Six years after Microcultures, Ujwal Arkalgud arrived at a sharper claim than his first book implied. Consumer anthropology had been about helping large businesses see past demographics into belief systems. Building MotivBase, exiting, and writing Invisible Rules forced a different conclusion: the lens is not only research methodology — it is a force for social change for outsiders navigating rooms where the scoring system was never designed for them.

Arkalgud parallels this argument across his first book and newest offering, Invisible Rules (2026). In Microcultures (2020) the visible surface is consumer behavior; underneath sit competing belief systems. In Invisible Rules (2026) the surface is the entrepreneurial game; underneath is a hidden scoring system that advantages insiders. People both in in-groups and outside them often know unwritten rules innately but do not use them as a decision-making lens. Seeing is step one. Choosing, pitching, formatting, and getting paid inside those rules is where the weight lives.

Why does seeing the rules still fail?

Arkalgud co-founded and bootstrapped cultural analytics firm MotivBase in 2015, building to a high-eight-figure exit and acquisition by Lux Research in 2022, and now advises founders while building CredsAI. Across that arc, the recurring discovery is not that rules are hidden. It is that visibility does not automatically convert into action.

After Invisible Rules appeared, Arkalgud reports receiving many messages from people starting or building businesses — founders who sensed the unwritten game but had not used it as a decision lens. In his advisory work he describes a parallel pattern among people in anthropology and adjacent fields who consult or teach part-time: strong on the insight process, weaker on the business side of extracting value from intellectual property or reading what a specific room rewards. To help people move from seeing to action, Arkalgud offers structured cheat sheets — five to ten rule types with common manifestations and questions to ask — rather than open-ended ethnographic training. He argues most people do not tolerate raw ambiguity well; without structure, he sees them reach for AI and skip the thinking.

This diffusion pattern has a theoretical name practitioners rarely use. Pierre Bourdieu's symbolic capital describes how consumers convert purchases into membership signals within competing meaning-systems. In both Microcultures and at MotivBase, Arkalgud modeled this mechanism as an iceberg — only the tip is visible and conscious. Language is the vehicle for reaching what sits below it. What groups say, read with an anthropologist's hat on, reveals what they mean and what they believe. The internet is rife with that language, which is what makes the reading possible at scale. Microcultures offer a way to model and predict group change over time through studying language, meaning, and belief. But they too require a stomach for ambiguity.

Tellingly, the term microculture has since proliferated in agency decks and CMO keynotes, but often as a synonym for narrower segmentation. The belief-system layer gets dropped. Widespread adoption can vindicate a frame while hollowing out the move that made it predictive in the first place. Arkalgud's truism echoes here too: making a rule visible doesn't guarantee anyone will adopt it. The hidden piece is understanding when and why certain elements of symbolic capital get picked up — and when they get stripped.

Why does the report have to look familiar?

At MotivBase client deliverables had to do double duty. Researchers educated clients about culture and human belief systems in ways those buyers had not encountered before. Yet the report itself still had to look like what that corporation expected a research product to look like. Format, language, credentials on the call: each functions as an invisible rule.

Early MotivBase wanted to be punk rockers overturning corporate research. Clients weren't buying it. Break the wrong rules and you are the punk rocker nobody wants to hire. Bend the right ones and you become a differentiated piece that still fits the broader structure. The workable move is to differentiate on chosen dimensions, adhere on others. They could bend customer-language rules; they could not bend presentation rules. Arkalgud's recurring shorthand for the balance is "Adam Levine" — loved by many, not a scene purist.

The same logic governed MotivBase’s pipeline work. Before calling the economic buyer, Arkalgud’s team talked to subordinates for twenty minutes — learning who actually speaks, what pressure the org is under, whether the boss must nod first. He now teaches founders variations of that prep; the reaction he describes is surprise at how much room deep audience knowledge buys for an imperfect product.

Knowing both why a report needs to be familiar to your audience and what that familiarity looks like are key invisible rules and can make all the difference. Whether delivering a client report or pitching a new project, narrative architecture matters as much as insight. Arkalgud’s pitch begins with framing an inevitable shift (the train has left the station), then sharing an insider secret (something the buyer may not know and why you know it). Leading with credentials loses the room; scarcity of ideas makes people lean in.

He is blunt about the authenticity dodge: clients hire for value, not because you are authentically you. He recommends scripting and rehearsing uncomfortable lines until they become second nature.

For example, MotivBase went through a learning period confronting anthropology’s distrust of certainty against CPG and branding clients’ desire for quantitative decision-making. Reading the room meant learning who needed buy-in and how information moved through the organization. That let Arkalgud and his team reframe the problem: clients wanted estimates — something to “sink their teeth into” — not false absolutes. Used as a decision tool, quantified cultural signal reduces risk without requiring the certainty anthropology rightly distrusts.

What happens when everyone sounds the same?

Cultural fragmentation and AI-driven homogenization now pull in opposite directions. Niche communities proliferate online; generative tools compress prose toward an internet-average style — the word “tapestry” everywhere, the same logical smoothness, the same absence of human messiness. Arkalgud does not resolve the tension cleanly. Homogenization is the easy path - no thinking required. However, pure revolt fails too: say “epistemology” five times in thirty seconds and watch the room disconnect.

Daryani, Sourati, and Dehghani (2026) sharpen this tension empirically: LLMs standardize cultural output at scale even as platforms personalize delivery. If emerging discourse is increasingly AI-shaped, reading fragmentation gets harder exactly when the tools for reading it improve.

Arkalgud is bullish on AI tools but skeptical of lazy users. Decks that look perfectly logical are a tell — no thinking went into them. Humans are illogical; if research contains nothing surprising, the work failed.

How are credibility markers shifting?

Within five to seven years, Arkalgud expects Yale will matter less and “what you did last” will matter more. Information democratization opens doors for non-privileged entrants; credibility becomes industry- and context-specific.

His practical hack treats insider language as extractable data: record conference sessions, feed transcripts to an LLM, extract recurring phrases and credential references. CredsAI, the startup he is building, aims to make such signals legible at scale.

The moat question lands here too. Replacing manual work with AI is not a business. Naming an inevitable shift and owning an insider secret might be. MotivBase outlasted online focus-group companies that solved the logical problem without solving the cultural one.

What should a strategist actually do?

If this framework holds, most strategic failures in unfamiliar rooms trace back to skipped anthropology: not understanding who you are selling to, what format they expect, what shift they fear missing. AI accelerates both the mistake and the fix — faster slop, faster scraping of insider patterns. If software and prose are commoditized, the only thing left to differentiate on is whether a specific room finds you credible — and credibility isn't general, it's always credibility to someone. Figuring out what this room needs to hear is not optional overhead before the strategy. It is the strategy.

Further reading & listening

• Ujwal Arkalgud, Invisible Rules (2026) — Rule types and decision moves for entrepreneurs outside insider networks.

• Ujwal Arkalgud, Microcultures (2020) — Belief systems beneath consumer behavior.

• How to Study Meaning at Scale: AI and Big Data Ethnography, Microcultures and the Future of Innovation (2020) - Ujwal Arkalgud on Adam Gamwell’s This Anthro Life Podcast

• Pierre Bourdieu, “The Market of Symbolic Goods” (1985) — Symbolic capital; the mechanism practitioners strip when “microculture” becomes segmentation language.

• Daryani et al., “The Homogenizing Engine” (Sage, 2026) — LLMs standardize output while personalizing delivery.

• Neil Fligstein & Doug McAdam, A Theory of Fields (2012) — Whether mastering incumbent rules changes the game or reproduces dependence.

A challenge worth wrestling with

Reflecting on both my conversation with Ujwal and writing this essay, here’s the thing I’m still wrestling with: that making invisible rules legible is inherently democratizing. Arkalgud argues that visibility creates opportunity, and the volume of founder messages he describes suggests he is not alone. Fligstein and McAdam’s field theory offers a counterweight — people who learn to play the incumbent game well often become the next gatekeepers, structurally invested in the rules they decoded. If everyone can scrape conference transcripts for insider language, insiders adapt. What being an insider means changes. The question is not whether the rules exist. It is to what extent teaching people to read the rules changes the game for the next outsider? Does this democratize access or raise the bar on performing credibility convincingly enough to get the next meeting?

Share you thoughts in the comments below.

According to the American Academy of Arts and Sciences’ Humanities Indicators project, bachelor’s degrees in core humanities fields (English, history, philosophy, classical studies, languages, linguistics) dropped 42% between 2012 and 2024. The 69,254 degrees awarded in those disciplines in 2024 was the smallest number recorded since 1987. Across all humanities disciplines, degrees awarded have fallen in every single year since 2012. Colleges and universities have shuttered departments, and faculty hiring has dropped to historically low levels.

That’s not a rounding error. That’s a structural collapse happening over more than a decade. The question the new report tries to answer — why? — is a legitimate one.

Its answer is: politics. Specifically, ideological capture from the left has degraded scholarly standards, driven out dissent, and replaced knowledge-seeking with activism-dressing-as-scholarship. The report isn’t wrong that this happens. But it’s a much weaker case than it sounds.

What the Report Is

In August 2025, the chancellors of Vanderbilt and Washington University charged a committee of ten distinguished scholars — chaired by NYU philosopher Paul Boghossian — to assess whether complaints about “the deterioration of scholarly standards” in the humanities were justified.

The resulting Report on the State of Scholarship in the Humanities and the Humanistic Social Sciences (submitted April 2026) runs thirty pages. Its verdict: yes, there’s a real problem, though not as total as critics claim. Disciplines are producing serious scholarship alongside what the report calls “tendentious, biased research, feeble academic agitprop and jargon-laden nonsense.” Anthropology gets the harshest treatment — “widespread deterioration” and a “toxic intellectual climate.” History and philosophy get more mixed reviews.

The report explicitly condemns right-wing political interference too, including federal attempts to dictate what counts as acceptable history and a Texas A&M professor being told not to teach Plato. This isn’t a one-sided political hit piece. But it has serious problems.

The Conflict of Interest That Runs Through Everything

Paul Boghossian, the committee chair, literally wrote the canonical analytic philosophy book against postmodernism and relativism — Fear of Knowledge: Against Relativism and Constructivism (2006). That book is cited in this very report as supporting evidence.

That’s not a footnote problem. That’s a structural one. Asking Boghossian to assess whether postmodernism has damaged the humanities is like asking a committed prosecutor to assess whether the defendant is guilty. The conclusion was baked in before the work began.

Four of the ten committee members are philosophers — all from the analytic tradition, which has a longstanding professional disagreement with the “continental” tradition the report criticizes. Joseph Henrich, the Harvard biological anthropologist on the committee, has had his own work extensively criticized by cultural anthropologists — the exact subfield the report most harshly judges.

The Evidence Problem

The report’s most honest sentence is also its most damning: the authors admit their conclusions “are not yet supported by the kind of quantitative evidence that would be expected in a peer-reviewed study.”

Read that again. A report grading entire academic disciplines for failing scholarly standards does not itself meet the standards of peer-reviewed scholarship. And the detailed internal reports on each field? Never released to the public.

