AI on education.

Key Insight: Career advancement in data requires intentional effort and mentorship, while the field itself continues to grapple with the gap between AI hype and reality.

This newsletter edition announces a new executive coaching course for data professionals looking to advance to leadership roles, discusses analytical data modeling approaches from a community session, and curates weekend reads including the critical xz Utils backdoor security incident and Amazon's 'AI' grocery stores that were actually powered by human contractors.

Key Insight: Writing is less about producing content and more about using the iterative process of drafting, resting, and editing to sharpen and clarify your own thinking.

Joe Reis shares his low-tech, methodical approach to writing articles, which involves hand-written outlines on legal pads, zero drafts in Google Docs, and multiple editing passes. He emphasizes that writing is fundamentally about clarifying your own thinking, not just producing content.

Key Insight: The tech industry faces a tension between AI's impressive capabilities and its practical risks, from corporate open-source bait-and-switches to dangerous feedback loops in AI-assisted development.

Joe Reis shares his weekly roundup of tech news and commentary while managing a heavy workload of courses, articles, and his upcoming data modeling book. The post covers major developments including Databricks' new DBRX LLM, the Redis licensing controversy, AI hallucination risks in code copilots, and the DOJ's antitrust lawsuit against Apple.

Key Insight: Despite recurring AI hype cycles predicting the death of coding and traditional data work, history shows these technologies tend to augment rather than replace existing practices.

Joe Reis announces a format change to his newsletter, splitting it into long-form articles and weekly curated reads. He shares commentary on Microsoft's strategic positioning against OpenAI, thoughts on AI's impact on coding jobs, and advocates for less 'data snobbery' in the industry.

Key Insight: The best data model isn't the most technically perfect one—it's the one that's fit for purpose and enables good business decisions within the constraints of your environment.

The post explores the tension between striving for perfect data models versus 'good enough' models that serve business needs. Drawing from a conversation with executive Sol Rashidi, Reis argues that while European data modelers tend toward rigorous, formal approaches, American practitioners often prioritize agility and practicality—and both can be 'good' depending on context. The key is matching your data modeling approach to business realities rather than pursuing technical perfection for its own sake.

Key Insight: Failed data projects are rarely just technical failures—they're organizational and political failures that require understanding stakeholder needs and building coalitions before touching the technology.

When inheriting a failed data project, the key is to resist the urge to start with technology and instead focus first on understanding stakeholder needs, uncovering 'dark matter workflows' (shadow IT solutions built outside the data stack), and investigating the political and organizational reasons the project failed. Success requires building relationships, setting realistic expectations, and making stakeholders the heroes rather than positioning the data team as saviors.

Key Insight: The Modern Data Stack succeeded so thoroughly at normalizing cloud-native analytics that the term itself became obsolete, but left behind a generation of practitioners who prioritized tooling over data fundamentals.

Joe Reis reflects on the rise and fall of the Modern Data Stack, from its promising beginnings with Redshift in 2012 through the ZIRP-fueled explosion of undifferentiated startups, to its current status as an obsolete term. While acknowledging the MDS ultimately succeeded by normalizing cloud-native analytics, he criticizes how the era promoted poor data practices and tool sprawl over fundamentals like data modeling.

Key Insight: The data community thrives when practitioners have vendor-free spaces to connect authentically, making indie conferences essential infrastructure for professional growth and genuine knowledge sharing.

Joe Reis reflects on the value of independent data conferences like Data Day Texas, contrasting their authentic, vendor-free atmosphere with corporate-driven events. He argues that indie conferences are vital to the data community because they allow practitioners to share knowledge and connect without commercial interference or carefully curated vendor messaging.

Key Insight: Data modeling must evolve from a human-centric discipline to one that equally prioritizes machine consumers, as automated systems now process the vast majority of modeled data.

Joe Reis proposes a new working definition of data modeling that expands beyond human-centric views to include machines as primary consumers of data models. He argues that traditional definitions focus too narrowly on analytics use cases while ignoring the vast majority of data modeling that occurs for automated systems, and advocates for a 'Mixed Model Arts' approach that draws from multiple methodologies based on situational needs.

Key Insight: Data engineering is maturing as a field, marked by a return to traditional enterprise practices and a shift from reactive firefighting toward strategic business value creation.

Joe Reis shares observations about data engineering trends in 2024, noting the field is maturing through tool abstraction, renewed focus on traditional 'enterprisey' practices like data modeling and governance, and increasing pressure on teams to demonstrate business value through FinOps. He sees this as both exciting (data engineering is finally recognized as critical) and boring (more traditional enterprise work), which he considers a positive sign of the field's maturation.

Key Insight: Despite the AI hype, 2024 will be a reality check year where AI tools must prove actual business value rather than ride on technological novelty.

Joe Reis announces his new 'Practical Data Modeling' project and upcoming book while sharing his 2024 goals including a data engineering course with deeplearning.ai. The newsletter curates weekend reads covering AI hype cycles, the failure modes of AI startups, and cultural commentary on topics from tipping to the potential return of a 'weird' decentralized internet.

Key Insight: Rather than making predictions or resolutions, focus on building strong daily habits that create a foundation for executing whatever goals emerge throughout the year.

Joe Reis reflects on the end of 2023 and shares his philosophy on predictions and resolutions. He argues that unexpected events trump linear predictions (citing ChatGPT disrupting the Modern Data Stack narrative) and advocates for consistent daily habits over annual resolutions.

Key Insight: The executives most excited about AI are often the ones who least understand data, while those who understand data know that most organizations haven't mastered the basics needed to make AI initiatives successful.

Joe Reis argues that the current AI hype in corporate boardrooms follows the 'Underpants Gnomes' pattern of 'AI...?...Profit' without understanding the middle steps. He observes that executives pushing AI initiatives often don't understand data fundamentals, while those who do understand data recognize how difficult it is to get basics right. Most companies can barely do basic dashboards yet dream of AI overlords running their organizations.

Key Insight: The greatest danger for data professionals isn't technical failure but organizational theater - going through the motions of 'data work' while never shipping anything that matters.

Reis argues that many data teams are essentially 'LARPing' - going through the motions of doing data work without producing anything that reaches production or delivers business value. He identifies symptoms like endless POCs and complex architectures that look good on paper but never ship, and advises data professionals to either find quick wins to break the cycle or leave for organizations where real work gets done.

Key Insight: Intellectual humility and admitting uncertainty is far more valuable than the false confidence of pundits who chase engagement by pretending to predict unpredictable events.

Joe Reis criticizes the flood of pundits who claimed to predict the OpenAI leadership drama, arguing that genuine uncertainty should be met with humility rather than false confidence. He reflects on the nature of forecasting, LLM limitations in data management, and the realities of startup life versus the dream of building businesses.

Key Insight: Data management needs to abandon its perfectionist mindset and embrace AI agents as the only scalable solution for managing today's data explosion.

Joe Reis argues that traditional data management's pursuit of perfect data has consistently failed and is fundamentally unscalable given the explosion of data types and volume. He proposes that AI-driven data management, using LLM agents to evaluate and curate datasets, represents the field's best chance at finally achieving meaningful results.

Key Insight: The chronic executive disconnect with marketing—a mature field—suggests that organizational dysfunction around data and AI leadership is likely far worse and requires fundamental rethinking rather than incremental fixes.

Joe Reis draws a parallel between the documented disconnect between CEOs and CMOs to argue that the gap between CEOs and Chief Data Officers is likely even worse, given that data is a much newer field. He highlights that CDOs are set up for failure due to unclear role definitions, lack of empowerment, and organizational confusion about data ownership and accountability.

Key Insight: The universal obsession with tooling in data teams is a distraction from the real work of understanding and solving business problems.

Joe Reis argues that data teams make a fundamental mistake by focusing on tools before understanding business needs. He emphasizes that the order of operations matters: start by understanding what stakeholders want, then select appropriate tools—never the reverse. Tool discussions without business context are distracting and lead to building things nobody asked for.

Key Insight: The AI gold rush of 2023 is producing more snake oil salesmen than genuine innovation, and companies would be better served fixing their data foundations than chasing AI hype.

Joe Reis criticizes the flood of companies 'AI-washing' their products at tech conferences, comparing them to 19th-century carnival barkers selling snake oil. He argues that most organizations will fail with AI because they lack data fundamentals, and urges buyers to take a sober approach rather than falling for hype.

Key Insight: Data engineering's maturation as a profession is hindered by fragmentation, requiring a shift toward global community-building and standardized practices to prevent every organization from reinventing the wheel.