What they offer instead are examples — real ones, but examples. The 2017 Hypatia/Tuvel controversy (800 scholars demanded retraction of a published philosophy paper; the editorial board apologized) is documented and genuinely troubling. The American Anthropological Association canceling a 2023 panel on biological sex is real and legitimately controversial. A 2024 reanalysis by fifteen leading researchers shredding a widely-reported 2023 study about women hunters is verifiable.

These cases are real. But the report explicitly says they’re illustrative, not statistical — then uses them to make sweeping disciplinary verdicts anyway. Anthropology does not equal its AAA presidential addresses. A discipline with 10,000+ members spanning biological, cultural, linguistic, and archaeological subfields cannot be characterized by forum response pieces in one journal.

What It Gets Right Anyway

The report’s core philosophical argument holds up: scholarship that pre-determines its conclusions on political grounds produces unreliable knowledge. That’s true and important.

The distinction it draws between scholars having political commitments (fine) and political criteria distorting scholarly standards (a problem) is genuinely useful and consistently underappreciated.

The concern about self-censorship isn’t invented. Independent research — including a 2012 study in Perspectives on Psychological Science — found that social psychologists reported willingness to discriminate against conservative researchers in hiring and publishing. That’s a documented structural problem.

The Timing Problem

This report arrived during an aggressive federal campaign to defund and reshape universities. Both chancellors who commissioned it have been publicly arguing that universities need to rein in their leftward tilt to appease critics on the right. AEI — the American Enterprise Institute, a right-leaning think tank — celebrated the report’s release.

The authors explicitly warn against using their findings to justify administrative intervention in academic departments. That warning is genuine. It also may not matter. A document concluding that academic disciplines are politically distorted, released to university administrators during intense political pressure on those same disciplines, will be used as ammunition regardless of the authors’ intentions.

The Verdict

C+. A legitimate problem, diagnosed with a flawed instrument, released at the worst possible moment.

The irony the report never quite faces: it holds the humanities to a standard of disinterested, evidence-based inquiry — and fails to meet that standard itself.

If a graduate student submitted this as a dissertation chapter, the committee’s note would read: “The argument is sophisticated and the examples are real, but your evidence doesn’t support the scale of your conclusions. Revise and resubmit with the underlying data.”

Which, notably, is exactly the standard they’re asking humanists to be held to.

See Conrad Kottak’s response to the report, which echos a couple of Claude’s points.

Methodology

This post above was created by Claude Sonnet 4.6, without human editing. The views expressed are the model's own. If you’re on LinkedIn, you’ve probably seen posts raving about how great Claude’s language latest models are. While we aren’t hopping on that bandwagon, automated analyses like the one discussed in this post may 1) play to the strengths of products like Claude and 2) be, at the very least, appropriate provocations for human conversations about societal issues.

Multi-step prompting workflow:

Step 1 — Assessment prompt: The full PDF of the report was uploaded directly to the conversation. The prompt asked for a fair, objective, empirically grounded assessment of the report’s credibility, bias, and factual accuracy, in plain language with bullet points.

Step 2 — Grading prompt: A follow-up prompt asked the model to assign the report a letter grade with component-level breakdowns.

Step 3 — Drafting prompt: The assessment and grade were passed back in and the model was asked to write a Substack post leading with the grade, providing enough context for readers unfamiliar with the report, and more concise than the original analysis.

Step 4 — Refinement prompt: The model was asked to add a methodology section and to either back up or revise the headline claim “The Humanities Are in Trouble” with verifiable data.

Throughout, the model conducted live web searches to verify cited events (the Hypatia/Tuvel controversy, the AAA panel cancellation, the Anderson et al. / Venkataraman et al. dispute), identify current reactions to the report, and retrieve enrollment data from the American Academy of Arts and Sciences’ Humanities Indicators project. The enrollment statistics cited come from AAAS data published April 2026.

Verbatim Prompts:

Prompt 1:

“Do a fair, objective assessment of the attached report. How seriously should it be taken? Does it appear to be biased, performative, or in any way disingenuous? Is it based on factual information or does it reflect ideological bias? Ground your assessment in empiricism, backing it up with verifiable factual data to avoid a biased assessment. Use bullet points and plain language to make it as digestible to a general audience as possible.”

Prompt 2:

“Take this and create a Substack post, leading with the grade it received and an assessment that is more concise and easier to read than the attached. Provide the context so that the Substack post makes sense without having to do a bunch of extra work as a reader.”

American culture is being shaped by the erosion of shared narratives, institutions, and reference points, and this is affecting everything from academic freedom to corporate strategy. Central to that story is the rise of human science in industry as the influence of scientists on American public discourse declines. Culture Capitalist began as Human Science during the COVID-19 pandemic and evolved into a way to explore how culture and capitalism impact each other.

Awkward Sciences serves as a launchpad for the Culture Capitalist podcast and revisits our initial mission: building a platform that helps human scientists in industry and academia learn from each other and shape public discourse.

Assault on Paradise

The research that produces the most insight into cultural change often begins as a study of something else. For , Assault on Paradise began as a study of class structures in a fishing village in Bahia, Brazil, called Arembepe. He first visited in 1962 as an undergraduate at Columbia University and found a tight-knit community organized around fishing. Arembepe became the site of his dissertation research and much of his work as an anthropologist. He returned repeatedly over decades — 62 years!. This longitudinal approach allowed him to track how technological and economic development generated new class distinctions and demographic diversification.

Kottak applied the same approach to television in Prime Time Society, studying how the medium reshaped cultural life as it spread throughout Brazil. Technology can drive meaningful cultural change, and anthropologists often find themselves positioned to document it and understand how it happens. Their perspectives are attuned to sociotechnical systems, and cultural change is always systemic, even when it happens quickly or what some call “at the speed of culture” nowadays.

This kind of work is crucial for understanding how technology shapes cultural change, from what happens when villagers start adding motors to their fishing boats to how artificial intelligence shapes life with social media today.

Understanding Cultural Change

Assault on Paradise was how I first learned about ethnographic fieldwork, through which a behavioral researcher becomes part of a community to observe, participate in, and document life firsthand. I read it as a freshman at the University of Michigan, where I took Anthropology 101 with Kottak as the instructor for the first half of the course.

He also advised my creation of an individualized major called Semiology of Visual Culture, which I designed to study how film, television, advertising, and the performing arts shape culture and produce meaning. I pursued it just as commercial semiotics was emerging in the UK in the early 2000s.

Kottak’s advisor, Marvin Harris, left Columbia for the University of Florida in 1981 as he emerged as one of the most prominent public intellectuals in the field. Harris was notable for his unflinching alignment with empiricism at a time when postmodernism was gaining ground across the humanities and social sciences. While many of his contemporaries were challenging claims of objectivity, Harris argued that cultural phenomena had material causes, often through books written for general audiences. I did not have the opportunity to meet him, but I continued my studies at UF for graduate school, still learning from his former students and colleagues under the guidance of UF’s department chair, Allan Burns. With his guidance, I was able to pursue my interest in industrial anthropology by studying an unusual topic for my dissertation.

The Thirty Meter Telescope project brought together Caltech, the University of California system, and the astronomical associations of Canada, Japan, China, and India to build a next-generation giant telescope. My work focused on how scientific and social factors shaped the site selection process.

My dissertation research started as a study of how the TMT organization would choose between Mauna Kea in Hawaii and Cerro Armazones in Chile. TMT’s science advisory committee had initially focused on Cerro Armazones, the reference site, but attention eventually shifted to Mauna Kea. This was driven by Mauna Kea’s extremely high scientific value, but also by what some stakeholders saw as an opportunity to introduce a new model for astronomy on Mauna Kea.

The summit of Mauna Kea holds profound significance for some Native Hawaiians as a sacred site. In Hawaiian cosmology, the mountain connects the realm of the living to that of the ancestors and the gods, making it spiritually and politically meaningful. That significance has coexisted uneasily with decades of astronomical development. The first telescope was built near the summit in 1968, and the site eventually grew to house thirteen. TMT’s new approach integrated social innovations, a more collaborative approach with Native Hawaiian groups (than previous astronomy projects), and a multi-million-dollar fund to support STEM education on the Big Island.

The site selection was announced in July 2009, within a few days of the official end of the Great Recession. The economic situation on the Big Island had been severe, and this contributed to the project having unprecedented support within the local community. By the time construction was scheduled to begin in late 2014, a new dynamic was emerging. Younger generations appeared more opposed to the project than the elders who had originally positioned Mauna Kea as a viable option. Short-form and image-based social media also created an alternate reality for many people, especially those learning about TMT for the first time. Celebrities contributed to this, culminating with Dwayne “The Rock” Johnson claiming that the telescope would be the size of a football stadium on The Tonight Show. The media environment had shifted; new forms of organizing, alongside new audiences, had emerged; the economy had improved; and what the telescope meant as a cultural symbol had become as consequential as the scientific work it was designed to help accomplish.

Symbols often have diverse, and sometimes divergent, meanings. What they mean depends on which community is interpreting them and when. To some, TMT symbolizes a new, more equitable approach to astronomy on Mauna Kea and Big Science in general. To others, it represents settler violence against Hawaiians and their sovereign kingdom. Short-form platforms like Twitter, image-based media like Instagram, and social movements like #LandBack have put this multi-billion-dollar Big Science project in uncharted waters, along with the Hawaiian community and the Big Island.

My study of where to build TMT became, unavoidably, a study of how economic, societal, and technological systems drive cultural change. This was precisely what made Assault on Paradise interesting to me as a college freshman. My project would, at the time, have been more difficult to pursue in a department that did not integrate applied anthropology. I transitioned from this research on organizational culture and technology development into applied work as a consultant, moving from Gainesville, Florida, to Brooklyn just as anthropology, ethnography, and design thinking were getting cool.

Anthropology Gets Cool Again

Florida’s universities have had an outsized impact on the social sciences, and researchers often emphasize practical value beyond academia. For example, Harris collaborated with McKinsey and Company in the early 1990s to help establish the McKinsey Global Institute. While the University of Florida combined aspects of an “old guard” department with applied research, the University of South Florida was the first American university to offer an MA and PhD in applied anthropology. Nonetheless, in 2011, Governor Rick Scott declared that Florida did not need more anthropologists and should focus on tech jobs.

That was the same year I began my first project for a major tech company with Tom Maschio through Maschio Consulting on how people use mobile devices. It coincided with another project on cultural mergers and acquisitions with Serena Saitas at REAL Brand Strategy. With Serena, I had the opportunity to work on a project for a client acquiring its biggest rival in a deal that would put them on the Fortune 500. My interest in that topic had emerged just a couple of months earlier after attending a session at the American Anthropological Association’s annual meeting on how evidence of acquired companies remains visible through material culture (artifacts). With Tom, I was able to start working on pioneering Big Tech projects a couple of years before there was a surge of anthropologists joining product teams as UX researchers.

Since then, my work has spanned the subjects I had been interested in since childhood: the evolution of car design, semiotics for media brands, and work for advertising clients that scratched an itch I had since the “Got Milk?” commercials of the 1990s. All of this was made possible by what I had learned at Michigan and Florida.

I went from saying “this is NBC” as my first words and being obsessed with the peacock logo to helping media companies develop culturally relevant strategies.

A New Divide

While my transition from Michigan to Florida had been seamless, there appears to be a meaningful divide emerging now. When UF’s board of trustees unanimously selected Santa Ono for the role of president in 2025, my initial reaction was shaped by something that might seem trivial. Ono was leaving his job as president of the University of Michigan at the time, and his move would have been the first instance in which a sitting Michigan president left to lead a public university in its 200+ year history. He was an unusual prospect, a Japanese Canadian fan of American football. I thought he would have a chance of winning approval from the board of governors if he could talk about football.

Ono had been instrumental in Michigan winning a national championship the previous year. His support was crucial in retaining head coach Jim Harbaugh after the team went 2-4 in the 2020 COVID season, a time when much of the fan base was calling for his dismissal. Harbaugh responded by leading Michigan football’s transition to a student-led culture, an approach that essentially revived a model not fully embraced since the early days of American football when the sport was created by college students. This shift led to three consecutive Big Ten championships in 2021, 2022, and 2023. Harbaugh’s time at Michigan ended with a perfect season and a national championship.

UF won two football championships and two basketball titles during my time there, so I knew from experience how a major national championship can benefit a university’s culture. Ono appeared poised to help restore the Gators football program to greatness by taking a special interest in it, just as he had at Michigan.

But the questions he faced tended to focus on ideological alignment, such as how many genders he believed existed and whether he thought climate change was real. This would have been unusual when I was in graduate school at UF. The Florida Board of Governors voted 10 to 6 to reject Ono, the first time in its history it had overturned a trustee selection. While my experiences at Michigan and Florida were very similar, they now appear to be on different trajectories because of cultural change. The difference is even more pronounced across Florida’s other public universities.

Sanitizing Sociology

Beginning in 2023, Florida passed legislation restricting instruction in concepts deemed “unproven, speculative, or exploratory.”

Sociology was removed as a general education option (it’s still an elective). By 2025, the Board of Governors determined that every introductory sociology textbook in use at Florida universities violated state statutes. The new text is 267 pages, cut down from the original 665-page text.

The chapters that were removed cover:

• Deviance, Crime, and Social Control
• Media and Technology
• Social Stratification in the United States
• Global Inequality
• Race and Ethnicity
• Gender, Sex, and Sexuality

If your work covers one of these areas, this project is particularly relevant. Please reach out if you’d like us to discuss and share it, which could be anything from an academic book or an industry research report. We’re starting with Media and Technology.

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The textbook has been distributed across the state system, with institutions including Florida State University and Florida International University adopting it. Although all 28 public colleges in Florida’s system are required to use the state curriculum beginning this summer in 2026, the University of Florida has declined to adopt the textbook, treating it as supplemental.

Why Sociology?

What’s happening with sociology may be a sneak peek of what is to come across higher education in Florida. Some might have expected anthropology to be targeted before sociology, since sociology is more easily associated with large datasets, quantitative analysis, and the legacy of the discipline’s founder, Auguste Comte.

Comte argued that the social world should be studied using the same empirical methods as the natural sciences, what he called positivism. For Comte, human societies were subject to discoverable laws just as the physical world was, and the task of sociology was to identify them. Anthropology is broader in scope, pioneering qualitative methods like ethnography alongside quantitative, archaeological, and biological research.

Florida’s leadership removed most of the textbook because the content is “unproven, speculative, or exploratory,” but they didn’t provide any supporting data, evidence, or examples to explain why they removed what they did.

We’re Here to Help

It is reasonable to question whether sociology has drifted from its scientific foundations. A useful concept for thinking about this is what Karin Knorr Cetina describes as epistemic cultures. She argues that each field develops its own way of producing knowledge, shaped by its history, constraints, and internal norms, and that these frameworks determine what counts as evidence. But these cultures tend to be resistant to change.

So we’ll analyze the material Florida has removed, evaluating both its empirical strength and its relevance to industries beyond academia. We’ll also engage the material directly and openly.

The Call

This project is most useful to organizations that use the social sciences in their work as well as students, faculty, and researchers who are going to be impacted by these changes. We’ll also highlight the work of subject matter experts across industry and academia as we discuss what we can learn from the cultural changes we are seeing in Florida, throughout academia, and across American society now. Subscribe to Culture Capitalist if you are interested in following, or collaborating on, this project.

Awkward Sciences is the start of Culture Capitalist’s exploration of societal change and cultural fragmentation.

Since you made it this far, Culture Capitalist is self-funded and we'd appreciate shares, re-posts, and likes in lieu of paid subscriptions.

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Mind Invaders Game Concept

As 2025 kicks off, organizations are publishing their perspectives on how technology, industry, and human behavior are changing. Recently, Amy Daroukakis, Iolanda Carvalho, Ci En L., and Gonzalo Gregori shared their impressive collection of 200+ trend reports from 2024. Seeing this many high-quality research reports in one place raises an obvious question: How can we leverage AI to harness the collective insights of research like this more effectively? Researchers and strategists across industry and academia are beginning to answer this question.

Research for Thought Leadership

Thought leadership research, which includes trend reports and other public-facing analyses, comes from a diverse range of sources — from agencies and brands to foundations and think tanks. Several venture capital firms (Primal, Harlem, Forerunner, Coefficient, and a16z) even published reports in 2024.

When prepared in concert with research professionals, thought leadership tends to be high quality. And organizations generally craft their points of view carefully because their content has the potential to help or harm their reputations. Sometimes organizations partner with each other to pool their resources and the reports are usually free because they’re intended to engage public audiences and, oftentimes, generate leads.

The main challenges of working with these reports are:

• Curating them thoughtfully to separate content that’s more focused on promoting organizations or biases than offering useful insights supported by data.

• Aggregating the best reports to build a nuanced understanding of issues and trends, so individual reports can complement each other with different insights, data points, and perspectives.

• Investing the time required to read and synthesize hundreds or (hypothetically) thousands of research reports — before the emergence of generative AI and Retrieval Augmented Generation (RAG).

RAG combines the language capabilities popularized by ChatGPT and Claude with the ability to search through and reference specific documents in real time. Using this technology, an app can pull and synthesize insights from vetted research reports and show users the original source material that was used for generating responses. It's vitally important to understand the limits of RAG before using it for research (see our primer video). But, when used properly, it gives users the power to query massive datasets instantaneously, aiding in the discovery of patterns and insights that would otherwise take much longer to uncover — if at all.

Leveraging AI

As last year wound down, we embarked on a side quest to collect 500 high-quality research reports from 2024 to help demonstrate how AI can be applied to research and strategic foresight. We started by:

• Finding trend reports in the treasure trove from Amy and her team

• Leveraging trendsunplugged.io, another excellent trend report aggregator

• Using Google search (including filetype:pdf) to find more reports on specific topics and trends

• Mining Scribd to discover a few that were difficult to find through Google

• Going down various rabbit holes to find reports from organizations that publish excellent research but keep lower profiles (there are many)

It's impressive to see how much research is freely available but still underutilized. We're already approaching 600 curated reports from 2024 and will keep up with the current flurry of trend reports. We're making daily additions and improvements to our app. There’s a lot of work that goes into optimizing a platform for this use case — such as refining API instructions, adjusting parameters, automating data collection, and adding relevant features — but it generally plays to the strengths of RAG.

Preview the App

We've built the platform to help users navigate the wealth of information available from public sources, syndicated reports, and custom datasets. A preview of the app is available upon request so we can manage our API costs and stay within the limits of fair use. We’re also seeking beta users to help us test and improve new features, like explanatory diagrams. Visit futurearchive.xyz to check it out.

Collaborating closely with organizations whose publications are used by the platform is crucial for serving their thought leadership objectives alongside the need for a high-quality product that's suitable for professional use. We believe that working with the domain experts behind thought leadership content is an ethical imperative that comes with strategic advantages. Getting permission from our first cohort of content partners will take some time and we'll share our ongoing work privately while we build. But the goal is to eventually make a public version of the professional app that’s freely available. And we’ll continue to post about what we learn along the way through this newsletter.

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The Rise of Open Access

We'll end our first post about trends with one that we’re seeing: An inflection point for open access content. The two primary drivers are:

Institutional: This year marks a major shift in research accessibility because - as of December 2024 - a number of governments and grantmaking organizations are requiring all data and content based on research they fund to be made available through an open access license. The US is a holdout; so data from federally funded research can still be kept behind paywalls from the public that paid for it. However, there are several major organizations in the US, like the Bill & Melinda Gates Foundation, participating in this initiative. If you want to learn more about the policy, it's called Plan S.

Technological: RAG is enabling the masses to more effectively leverage high-quality, open access content. Like thought leadership research, open access books from domain experts are abundant, freely available, and tend to be packed with useful information and expertise. RAG makes accessing it easier than ever. It’s also useful for creating content inexpensively, including summaries and analyses of lengthy source material.

What’s Next

In addition to the AI-powered foresight app we're building, we're creating one for relevant open access books that are published under CC BY licenses that allow it. Stay tuned and, to explore the reports Amy and her team have put together, follow this link. It's fun to try them with products like Google's NotebookLM, especially the podcast feature. Chloé de Ruffray has also created a list of AI tools that she’s found useful for analyzing trend reports. And visit futurearchive.xyz to check out what we’re building.

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In an interview with Mario Gabriele from The Generalist earlier this year, Reid Hoffman, a prominent venture capitalist, described his work as “predictive anthropology.” He said:

This, along with other references to anthropology by VCs over the past few years, signals that venture capitalists are increasingly recognizing the relevance of anthropology to their work. Both social scientists and VCs can track cultural change through signals that offer clues about what may be happening. Regarding the potential for anthropological research in venture capital, an early sign is a 2006 blog post by Union Square Ventures about how research appears to be changing:

The fact that this statement was made, at least partially in jest, almost 20 years ago makes sense because anthropology and industrial research were very different then. There was nowhere near as much public awareness about the usefulness of anthropological perspectives in tech, and culture was less of a corporate buzzword. Nonetheless, anthropologists were sought out by leaders in several industries, including private equity, for consulting gigs and research projects. Since then, the methods researchers in industry employ have become more varied, and the value of social and behavioral science (i.e., human science) is now embraced by much of the corporate world. The value of anthropological perspectives in particular is heralded within influential companies and by well-known thought leaders.

Other fields, like strategic foresight and commercial semiotics, have also been shaped by anthropology and proven useful for understanding cultural change through management consulting.

American anthropology is notable for being a holistic science that explores physical systems, such as those associated with human biology and people’s use of materials, alongside social systems, such as how people communicate and behave towards one another. This makes anthropological perspectives uniquely useful for understanding how physical and social systems impact each other. Anthropology has unique value to VCs due to the field’s emphasis on culture and how it changes. However, the field’s secret sauce when it comes to understanding how people create and use technology is its holism.