Joe Reis observes that despite growing enthusiasm at data engineering conferences worldwide, the profession remains fragmented with practitioners repeatedly facing the same challenges in isolation. He argues that for data engineering to mature, the community needs to think globally and develop standardized practices rather than having every company solve the same problems from scratch.

Key Insight: Mastering foundational knowledge—not chasing new tools—is what separates competent data professionals from those who accumulate technical debt and eventually fail catastrophically.

Reis argues that the biggest gap in the data industry isn't tooling—it's the lack of foundational skills and knowledge to properly leverage those tools. He advocates spending 60-70% of learning time on fundamentals (classic books, papers, underlying principles) because they provide stable foundations for evaluating the constantly changing landscape of technologies and tools.

Key Insight: The AI/LLM revolution is exposing years of technical debt in data quality and modeling, making the 'boring' fundamentals of data governance and conceptual modeling suddenly urgent and unavoidable.

Joe Reis argues that the 'modern data stack' era is over and we're entering the 'boring data stack' era, where the focus shifts from managing underlying technology to tackling long-neglected fundamentals like data governance, data management, and data modeling. He sees the rise of AI/LLMs as the catalyst forcing organizations to finally get their data house in order, since messy corporate datasets that barely worked for BI are completely inadequate for AI applications.

Key Insight: The maturation of data infrastructure means the real work now shifts from technical plumbing to solving actual business problems and integrating AI into workflows.

Reis argues that as classical data problems become easier to solve, data professionals can now focus on higher-value 'boring' business problems and the next frontier of integrating ML/AI into business workflows. The post is a casual weekend roundup mixing this thesis with industry commentary and personal updates.

Key Insight: Data teams must shift from passive recipients of business requests to proactive partners who engage with and get ahead of business needs to earn respect and relevance.

Data practitioners worldwide feel misunderstood and underappreciated because they're too removed from core business activities, constantly playing defense. Reis argues that data teams need to proactively engage with business initiatives and demonstrate they can get ahead of needs rather than just react to them.

Key Insight: The AI hype cycle is a forcing function—if the data industry can't finally prioritize fundamentals like modeling, quality, and governance during this moment of peak attention, it never will.

Reis argues that while AI hype may be cooling among consumers, the real opportunity lies in enterprise adoption—but only if companies finally take data fundamentals seriously. He warns that rushing to layer AI on top of poorly modeled, low-quality corporate data will fail spectacularly, and challenges the data industry to lead from the front during this critical moment.

Key Insight: Data teams must proactively communicate their value and needs to dev teams because devs won't prioritize what they don't understand or aren't incentivized to care about.

The divide between development and data teams remains a persistent problem, with devs often unaware of or indifferent to data teams' needs. Data teams depend on dev for their data sources, but without clear communication and incentives, dev teams ignore their requests. Reis urges data teams to proactively build bridges through communication and education to foster empathy.

Key Insight: The data industry's struggle to deliver value stems not from inadequate tooling but from practitioners' failure to master fundamentals and read the documentation.

Despite the abundance of sophisticated data tools available today, teams still struggle to deliver business value because of a fundamental knowledge gap about data basics and best practices. The problem isn't the tools themselves—which are excellent and constantly improving—but rather that practitioners lack foundational skills in clean code, architecture patterns, data modeling, and stakeholder management.

Key Insight: The rhetoric of being 'data-driven' often conceals the reality that organizational decisions are driven by politics and personal career motivations, not by objective analysis of data.

Organizations claim to be 'data-driven' but typically use data to justify pre-determined agendas driven by politics and ego. Individual contributors have little control over their destiny despite Agile methodologies, as decisions flow top-down based on career motivations, not technical excellence. Understanding organizational politics and the motivations behind decisions is essential for navigating your career, especially during layoffs.

Key Insight: Raw compute power and modern tooling cannot substitute for thoughtful data modeling - prioritizing query performance over conceptual understanding leads to data warehouses that fail to deliver business value.

Joe Reis defends the continued relevance of Kimball dimensional modeling against critics who claim columnar storage and unlimited compute have made it obsolete. He argues that dismissing data modeling in favor of raw performance leads to incomprehensible data warehouses that fail to deliver business value, ultimately resulting in users reverting to Excel and data teams getting cut.

Key Insight: The data industry has traded disciplined data modeling for reactive, query-driven chaos, and Reis challenges practitioners to consider whether the old ways are worth preserving or if we've simply outgrown them.