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While the relationship between anthropology and venture capital has been changing for years, both physical and social factors would have to align for any real paradigm shifts to take place. On the social side, several VCs recognize the value of perspectives from fields like anthropology and psychology. Human scientists are also studying venture capital, with the most ambitious work coming from researchers like Erika Brodnock and Johannes Lenhard. The most apparent barriers to human science becoming commonplace in venture capital firms have primarily been the expense and time required to conduct the research, i.e., physical constraints. This is especially the case with conventional anthropological methods, which tend to be associated with resource-intensive interviews, ethnographies, and in-depth analyses. However, technological advances like the introduction of generative AI are making research more efficient. Because of the interconnectedness of social and physical systems, technological change is causing the work of researchers to evolve. As this shift reduces the material constraints associated with human science research, it should be expected to take place where it’s needed more often.

If human scientists find ways to collaborate with VCs to the degree they have in industries like product design, advertising, media, entertainment, management consulting, and private equity, the results could be profound. While VCs are interested in the systems human scientists study, they also impact them in more meaningful ways because of the nature of their work. Anthropology and venture capital may seem like an odd pairing, akin to the two contrasting worldviews of David Graeber, an influential anthropologist, and Peter Thiel, an equally influential VC. They had very different opinions about the future humanity is building and shared them through a debate called Where Did the Future Go? in 2014. Both anthropologists and VCs have a vested interest in understanding the human experience and how it changes. As cultural change happens faster than ever in ways that are increasingly difficult to predict, anthropologists and venture capitalists will need to learn from one another to truly understand what’s happening and why. And when that paradigm shift finally happens, it will have been decades in the making.

Ferret combines the speed and affordability of an AI research platform with the depth of custom datasets from vetted experts.

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Cultural Moats

The concept of an economic moat, popularized by Warren Buffett, refers to a company's capacity to sustain a competitive advantage. A cultural moat gives a company a competitive edge through the cultural beliefs and practices of the company, its customers, and other stakeholders. Companies often build cultural moats through the value and meaning of their brands and products to consumers. This happens when companies think beyond the usefulness of what they offer to how what they do contributes to people’s lives holistically, on functional, economic, social, and emotional levels.

Companies with strong cultural moats tend to have distinctive brands, loyal customers, and a societal impact that sets them apart from other companies. For example, Disney’s devotees form a subculture that has become the subject of serious academic scholarship (Disney Studies), shaped political discourse, and influenced culture globally. Cultural value is built over time through effective experiences and narratives. While Disney’s cultural value is often tied to nostalgia and customers’ memories, Apple’s is more often associated with envisioning how technology is shaping the future. The cognitive processes for remembering the past and imagining the future are powerful and closely connected to each other. And these companies have used them in the creation of especially impactful cultural moats. There’s a lot to cover when discussing cultural moats, so let’s start with an example that’s been in the news lately.

Apple Messages

Apple is known for providing an integrated user experience across its hardware and software products, reinforcing the perception that the company goes beyond creating gadgets to helping customers maintain a way of life, i.e. cultural practices. This is famously known as the Apple ecosystem. But the company’s strategy is holistic and effective because it goes beyond technical functionality. Apple's cultural moat includes a symbolic ecosystem.

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iPhones are known for color-coding messages depending on whether or not they were sent from iPhones. When iPhone users receive messages from other iPhone users, i.e., cultural insiders, they are in blue bubbles. When they receive text messages from other kinds of phones, like Androids, they are in green bubbles.

Apple has been public about its plans to add support for Rich Communication Services (RCS), which Android adopted in 2019. With RCS functionality, iPhone users will soon gain features like typing notifications, read receipts, high-resolution media sharing, and heightened security when communicating with non-iPhone users. This change makes a meaningful difference on a technical level, but it’s likely that Apple Messages (formerly iMessage) will keep color-coding texts because of how important symbolic communication is to Apple’s cultural strategy. The color of text bubbles matters to some consumers, particularly in the US, where iPhone (and Apple Messages) has a particularly high market share. As a result, there is an ongoing quest to bring blue bubbles to Android devices, with multiple companies attempting different strategies.

AirMessage (2018)

AirMessage was the first significant attempt to bring blue bubbles to Android devices. The solution requires a Mac computer to act as a relay server. Users have to install server software on their computer, which forwards Apple Messages to their Android devices. This setup is cumbersome because it requires users to make sure their Macs were always on and connected to the web.

BlueBubbles (2020)

BlueBubbles attempted to improve on the approach taken by AirMessage. This was accomplished by eliminating the need for a continuous connection to a Mac, but the requirement of a Mac-based server still limits adoption.

Sunbird (2022)

Sunbird introduced an app that brought iMessage functionality to Android devices without the need for an Apple device as a relay. This approach simplifies the setup process with the goal of making it compelling to a broader audience. However, the solution is technically challenging, resulting in reliability issues and some unavailable features.

Nothing (2023)

The makers of the Nothing Phone partnered with Sunbird to create Nothing Chats. It allows Android users to send messages to iPhones through blue bubbles as long as they have a Nothing Phone and share their Apple ID with the service. However, the service faces the same reliability issues as Sunbird's standalone app alongside the security concerns that come with users having to share their Apple IDs with a third party.

Beeper Mini (2023)

Beeper Mini was an app connected directly to Apple’s servers, allowing users to send and receive blue bubble messages without needing an Apple ID or a relay server. Beeper Mini also offered end-to-end encryption, addressing the privacy concerns associated with previous solutions. Beeper Mini was the most technically impressive and user-friendly option, but Apple acted swiftly.

Apple disabled Beeper Mini's integration with Apple Messages, citing security concerns. Beeper attempted to restore service several times but Apple continued disabling it. Beeper eventually conceded defeat and shut down Beeper Mini. This incident drew the attention of regulators and lawmakers. The U.S. Department of Justice, which was already conducting an antitrust investigation into Apple, expanded its probe to include the company's reaction to Beeper Mini. And several politicians have called for an investigation into Apple's practices, arguing that they may be anti-competitive. So while Apple’s strategy demonstrates how much successful companies value their cultural moats, it also reveals the risks some may take to protect them.

How to Build a Cultural Moat

We’ll discuss how organizations build cultural moats in follow-up posts, but one useful perspective in the context of the iPhone’s green and blue bubbles is semiotics. Semiotics is the science of signs and symbols. Blue and green text bubbles have strategic value to Apple because of their symbolic meanings to American iPhone users. In the corporate world, semiotics is often discussed in terms of “residual, dominant, and emergent” cultural phenomena. While semiotic perspectives go beyond this framing, it’s often how people are introduced to semiotic concepts. This is because symbolic meanings tend to change with culture — and culture is changing faster than ever. For example, think about how the symbolic meaning of a blue check mark on Twitter has changed over the years. And Twitter’s cultural moats have also undergone dramatic changes in just the last couple of years. Black Twitter hasn’t been the same since the Fune in November of 2022, but there are also new subcultures finding a home on the platform now. So at a time when culture seems to change by the day, Apple’s ability to keep blue and green bubbles at the core of its cultural moat is especially noteworthy.

We’re using AI to help organizations conduct semiotic analysis and integrate human science into their decision making. Learn more below.

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It’s essential for organizations to understand cultural issues during leadership changes. The following discusses this in the context of college sports and strategies for maintaining and building competitive advantages rooted in culture using the example of the University of Michigan’s football team, the Wolverines.

Takeaways:

• Recruiting for Culture Fit: Jim Harbaugh hired assistant coaches and other personnel who enabled a more equitable distribution of power, reviving the culture and re-centering student-athletes.

• Distributed Leadership: Flattening the team’s culture allowed the organization to become more agile and resilient to adversity, including personnel disruptions.

• Players as Culture Creators: New traditions practiced by the football team, such as the Turnover Buffs ritual introduced by Will Johnson, demonstrate how students contributed to Team 144’s culture.

• Vested Interests in Culture: Leadership by players fostered commitment to the team because student-athletes had vested interests in the culture they built.

• A Return to Form: Elevating the agency of student-athletes revives the ethos of the earliest days of college sports and appears particularly well-suited to the changing landscape of college sports.

• Developing a Cultural Strategy: Organizational culture consulting enables organizations like the Wolverines to maintain and build cultural advantages by addressing social and behavioral systems more strategically.

Culture, Beyond the Buzzword

The University of Michigan’s football team has won the 2024 National Championship. The Wolverines' success tends to be attributed, in large part, to the team's culture. Michigan's former head coach, Jim Harbaugh, is widely credited as the driving force behind the team's cultural revitalization. But what did he actually do? Culture is complicated and whenever it's mentioned in public discourse, it's usually talked about in vague, generic terms… vibes instead of social and behavioral science. So, let's use cultural anthropology to go beyond vibes and answer three questions:

1. How did Jim Harbaugh change the Michigan Wolverines' culture?

2. Why did this cultural change restore Michigan's football team to greatness?

3. What can other organizations learn from the Wolverines' success?

Using AI for Research

To explore these questions, we’ll use Ferret — an AI research assistant that’s designed for professional use. It allows us to ask research questions through a knowledge base that is robust enough for in-depth research. Ferret helps users analyze their own internal research materials as well as discover a myriad of vetted sources ranging from podcasts and research reports to expert interviews and bespoke datasets.

A Player-Led Culture

Jim Harbaugh created a culture at the University of Michigan that is notably different from the kind he experienced when he played quarterback for Coach Bo Schembechler in the 1980s. Schembechler's Wolverines were coach-led through a conventional hierarchy. Now, Michigan’s team is known for being uniquely player-led — a departure from Schembechler's team and others associated with what's often called the "old school" of college football.

Having a player-led culture doesn't mean coaches don't still lead their teams. Instead, they create and manage systems that empower players in ways that drive the team's success. Player-led cultures develop decision-making skills in the players and foster a sense of shared ownership in the team.

The most notable modern example of a more conventional, coach-led college football team is Nick Saban’s at the University of Alabama. The Crimson Tide was a perennial national championship contender during his tenure, which came to an end less than 48 hours after Michigan’s national championship victory. The last game Saban coached before retiring was a loss to Michigan in overtime at the Rose Bowl. Saban explained that his decision to retire was influenced by how college athletics is changing, alluding to the fact that the way he ran the Tide’s football program was misaligned with where the sport is heading. Michigan’s most recent championship team, known as Team #144, embodies a team culture and coaching style that are more aligned with where college football is going. To fill the coaching vacancy left by Saban, Alabama hired the University of Washington’s former coach, Kalen DeBoer. His Washington team lost to Michigan in the National Championship game the week after Michigan beat Saban’s.

Many of the ways Jim Harbaugh and his team created their unique player-led culture are visible only to insiders, but some of the most important factors are discussed by Ferret through an analysis of publicly available sources.

A Team Effort

Michigan's revitalization reached an inflection point when Jim Harbaugh hired several young, former student-athletes to assume leadership roles — particularly between 2019 and 2022. Harbaugh hired personnel into the Wolverines' football organization who were, in many ways, more similar to his players than they were to him. These new leaders allowed Harbaugh to flatten the team's culture by elevating the agency of its student-athletes.

Organizations with relatively flat cultures tend to foster more open communication and less emotional distance among members. This can be especially important at Michigan, where football players are elite athletes and full-time students in equally elite academic programs. In fact, several of Michigan's new hires during the team's revitalization were players at Michigan just a few years before they went back to work there. Unusually good rapport between players and the team's leadership likely led the coaches to trust players to take key roles in building the kind of culture that would lead to their success. This has become evident in various ways, including the close bonds among the players and their newest traditions.

New Rituals & Culture Creators

One of the Wolverine's most visible rituals is called the Turnover Buffs. This new tradition involves Michigan's defense taking a photo following a turnover by the opposing team, with the Wolverine who caused it wearing symbolically meaningful sunglasses. They are made by Cartier and known as "Buffs" because their frames are made of buffalo horn. They become popular in Detroit during the early 2000s and were introduced to the Wolverines in 2022 by a player named Will Johnson, who was a freshman at the time. This symbolic incentive for players, alongside increased emphasis on turnovers in the team's preparation for games, led to Team 144 becoming the most effective team at causing them in the country as measured by turnover margin.

The Wolverines are currently the only national championship team to finish their season first in this metric during the College Football Playoff era. Turnovers were a key component of Michigan's unique and historic run, including during Team 144’s last game. Washington had been particularly effective at scoring quickly throughout the season with explosive passing plays led by their quarterback, a Heisman runner-up named Michael Penix Jr. However, Michigan's defense made two crucial interceptions in the second half of the game. Will Johnson caused the first, shifting the momentum to Michigan when the Wolverines were up by a touchdown. The final score was 13 to 34, Michigan.

Rituals play a number of vital roles in the success of organizations and Johnson introduced the Buffs at a time when he was one of the youngest players on the team. Nonetheless, his idea has become an integral part of the team’s culture. He’s also capitalizing on this contribution through his own Turnover Buffs brand.

A Vested Interest in the Culture

In addition to creating new cultural practices, like the Turnover Buffs ritual, giving players significant roles in creating their team’s culture also fosters a sense of responsibility in it. Team 144 included a multitude of veteran players, many of whom will likely be NFL rookies soon. It’s easier than ever for student-athletes to leave their programs by transferring or going pro after a relatively short time on a college team. The Wolverines were able to retain their most experienced players and reach their stated goal of winning a national championship, in large part, because of the loyalty shared by cultural insiders. Team 144’s players were attached to the Wolverines' culture because they built it.

Distributed Leadership

Strong, relatively flat cultures also enable teams to be resilient to unexpected disruptions, including periods when key people at the helm of their organizations become unavailable. For example, Sherrone Moore was able to step in and lead the team to wins as head coach for the last three — most difficult — games of Michigan’s regular season when Jim Harbaugh was suspended by the Big Ten Conference. In terms of players, Michigan is notable in that there were no teammates who overshadowed the rest of the team. It’s usually the case that national championship teams have a player who finishes among the Top 3 Heisman finalists. For example, when the Crimson Tide won its most recent national championship in 2020, the team had a second runner-up in addition to the Heisman winner on its roster. It’s notable that Team 144, which is often characterized as being unselfish, did not have a Top 3 Heisman finalist because it’s also the most successful team in Wolverines’ history, with 15 wins and no losses.

The Value of Social Capital

Jim Harbaugh ultimately coached at the University of Michigan for nine years. The program’s unique value and meaning to Harbaugh led him to leave the NFL in 2015, where he was having success. At Michigan, he was given the time he needed to turn the Wolverines' football team around because he returned to his alma mater with an enormous amount of social capital based on the trust and expectations of Michigan’s cultural insiders. Many students and alumni believed that if the team could be revitalized, Jim Harbaugh would be the one to do it. This remained the case after the 2020 season when the team had 2 wins and 4 losses in the midst of the COVID-19 pandemic. He has demonstrated the real-world impact of changing a team’s culture but also how difficult it is. Managing systemic and structural issues like culture takes time and expertise. Michigan’s new head coach, Sherrone Moore, became the clear successor to Harbaugh following his coaching success during Harbaugh’s suspensions at the end of Michigan’s regular season. He now has to leverage the social capital he’s built with Michigan’s cultural insiders and fans to guide the program through its next transition.

Playing to Cultural Strengths

Harbaugh’s return to the University of Michigan was likely most surprising to people who are unfamiliar with its culture. While all universities have culture, some are more powerful than most. Harbaugh’s decision to leave the NFL and revitalize Michigan’s football program was driven, at least in part, by the attachment he feels to Michigan. The symbolic value of Michigan’s culture to insiders is an invaluable asset as the Wolverines continue to evolve. Leveraging it is easier said than done, but it is among the team’s most powerful competitive advantages. Institutions have cultural advantages beyond the attachments made by insiders, but these are even more difficult to understand and maintain. The Michigan Wolverines' culture will undoubtedly evolve as they transition from Harbaugh’s era to Moore’s, so fully understanding Team 144’s culture would be a time-sensitive initiative. This is because the most impactful aspects of an organization’s culture are often hidden in plain sight, overlooked by insiders, and missed during conventional succession planning. How U-M’s football team handles its culture moving forward will be a significant factor in whether Harbaugh’s legacy is defined by Team 144’s national championship, him laying the groundwork for a Moore Era defined by unprecedented success, or something in-between.

Lessons from Team 144

Creating and maintaining a player-led culture requires systems-thinking alongside decisive, inclusive, and innovative leadership from coaches, managers, and administrators. These senior leaders have to implement and manage systems that enable leadership to take place strategically and agilely throughout their organizations, from a CEO or coach to a new hire or freshman student-athlete. What matters is that leadership contributes to the team achieving its objectives. Creating Michigan’s unique culture was risky, but Harbaugh brought in the people who could help him build and use it most effectively. In Michigan’s case, the greatest risk was not taking one. When leaders operate within systems undergoing significant changes, conventional “tried and true” thinking often leads to failure. This is currently the case in college athletics and virtually every industry navigating the current era of unprecedented societal and technological change.

Team Cultures for a New Era

Jim Harbaugh demonstrated the importance of recruiting for culture fit. A program’s culture also needs to fit within broader institutions and systems that impact its success. Ultimately, if Michigan’s strategy includes leveraging the program’s apparent cultural advantages, the team will have to:

1. Understand Team 144’s culture and why it was successful.

2. Address the team’s cultural strengths and weaknesses intentionally.

3. Navigate cultural threats and seize cultural opportunities as they arise.

Success rooted in culture may appear to be self-sustaining, but it requires an especially profound understanding of social and behavioral systems so they can change in the ways that they need to change while preserving what shouldn’t. While the fundamental human needs met by elements of culture are timeless, the ways cultures meet them tend to evolve. Corporate leaders often seek out advice from applied anthropologists and management consultants to address these issues and they’re best understood through approaches that go beyond conventional change management. There’s clearly a need for the same kind of work in athletic programs like Michigan’s, particularly as they experience unprecedented institutional changes. In the context of college sports in the US, they include:

• Collectives: Independent groups of fans and boosters who pool funds to support student-athletes through endorsement deals and other financial opportunities

• Name, Image, and Likeness (NIL): Opportunities for college athletes to earn income from the use of their name, image, or likeness for endorsements, sponsorships, and business ventures

• New Transfer Rules: Relaxed NCAA regulations that give student-athletes more freedom to transfer between schools without having to sit out a year of competition

Jim Harbaugh has been a vocal advocate for more equitable business practices in college sports and he has built the foundation for a uniquely diverse program that re-centers student-athletes. This began by bringing in the assistant coaches and other personnel who would build Team 144’s culture. For continued success in this area, it’s imperative that the Wolverines understand what made this unique culture so effective in specific and actionable terms. As the NCAA also begins to re-center student-athletes, Harbaugh’s model of leadership and culture-building appears to be the most viable. The so-called “old school” appears to be at risk of becoming obsolete — or culturally irrelevant — as college sports enter a new era. As the pace of cultural change accelerates, anthropologists may join psychologists in the most innovative programs. While behavioral sciences like sport psychology are best-suited for optimizing the well-being and performance of individuals, social sciences like organizational anthropology optimize the well-being and performance of teams, programs, and communities.

Everything Old Is New Again

Harbaugh flattened the power structure of the Wolverines' football organization through bold leadership at a time when the role of student-athletes in the US is fundamentally changing. The organization he and his team built at Michigan runs counter to what is usually described as the old school, but it revives the ethos of the earliest days of college sports. This was when students took it upon themselves to create a cultural phenomenon that would grow into an American institution. Before student-athletes were building their own brands and their coaches were making 8-figure salaries, college sports were student-led — because they had to be.

Organizational Culture Consulting

Although culture is a topic that is discussed from many perspectives, most of the methods used to understand it come from cultural anthropology. Survey research can give an understanding of what people think about their organizations. More qualitative methods like participant-observation and ethnographic interviews go deeper to explain why cultural insiders think and behave the way they do. As a result, anthropology and its methods are now embraced by management consultancies, including large firms like Deloitte and McKinsey & Co. They are often applied to address issues within the world’s most impactful corporations, where culture tends to be an especially complex and consequential topic. Anthropological perspectives are also well-suited to organizations seeking competitive advantages grounded in culture through:

• Cultural Principals

  • Guidelines for building and maintaining effective cultures based on mixed-methods research, incl. participant-observation, surveys, expert interviews, stakeholder analyses, and unmoderated sessions

• Cultural Playbooks

  • In-depth guides and content created in collaboration with teams that integrate cultural principles and additional research with stakeholders, incl. co-ideation, participatory co-design, and group workshops

• Cultural Roadmaps

  • Strategic planning that integrates cultural principles and playbooks with landscape analyses of macro-level shifts to find emerging opportunities, threats, and competitive advantages

Addressing cultural issues is crucial to the Wolverines football program if maintaining its cultural advantage is a priority, but also to the men’s basketball team as it undergoes its own transition. While cultural problems are reportedly an underlying cause of the basketball team’s recent challenges, Michigan’s football program has the challenge of understanding, maintaining, and building on its unique cultural advantages. U-M’s football and men’s basketball programs demonstrate the impact of organizational culture on the success of sports teams. And the value of cultural consulting to athletic programs is becoming more apparent as the cultural landscape of college sports evolves.

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ICTC has provided upskilling, reskilling, and on-the-job training to over 118,186 people across 36 different initiatives in the digital economy. The organization’s programs and resources are aimed at helping job seekers, employers, and educators succeed in the digital economy through evidence-based research and policy advice. This partnership will provide users of our research platform with ICTC's research reports and policy briefs. Stay tuned to Culture Capitalist for updates.

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Artificial intelligence (AI) is transforming research across academia and industry (UX/design, brand/communication, management/strategy, etc). In this new era, researchers need to understand its capabilities and shortcomings. We began noting some of the issues researchers face during an interactive course we offered in collaboration with the Wenner-Gren Foundation and the American Anthropological Association last year. Now, as we think through and gauge interest in another on AI, we’re seeking input from the human science and research communities. If you are interested in improving how you use AI in your research, as an individual or organization, please complete the interest form below.

Mini-Course Interest Form

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Academics are increasingly sought after for Subject Matter Expert (SME) interviews, particularly by corporate clients grappling with complex problems. Their depth of academic knowledge is highly valued across industries, and the surge in interdisciplinary collaboration has significantly fueled the demand for expert interviews. SME interviews serve as a unique opportunity for academics to transcend their traditional silos, offering insights that extend beyond academia and often come with more substantial compensation compared to what’s typical in academia.