Joe Reis argues that the data industry has largely abandoned rigorous data modeling in favor of reactive, query-driven approaches that prioritize speed over resilience. He provocatively asks whether we should simply 'burn it all down' and forget traditional data modeling practices, questioning if they even matter when companies seem to succeed without them.

Key Insight: In the current market, your ability to communicate and help others succeed matters far more than your technical abilities, which have become baseline requirements rather than differentiators.

Joe Reis argues that soft skills and people skills have become more valuable than technical skills in the current job market. With organizations shrinking and data practices maturing, the ability to communicate effectively with stakeholders has become the key differentiator for career advancement. Technical skills are now just table stakes, while interpersonal abilities determine who rises above the crowd.

Key Insight: The data community is being fragmented by vendor competition rather than unified around practitioner needs, and it's time for community-led, vendor-neutral conferences to fill that gap.

Joe Reis reflects on the awkward timing of Snowflake and Databricks conferences being scheduled in the same week, forcing the data community to choose sides in what feels like a vendor-driven wedge. He argues that big vendor conferences prioritize selling over community needs, and calls for more practitioner-focused, vendor-neutral data engineering conferences.

Key Insight: The AI gold rush will collapse unless the industry returns to data modeling fundamentals, because no amount of semantic layers or tooling can fix the broken data foundations most companies are building on.

Joe Reis argues that data modeling is critically endangered in the industry, and without reviving and modernizing this practice, companies rushing to implement AI and LLMs will fail because their underlying data is fundamentally broken. He proposes specific reforms including making data modeling a core practice across all teams, ending religious wars between modeling methodologies, and adopting a data product mindset.

Key Insight: The fragmented use of the word 'model' across data disciplines reflects a deeper problem: organizations lack shared conceptual frameworks for understanding their data, and returning to formal high-level modeling is essential to fix this.

Joe Reis explores the overloaded meaning of 'model' in data work—from ML models to dbt models to dimensional models—arguing that this terminological confusion fragments organizational understanding. He advocates for returning to higher-level conceptual and logical data modeling to establish shared vocabulary and coherent data practices across organizations.

Key Insight: Rather than fearing AI takeover, young people should focus on developing human skills, embracing change, and learning to work alongside AI as a tool.

Joe Reis shares his experience speaking to his son's 6th-grade class about AI, offering practical advice for young people navigating a world increasingly shaped by artificial intelligence. The post combines this personal reflection with curated weekend reading links covering AI, tech, business, and upcoming events.

Key Insight: The traditional organizational separation between software engineering and data teams is becoming obsolete as data moves from being a byproduct of applications to being the core of the application itself.

The post argues that the artificial divide between software engineering and data teams is outdated and counterproductive, especially as data becomes increasingly integrated into applications through ML/AI and streaming. Reis observes that while 'shit flows downhill' from dev to data teams in traditional org structures, this separation will inevitably disappear as data becomes the application itself.

Key Insight: Instead of exhausting yourself chasing every new technology trend, invest in mastering timeless fundamentals and first principles that will remain relevant regardless of which tools come and go.

Joe Reis argues that staying current in tech requires focusing on timeless fundamentals rather than chasing trends. He advocates for identifying your 'True North' - the unchanging principles in your field - and mastering first principles deeply rather than superficially jumping between technologies.

Key Insight: The Modern Data Stack's democratization, while valuable for startups, becomes a liability at scale when data applications require trust and reliability.

Joe Reis launches his weekend newsletter, explaining his preference for weekend reading when people have more time to engage with content. The inaugural edition curates articles spanning data team scaling challenges, AI regulation debates, developer productivity metrics, and architectural decisions around monoliths versus microservices.

Key Insight: Bridging the gap between development and data teams requires actively engaging with non-data audiences and fostering cross-functional dialogue.

Joe Reis delivered a keynote at SLC DevOpsDays 2023 about improving collaboration between development teams and data teams, framing it as 'shifting left and right.' The talk was notable for its interactive format with a predominantly DevOps and software engineering audience rather than data professionals.

Key Insight: Data professionals must flip their approach: instead of leading with data and technology, start by deeply understanding business problems in stakeholders' own language, then determine if and how data can help.

Data professionals make a critical mistake by leading conversations with data and technology rather than understanding business problems first. The business cares about making money, not data itself—data is merely a means to an end. Success requires learning to speak stakeholders' language and genuinely understanding their needs before proposing data solutions.