If you’re an academic who has been approached for an SME interview, you should be prepared to do the following.

Understand the Purpose of the Interview:

• The best clients will address expert prospects' concerns preemptively by explaining the interview's purpose, compensation, and the role of the interview in the project. Maintain an open mind and feel free to ask any questions before agreeing to the interview. When approached by expert networks or corporate clients, carefully evaluate the request and be respectful of the potential client’s time. They don’t need to hear a whole mission statement from you but will take time to fully address any concerns that you have if they truly value your expertise.

Align on the Value of Your Expertise:

• Academics should understand the value of their expertise and exercise caution because some expert networks, direct clients, and consultancies will offer modest compensation. The value of your academic knowledge is comparable to that of your corporate counterparts, particularly to for-profit companies using expert interviews as part of resource-intensive projects. Don’t sell yourself short and confirm that the compensation you are being offered is appropriate for what is being asked of you.

Be Ready to Code-Switch:

• Account for your client’s culture and their potential unfamiliarity with academic jargon. Using plain language and real-world examples tends to enhance clarity. You should also be open to in-depth discussions with clients who need them. There are meaningful differences across industries, so a financial services client will have a different mindset and style that an advertising agency. Understand how the client will want to interact and feel free to discuss this before participating in an interview.

Be Aware of Time Constraints:

• Account for the time allotted for SME interviews. Think through how to deliver insightful and concise responses that ensure the interview remains engaging and impactful. If your interviewer has follow-up questions, they will ask them. Be attuned to moments when you’re spending a lot of time on particular answers so interviewers can guide you through the questions they have without spending too much time on any one question. While extensive preparation may not be necessary, it is essential to understand how your expertise aligns with the client's needs. Think about the work you’ve done that’s relevant to the topic and be prepared to reference it as needed in a timely manner.

Prepare for Potential Questions:

• Anticipating potential questions is essential for feeling comfortable before an interview. Feel free to ask clarifying questions to align with the interview's focus before and during the interview. Recognize that it's acceptable to refrain from answering questions outside your expertise or those that may pose a conflict of interest. Embrace the collaborative nature of SME interviews, understanding that a client has vetted your expertise and is likely interviewing multiple experts to answer their questions fully. The value you provide may come down to one answer or comment that sets the client on a better path, so don’t feel like you have to try to answer

Academics may initially feel skittish when contacted by corporate clients offering money for confidential conversations. In navigating expert interview opportunities, you should approach corporate collaborations with the same degree of wariness as those in academia. SME interviews often lead to enriching experiences that broaden the impact of academic expertise beyond traditional boundaries. It's often the case that when clients reach out to academics, their goal is to understand a topic to a sufficient degree so they can address it responsibly. By actively seeking alignment with corporate clients, academics can make their work more meaningful to society while earning compensation commensurate with the value of their expertise. In some cases, it means that a grad student can make as much as a CEO — even if only for a moment in time.

Ferret provides SME interview opportunities and coaching to experts across industry and academia.

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Business anthropology involves the application of anthropological theories and methods to the study of culture in the context of businesses and their customers. In the US, it can be traced back to the Hawthorne Studies, which began in the 1920s when American anthropology was in its infancy. Today, anthropologists are uniquely positioned to understand human behavior and cultural change. But it's also important to consider how well the work of anthropologists fits the culture of businesses that hire them. For example, collaborations between anthropologists and advertising professionals are probably not too surprising. The advertising industry is known for its creativity and anthropologists like Robert Morais, Tim Malefyt, Bob Deutsch, and Tom Maschio, have been instrumental in crafting successful advertising strategies as consultants, account planners, and executives. Adjacent industries have also embraced anthropology, evidenced by Dori Tunstall’s work in design and Grant McCracken’s work with the entertainment industry. It seems fitting that leaders who work closely with advertising, design, and entertainment creatives would turn to anthropologists to help them understand what drives human behavior and cultural change. Financial services, on the other hand, could seem as though it's the complete opposite. This is only part of the story because, while anthropology is used across the sector today, private equity has been an early adopter of business anthropology.

Despite the wealth of quantitative data that private equity professionals usually have when making investment decisions, they continue to hire anthropologists to help them understand what numbers alone can't:

• Behavioral patterns related to industry sectors: For example, examining what the growing popularity of organic pet food says about how people feel about their pets and what it means for the types of pet food companies a firm should acquire

• Behavioral patterns related to individual companies: Such as going to a resort that's under consideration for an acquisition to understand why business is going the way it is by observing what customers do there

• Cultural due diligence: For example, revealing the risks and opportunities that result from the culture at a company that's being considered for an acquisition, such as when two rival companies could end up having to collaborate or even merge their cultures

Private equity firms have hired anthropologists as consultants to improve their due diligence and investment processes since the 1990s. However, venture capital has yet to catch up. Anthropology, alongside other human science fields, has the greatest potential for investors in companies that rely on more difficult-to-quantify cultural changes to succeed. For example, a major question within the tech community today is how artificial intelligence will change human behavior. Anthropologists have already developed a massive body of work on how technology shapes culture and some anthropologists, like Genevieve Bell, have specific expertise in AI.

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Cultural change can be quantified in various ways, such as by showing the growth of smartphone and ride share use over the last decade. However, understanding why cultural changes occur is challenging because they are typically the result of numerous systems acting on each other. For instance, Apple's $3,000,000,000,000 ($3 trillion) market cap is attributable to economic factors but also the symbolic value of owning Apple products and even the colors used in its messaging software (including the blue and green bubbles in iMessage). Anthropologists have developed methods and models to understand what drives cultural changes and their perspectives can help investors move from thinking about culture in terms of vibes to understanding how socio-technical systems impact it. In the context of how Apple has become one of the the world's most valuable companies by harnessing symbols, semiotics (the science of signs) and symbolic anthropology are particularly useful.

Gillian Tett is best known for bringing her background in cultural anthropology to bear in predicting the financial crisis of 2007-2008. In her most recent book, Anthro-Vision, she discusses the value of anthropological intelligence, another kind of AI, in business—particularly in the era of Environmental, Social, and Governance (ESG) concerns. Anthropologists have historically thought about capital more holistically than economists and financial professionals because they spend as much time considering the value of environmental and social capital as they do studying financial capital. And, while this perspective is still novel in venture capital, ESG has already helped introduce this aspect of anthropological intelligence to the world of finance. In some ways, venture capitalists already do work that's similar to that of business anthropologists, which may be one reason they often discuss and write about culture.

The business community often talks about culture in the context of behavioral norms and values within companies. The popular phrase "culture trumps strategy" usually refers to the critical importance of companies having the right culture among the staff, leadership, and other corporate stakeholders. But angel investors and VCs also tend to capitalize on cultural change happening around their portfolio companies, across society, when they're successful. Anthropology is a field that is designed for the study of culture and has produced a critical mass of professionals who can help investors keep up with the ever-increasing pace of cultural change. And early tech investors are instrumental in driving cultural change through the companies they support. As a result, it's imperative for them to understand how culture is changing as well as how it could change as a result of their investments. Change is already happening, evidenced by VCs beginning to seek out experts on culture and tech ethics for subject matter interviews.

We help investors conduct research by combining the speed and affordability of an AI research platform with the depth of custom datasets from vetted experts. Our offerings include:

• Cultural Insight and Foresight: Helping investors understand the implications of cultural change on their investments and helping portfolio companies navigate, learn from, and drive cultural change

• Commercial Semiotics: Giving investors a competitive edge by helping them understand residual, dominant, and emergent customer behaviors and needs

• Organizational Culture: Helping portfolio companies build sustainable and effective organizational cultures

• Cultural Due Diligence: Assessing the cultural threats and opportunities that prospective and current portfolio companies must navigate for success

• Gap Analysis: Supplementing investors’ existing data with robust insights to identify and address gaps in understanding

Learn About Ferret

We are recruiting Subject Matter Experts and Authors of fictional stories to help us study the impacts of artificial intelligence on:

• The Creative Process for Fiction Writers and Readers

• The Ability of Creative Writers to Make a Living

• The Consumption of Creative Writing Content

About the Subject Matter Expert Questionnaire:

• 5-8 minute duration, participants can opt-in to longer sessions.

• Names of participants are included in the final report after confirmation of consent from included participants

• Participants can opt in to having their work promoted via a hyperlink alongside their name

• Relevant areas of expertise include: artificial intelligence, tech ethics, creativity, innovation, design, and social/behavioral/human science

• Participants (i.e. co-creators) will be credited in the final publication after confirming that they consent to 1) being credited 2) having a quote from them included if one is included in the final draft.

• Writers may opt to have their names included and work promoted in the final report

Brief Screener Survey

Responses are digitally recorded and processed privately by AI (i.e. not used for training language models). All questions are optional and data that is not included in the final publication will be deleted after analysis.

We are using Ferret, an AI chatbot for research and content creation, for this study.

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We are recruiting Subject Matter Experts and visual artists to help us study the impacts of artificial intelligence on:

• The creative process for visual artists

• The ability of visual artists to make a living

• The intellectual property rights of visual artists

About the Subject Matter Expert Questionnaire:

• 5-8 minute duration, participants can opt-in to longer sessions.

• Names of participants are included in the final report after confirmation of consent from included participants

• Participants can opt in to having their work promoted via a hyperlink alongside their name

• Relevant areas of expertise include: artificial intelligence, tech ethics, creativity, innovation, design, visual art, and social/behavioral/human science

Participants (i.e. co-creators) will be credited in the final publication after confirming that they consent to 1) being credited 2) having a quote from them included if one is included in the final draft. Visual artists may opt to have their names included and work promoted in the final report, which will include principles for using AI in the creation of visual imagery.

Brief Screener Survey

Responses are digitally recorded and processed privately by AI (i.e. not used for training language models). All questions are optional and data that is not included in the final publication will be deleted after analysis.

We are using Ferret, an AI chatbot for research and content creation, for this study.

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Artificial intelligence (AI), especially generative AI, has ignited discussions, debates, and controversies. While there are numerous benefits and applications of AI, it is essential to acknowledge and address the associated challenges. Generally, these challenges fall into three categories, popularized by Donald Rumsfeld:

• Known Knowns: things we know we know

• Known Unknowns: things we know we don't know

• Unknown Unknowns: things we don't know we don't know

Known Problems with Generative AI

Known problems are discussed in various ways among different communities. In public discourse, they are relatively easy to talk about at a high level. The solutions to known problems tend to be less interesting to general audiences, but teams building products have to tackle them. For example, hallucinations, or the generation of responses that deviate from training data, are a known issue with large language models. In some contexts, such as for research applications, they are a known problem. In others, such as in generating art, they are a powerful capability. And, like many known issues with generative AI, they can be effectively mitigated and addressed through product development, design, and user guidance. So, the teams building generative AI products discuss known ways to modulate the degree to which AI responses include hallucinations and think through the design challenges and opportunities presented by them.

Generative AI for Art

Designers and developers can embrace or mitigate hallucinations when they focus their AI products on specific user needs. When using AI for visual art, hallucinations allow products to use the data they are trained on as a springboard for generating new creations. This can lead to visually impressive results while creating new challenges in recognizing the contributions of artists and other intellectual property rights holders. However, by leveraging existing models from industries that already deal with intellectual property rights, it should be possible to create equitable systems and business models that compensate artists appropriately. AI products have the potential to help artists protect their rights and monetize their work alongside creating new work.

For example, a product could integrate the byzantine licensing system for musical productions within a generative AI offering for music production. Imagine a product, let's call it Earmarker, that helps users generate new musical tracks and beats. It would rely on content from human musicians, much of it high quality but undervalued, to create music that has additional value while helping new audiences discover the work that was used by the system. A generative AI tool could help users with their creative work while tracking the use of source material so that artists who have opted in could receive compensation based on the use of their work, i.e., a stake of any revenue from the final musical production. Similarly, in domains like the visual arts and creative writing, AI systems have the potential to compensate and credit artists through commissions and royalties.

Known problems often overshadow unknown problems, which are more challenging to explore through conversations. However, thought leaders discussing AI ethics and safety bear the responsibility of directing their audience's attention thoughtfully because unknown problems pose the greatest risk due to being the last to receive attention. Addressing problems requires in-depth discussions, a strength of most academics, and attempts to solve them, a forte of business people. Integrating problematizing and problem-solving is necessary to create solutions that don't generate worse problems. Unfortunately, the current AI era reveals a disconnect between some industry experts and academics, as alluded to in our introductory skit. While polemic perspectives rarely help address problems, they can be engaging topics for conversation and content.

Some academics tend to dismiss the usefulness of generative AI, despite its widespread application across various industries, including academia, for increased productivity and real-world problem-solving. One reason for this disagreement may be the focus of some experts on how the technology works, while users concentrate more on the needs that AI products serve. The value of generative AI doesn't solely stem from the technology, which some experts downplay, but the wealth of high quality of data that’s available to generative AI systems, often freely, from people who’ve had their work appropriated by them. The tendency of generative AI products to decouple expertise and creativity from human knowledge and artistry is one of the major known problems with generative AI. However, there are known solutions that could usher in an era where people monetize their work through datasets for AI systems, or, in a bleaker alternate future, have their work taken without permission as they become obsolete.

In the realm of generative AI for artistry, hallucinations allow responses to deviate from their source material to create something new. By tracking the automated use of inputs, AI products for art can serve their purpose while addressing even more significant problems, such as the challenge many great artists face in making a living through their work. The intellectual property rights of artists were under threat long before ChatGPT and Midjourney. For example, some creators have found success by buying second-hand art and adding interesting elements, like a brand logo or celebrity photo, without facing the pushback that artists using generative AI receive. Integrating artists into the value chain of products relying on their data should be a no-brainer because it's both a business opportunity and an ethical imperative. And employing a business model that relies on appropriating art without the creators' consent is risky, as ongoing lawsuits have the potential to disrupt companies taking an "it's easier to ask for forgiveness than permission" approach. Grace Hopper's wise words don't apply when suing is a viable option.

Generative AI for Research

While generative AI holds creative potential valuable to the arts, its application in science and research poses challenges. Researchers require accurate data rather than phantom quotes and hallucinations from AI. Designers and developers can implement measures to address this issue, such as designing products to restrict responses to specific data sources, displaying citations, and facilitating users in verifying response accuracy through thoughtful features and clear communication.

AI systems, whether used for research or artistry, can compensate and credit individuals who have consented to the use of their work. While implementing equitable business models presents complexities in the arts, these issues are more straightforward in the context of research. Companies and institutions already invest billions in research, and generative AI can assist researchers across industries and academia in making better use of research reports, books, and interview transcripts. Furthermore, the authors of materials used for secondary research often make excellent interviewees. The integration of secondary research and expert interviews through AI is a unique aspect of Ferret, our AI chatbot that credits and compensates the individuals who contribute to it.

Expanding the Narrative

Generative AI has known problems that can be effectively addressed by integrating research, design, and tech ethics. The most discussed problems with general-purpose offerings like ChatGPT are often the easiest to tackle in more focused AI products. And while it may seem that generative AI exploits unwitting human contributors, that’s not the full story. As we work on developing Culture Capitalist into a resource for the tech community, alongside professionals from related industries and human science, we’ll highlight the work of people creating AI products designed to uplift and empower human contributors rather than erase them. We plan to publish follow-up reports and articles to assist innovators in developing more ethical business models. Additionally, by showcasing successful solutions to known problems, we aim to draw attention to issues that still lie in our blind spots—problems we genuinely cannot solve because they have yet to be discovered.

If you are building an AI product with an equitable business model or are interested in monetizing your expertise through interviews with people who value it, please use the button below to join our network or visit askferret.com.

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Artificial intelligence (AI) is transforming how researchers across industry and academia analyze data from primary as well as secondary research. With its ability to process large amounts of data quickly and efficiently, AI can provide valuable insights and save a lot of time and money. But, as with any technology, there are also risks and limitations associated with the use of AI. So let’s explore the benefits and challenges of using AI products like Ferret to analyze transcripts from primary research. Here’s a five-step checklist for using AI for primary research in ethical and responsible ways:

1. Determine if using AI is appropriate for your project: Researchers should consider if using AI is appropriate for their project, especially if they are 1) getting information from vulnerable populations or those who may be targeted for surveillance in some way and 2) collecting sensitive information from research participants. Data breaches happen all the time, so researchers should exercise discretion when deciding whether to use AI.

2. Obtain consent from research participants to use AI for analysis: Researchers should inform participants that they will be using AI technologies during the informed consent process and screening process. Prospective participants should be able to opt-in and provide consent before they begin answering research questionnaires. Offering an additional incentive for participants to allow the use of AI may be appropriate for some studies.

3. Anonymize and edit transcripts before analysis: Generally, researchers should remove any information that could identify research participants from transcripts, such as specific locations or names, and replace them with pseudonyms or alphanumeric codes. The code/pseudonym list, matching identities with codes, should be kept safe and private until no longer needed and then destroyed after the research has been completed. Researchers should review their transcripts to remove any more unnecessary information and, for more sensitive research topics, it is appropriate to provide edited transcripts to participants for their approval review before analysis with AI. In these cases, participants should be given a specific time frame to review and provide their feedback and/or confirmation. This expectation should be conveyed before the research, when requesting their express consent to conduct it.

4. Double-check the prompts you use: While there are protocols and levels of encryption to protect data, once it’s made available to large language models like GPT-4 via products like ChatGPT, the researchers and research participants are no longer in control of what happens to it. As with transcripts, the prompts researchers use to control AI systems may directly or indirectly convey sensitive data. AI products designed for industrial research, like Ferret, avoid using ingested data to train language models and are designed to be used with proprietary data. However, consumer tools like ChatGPT, will often use prompts as well as ingested data for training. If you are using general purpose, consumer AI products for less sensitive aspects of research like ideation you can usually disable training and chat history in the product’s settings. For products that are not designed to handle proprietary data, only provide information that would be OK to hypothetically share with the general public, such as through a blog.

5. Use AI as a tool, not a replacement for human expertise: While AI can provide useful insights and save time, it should not replace human analysis entirely. Researchers can use AI for general insights but also to point them to the areas of raw data that require more in-depth analysis. The most important part of any research project is protecting research participants by adding additional steps to existing protocols. Secondarily, best practices for research still apply. AI is not a means of circumventing human researchers. In fact, the new capabilities come with new ethical and methodological threats. As a result, experienced human researchers are more important than ever when integrating AI.

Remember to think holistically about the information you provide to tools like ChatGPT. While individual transcripts may be okay to share with OpenAI and other third parties after following this guide, a corpus of knowledge composed of several pieces of information can introduce new liabilities and threats, such as the potential for cross-referencing to circumvent the most basic safeguards. The threats range from potentially exposing research participants to unexpected risks to making the information that you are using for private analysis available to potential competitors.

While AI can seem high-risk and high-reward, it’s important to remember that technology has always shaped how researchers do their work and that avoiding digital technologies can also present significant risks. The benefits and risks of using AI in primary research comes with unprecedented advantages that are counterbalanced by less predictable risks. But whether AI is part of your research stack or not, it’s always best to err on the side of caution.

Expert Request: We are seeking input for the tech and research community for a study on how artificial intelligence is changing how industry and academia conduct research.

• Subject Matter Expert Questionnaire:

  • 5-8 minute duration, participants can opt-in to longer sessions.

  • Names of participants are included in the final report after confirmation of consent from participants

  • Participants can opt in to having their work promoted via a hyperlink alongside their name

  • Relevant areas of expertise include: artificial intelligence, tech ethics, research ethics research methodology, innovation, design, social/behavioral/human science.

Brief Screener Survey

Participants (i.e. co-creators) will be credited in the final publication after confirming that they consent to 1) being credited 2) having a quote from them included (if one is included in the final draft).

We are using Ferret, the AI chatbot for research and content creation, for this study.

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If you were to ask an American 50 years ago who they think of when they hear the term “culture”, there’s a pretty good chance Margaret Mead would come to mind. She was a famous anthropologist who spent her career helping people both within and beyond her discipline understand culture, her area of deep expertise. Her career as a public intellectual began at age 25 when her first book, Coming of Age in Samoa, became a best seller in 1928.

Today, culture is a term that people seem to use constantly. In business, “culture trumps strategy”. Political thought leaders and pundits often claim they are fighting a “culture war” and have lively debates about “cancel culture” and “woke culture”. And “Culture” is the best selling album by the Migos, edging out their sophomore album “Culture II.” It’s clearly a topic that people care about and want to understand, but it’s also one of those concepts that we rarely define. It’s like, culture is everything and everywhere while still meaning something unique to every cultural group. It’s more of a vibe than a concept.

To people who study culture professionally, cultural anthropologists, culture is what happens when a group of people spend enough time interacting with each other that they begin to share certain values, beliefs, behaviors, norms and practices. It is crucial to how human beings survive as well as make sense of the human experience. It can also be at the root of systemic problems and human suffering. While culture is a powerful unifying force that shapes the reality of cultural insiders, it’s often the source of conflict within and between groups.

Culture, a term often associated with traditions, rituals and customs, extends far beyond its conventional boundaries. From college football to the corporate world, culture tends to emerge as a topic of conversation. Anthropologists provide us with the methodological, theoretical, and ethical tools to understand culture. The best anthropology works result in learning how to see the world from the perspectives of people who have very different life experiences from our own.

Understanding the perspectives of multiple cultures also allows for better scientific analysis, including when studying politically charged topics like race and gender. By studying the various ways concepts like these show up in different cultures, we can gain an understanding of fundamental aspects of human experiences alongside insight into the, at times, profound differences across cultures, and the knowledge that the reality one person faces is not everyone’s. To anthropology, empathy is as much about producing useful scientific knowledge as it is about personal and professional ethics. And, because of the extremely personal methods that anthropologists sometime employ to do their jobs, ethics are always top-of-mind for most anthropologists.