Key Insight: The data engineering career path encompasses not just technical skills but also entrepreneurship and navigating personal challenges like ADHD and mental health.

Joe Reis interviews Zach Wilson, a well-known figure in data engineering, about his career journey from staff data engineer at Airbnb to entrepreneurship. The conversation covers practical realities of being a data engineer, mental health challenges including ADHD, and building a large following in the data community.

Key Insight: The data industry needs more candid, unfiltered commentary rather than the polished narratives that typically dominate industry discourse.

Joe Reis announces his new solo podcast, The Joe Reis Show, as a companion to his existing Monday Morning Data Chat podcast. He positions it as a space for candid, unfiltered commentary on the technology and data industry.

Key Insight: Best-selling technical books require both grueling effort most aren't willing to put in and a healthy dose of luck that successful authors often fail to acknowledge.

Joe Reis and Matt Housley recorded a discussion about writing a best-selling O'Reilly technical book. The key ingredients are an enormous amount of hard work that most people won't want to do, combined with significant luck that authors often misattribute to their own efforts.

Key Insight: Data professionals must recognize that value is ultimately defined by shareholders, not their immediate stakeholders, and must align their work with the organization's highest-order priorities while advocating for the authority needed to act on their insights.

Value in organizations is hierarchical and transitive, flowing from shareholders down through executives, management, and individual contributors. During layoffs, data professionals must recognize that shareholder priorities ultimately determine what counts as valuable, and align their work accordingly. The challenge is that many data professionals have responsibility without authority, creating a frustrating gap between producing insights and delivering recognized value.

Key Insight: AI success requires boring foundational work—quality data, solid engineering, and clear problem framing—not hype-driven investment in algorithms.

Joe Reis argues that AI initiatives have historically failed because organizations skipped foundational work—quality data, proper engineering, and clear problem framing—in favor of hype-driven investments. Drawing on his experience in ML startups during the 2010s, he observes that the economic downturn is forcing a reality-based approach where companies must finally 'show me the money' and build solid data foundations before pursuing AI.

Key Insight: Long-term success comes from boring, consistent daily habits rather than ambitious annual resolutions - let compound interest work in your favor.

Joe Reis argues against traditional New Year's resolutions in favor of building consistent daily habits and systems. He suggests that long-term success comes from compound improvements over years rather than cramming goals into a single year.

Key Insight: WASM's ability to run databases in browsers could restore the peer-to-peer ethos of the early internet to data sharing, enabling truly decentralized data products without dependence on centralized cloud providers.

WebAssembly (WASM) could enable the decentralized data sharing vision of Data Mesh by allowing databases to run directly in browsers, bypassing centralized cloud infrastructure. This mirrors the peer-to-peer ethos of the early internet, where information flowed freely before large monopolies took control.

Key Insight: The cheap money era allowed data teams to exist as performative theater without delivering real value, but rising interest rates are forcing a return to fundamentals where only teams that measurably impact revenue or costs will survive.

The era of historically low interest rates created data teams that operated as 'performative theater' with unclear mandates and questionable value delivery. Now that money is expensive again, data teams must shift from 'growth at all costs' to proving concrete business impact through the only two levers that matter: making money and reducing costs.

Key Insight: The Modern Data Stack is entering a transition phase, and investors and practitioners alike need to think critically about what the next generation of data infrastructure looks like.

Joe Reis hosts Sarah Catanzaro of Amplify Partners to discuss the future beyond the Modern Data Stack, covering investment perspectives, AI/ML advances, and next-generation databases. The episode explores what comes after the current wave of data tooling and how investors evaluate the space.

Key Insight: The best data engineering solutions are defined not by what you add, but by what you can remove while still meeting requirements.

Simplicity in data engineering means designing systems with the fewest moving parts while maintaining required functionality. Data engineers often fall into cycles of building complexity then seeking simplicity, but there's no reward for unnecessary complexity.

Key Insight: Data teams must shift from technology obsession to customer obsession, because believability and value are defined entirely by stakeholders, not by the sophistication of your data stack.

The data industry is stuck in a Groundhog Day loop, perpetually asking the same two fundamental questions: 'Do I believe the data?' and 'How is this data adding value?' Despite decades of technological advancement and the hype around data roles, these customer-centric outcomes remain elusive because data teams focus on tools and technology rather than understanding stakeholder needs.