In the current political landscape, discussions on gender have become more relevant than ever. Margaret Mead conducted groundbreaking research on how culture shapes various conceptualizations of gender. She revealed to a massive audience how anthropology, then in its infancy, can generate a scientific understanding of what it means to be human. Her work highlighted the variability of gender norms across different societies and paved the way for more accurate conversations about gender across academia and the public sphere. But while Mead relied on data to shape her opinions, her work was often politicized. Scientists today understand the topics of gender, biological sex, and gender identity much better than in the early days of anthropology. Yet these topics are more controversial among the general public than ever.

Mead became one of the most famous scientists of the 20th Century because she knew how to communicate about her work in ways that made it relevant and understandable to non-academics. We need more human scientists who can shape culture based on a scientific understanding of it. Culture Capitalist exists to help make that happen.

In that spirit, we’re giving away a copy of From the South Seas: Studies of Adolescence and Sex in Primitive Societies that Mead signed in 1946, while she was still early in her career. It’s an artifact from another era, when scientists like Albert Einstein, BF Skinner, and Margaret Mead shaped American public discourse based on their expertise. We’ll send the book to a random member of Ferret’s expert network who opts in to the giveaway next year. And because members can also make referrals, with higher referral commissions until early next year, we will give an additional entry each time they are mentioned as a referral during a sign-up. We expect this is our first rare book giveaway of many.

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Culture Capitalist, formerly Human Science, explores the intersection of culture and capitalism. We integrate human science expertise across industry and academia through Ferret, an AI platform we use to accelerate our research and content creation. 

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Researchers across academia and industry have access to more information than ever, but rarely have enough time to use it to its full potential. Traditional research methods are labor-intensive and fragmented, resulting in inefficiencies and high costs. And while AI products have the potential to accelerate and improve research, they are also creating a new problem by not crediting or compensating the humans whose work powers their databases.

Our solution is Ferret, an AI platform that:

• Accelerates analysis of diverse sources like books, articles, research reports, and datasets;

• Helps users discover the experts and authors who’ve contributed to its knowledge base; 

• And allows these experts to address users’ specific research needs through custom datasets.

Ferret brings experts into the AI value chain as stakeholders by ensuring they receive credit and compensation for their contributions, which tend to go for top dollar from corporate clients. 

By combining bespoke datasets with insights from books and reports, Ferret offers an efficient, integrated approach to research that becomes more invaluable with each use. Learn more at beta.askferret.com and subscribe to Culture Capitalist to stay in the loop.

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Human Science will discuss this topic and video in an upcoming Clubhouse room. Join the conversation at joinclubhouse.com/club/human-science and reach out to @husclub to learn more. Subscribe to Human Science to stay informed about additional audio and livestream events.

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Background: This Anthropological Life

I recently found myself talking about what I feel is the connection between art and science during an interview for This Anthro Life podcast. I was asked why I became an anthropologist and, because I expected the question, planned to talk about moving around as a child, facing nearly constant culture shock, and becoming interested in how people did things differently whenever I moved to a new place. But my interviewers approached this topic in an unexpected way and I found myself discussing art and science. I wanted to share some thoughts on this, especially in light of a Wenner-Gren Foundation project that I’m working on with a small group of anthropologists focused on public intellectualism.

I was an artsy child and attended high school at at Interlochen Arts Academy. Although I was a creative writing major, I was primarily interested in why art had meaning across media and disciplines, like visual art, music, and writing. So in college, I pursued a self designed a major called Semiology of Visual Culture. This concentration focused on understanding how symbolic communication worked, essentially trying to understand the social and behavioral science behind art and human expression. As a result of becoming more interested in how and why people communicated artistically than making art myself, I switched my career focus from writing, film, and advertising to cultural anthropology. However, I did this with the goal of eventually applying what I would learn to industry.

Now, my focus has shifted from the science of art to the art of science and public intellectualism. To be impactful beyond their disciplinary silos, scientists have to put as much thought into how they express themselves as artists. And, while academia generally doesn’t incentivize public engagement very much, the pandemic and social unrest of 2020 has brought many scientists and experts out of their silos. Society needs scientific expertise more than ever… and many experts are heeding the call.

Unfortunately, scientists and other experts often have difficulty influencing public opinion, even about their specific areas of expertise. Public discourse often stays at the basic level of arguing about whether insights from scientists and experts are even worth discussing. So, even as humans face the dramatic consequences of climate change, a global pandemic, and systemic racism, the general public is often debating their very existence. While there is intellectual diversity among the experts addressing these issues, most people in society don’t bother themselves with them. But the problems academics find and discuss, through problematizing, have real world implications that business and political leaders encounter and do their best solve everyday. This is illustrated through the rise of environmental, social, and governance (ESG) concerns alongside the growth of anthropology in industry.

Scientists often collaborate with experts outside of their silos in ways that are white-labeled… hidden in plain sight with the contribution of the sciences, including behavioral and social science, remaining unacknowledged to the public. And the COVID-19 Pandemic has revealed how the impact of experts on public discourse has declined in the social media era. I think a crucial part of how experts (i.e. public intellectuals) can make their comeback lies in them leveraging emerging media formats, like drop-in audio, and learning how to express their understanding of the world to audiences outside of their silos more artfully.

The following essay is available through audio narration in the video above.

Influence & Expertise

For any topic that’s being discussed by the general public, I think about the most notable people talking about it as falling into one of two general categories: influencers and subject matter experts.

But there are also two intermediate categories. One is thought leaders, which is a segment that has become increasingly prominent over the last decade with the help of platforms like TED talks and the emergence of thought leadership marketing in industry. Thought leaders are influencers, but they can speak with authority about issues because of their knowledge about them. A fourth category, public intellectuals, receives much less attention from the public nowadays. They are experts who are able to engage an audience outside of their professional communities, which generate the knowledge they’re sharing.

Influencers

Influencers often come from the arts and entertainment, but in many ways we all fall into this category on some level in the age of social media. Social platforms allow us to express ourselves and influence each other in new ways. Influencers discuss what’s happening in the world and, in doing so, spread awareness about issues and current events. But they also shape people’s opinions, not so much because of what they know but who they are, the size of their following, and how well they express themselves. Technological change now allows us to be influenced by more sorts of people, more of the time, in more ways than ever. Influencers bring to bear their ability to express themselves in order to create content that engages a public audience. And they’re usually effective at leveraging short-form and image-based social platforms, which have grown in popularity over the last decade.

Subject Matter Experts

On the other side, there are subject matter experts. They are able to impact society, but it’s because of what they know instead of who they are. They tend to work behind the scenes and seldom impact public discourse very noticeably. Subject matter experts are members of communities that generate new knowledge. This means that they don’t just have knowledge about their topics, but they also have the skills required to help produce it.

Thought Leaders

Thought leaders create content and share ideas like influencers, but it’s based on some authority they have when it comes to a certain topic. They don’t have to be subject matter experts, but they often synthesize or explain the ideas of experts in ways that are more assessable to broader audiences. Their focus is creating content and proposing intriguing ideas based on their insights and this often incorporates the work of experts in relevant areas. But what they share is also going to be shaped by their personal biases.

Thought leaders tend to come from journalism and business, domains that often translate theories and concepts from the arts and sciences for broader audiences in the general public and business.

Public Intellectuals

Although they're often associated with academia, public intellectuals can come from anywhere. They explore their areas of expertise critically, as experts, but also engage broader audiences. Thought leaders are oriented towards creating interesting ideas that tend to be associated with them as individuals. While they’re often evangelizing them as new inventions, public intellectuals are more oriented towards an analytical understanding of issues and are more likely to evangelize their domains of expertise.

Expression & Understanding

People often move across these categories throughout different career stages. As examples, let’s talk about two well known people in science, Bill Nye and Michio Kaku. Although they have similarities, they have very different backgrounds and skillsets. Bill Nye has a bachelors degree in mechanical engineering, but throughout his career he’s been focused on writing and entertaining people by explaining scientific ideas. Early in his career, he created content for children as a TV personality. But over the years his influence has grown alongside his understanding of the topics he discusses and the breadth of his audience. Now he’s a thought leader on topics like climate change because of the credibility he’s gained over decades as a science guy… even though he isn’t a scientist.

Dr. Kaku as a high school student with a small particle accelerator... that he built

Michio Kaku, on the other hand, is a theoretical physicist. He has deep scientific expertise and as his career has evolved, he’s become more effective at expressing his ideas as a writer and orator to the point of becoming a public intellectual. He can contribute to the actual work of scientists in ways that Bill Nye can’t. But both of them can contribute to the public’s understanding of science through their content and, in the area of science communication, they both have significant expertise. So if an organization wanted advice on how to impact society’s scientific literacy, they’d both have meaningful expertise to offer. And there are people who eat, breathe, and sleep science communication who have even more expertise in this area and work behind the scenes in academia and entertainment.

Nye and Kaku demonstrate how creative expression can elevate the work of experts, and this is something that we can see by looking at other prominent figures from the arts and sciences.

Albert Einstein is perhaps the most influential public intellectual of the 20th century because of his work as a scientist, but he would play piano and violin as he thought through his theories.

Live Aid in 1985

And David Bowie isn’t the only legendary rock star who’s associated with stardust, because Brian May — the guitarist for Queen who wrote hits like We Will Rock You — is also an astrophysicist who did his doctoral research on zodiacal dust. And two of the most culturally impactful filmmakers of the 20th century, Orson Welles and George Lucas, share a reverence for cultural anthropology. It makes sense that a discipline focused on understanding the human experience might inspire artists who portray it, even when their characters aren’t human.

I began to fully appreciate the difference and interconnectedness of art and science after a conversation with a guitarist a few years ago.

Train of Thought

In the summer of 2016, I was teaching cultural anthropology to high school students in Harlem. I had to take the subway to a meeting across town, which took between one and a half and two hours each way. It was a hot day and I had just started a very demanding job in Big Tech. The pay for the teaching job was negligible and I didn’t understand why this administrative meeting had to be in-person. I was in a bad mood and began to question my decision to take on this additional job that was more difficult and stressful than the one I was using to pay my bills. As I waited for the train, a musician who looked to be in his 50s came up to me and started asking me questions about my educational background and what I did as an anthropologist. Once our train arrived, we sat together and started talking about our lives. We learned that we had lived in some of the same areas of the US along the Gulf Coast, in Texas, Mississippi, and the Florida Panhandle. Then he asked me a question I didn’t expect.

“How do you deal with negativity?”

I asked him to clarify what he meant and he began talking about his insecurity about not having much formal education, making his living by busking, and wondering what educated, affluent, and white people thought when they saw him… until he starts playing his guitar and all his insecurities fade away. As he spoke, I began to realize this was his artful way of asking me how I, as someone who could relate to some of his experiences as a black man in America, dealt with racism.

This was something I’d never thought about before, so I began trying to explain.

I guess I always tried to understand why… what was going on…

And then he excitedly said

“Analyzing!”

He remarked on how interesting it was that he coped with his hardships by expressing himself as an artist but I dealt with mine by trying to understand them through science. And we talked about my students, who were constantly reminded of the privileges and resources they didn’t have while they dealt with an increasingly uncertain world as Black Lives Matter entered the public consciousness and Trump emerged as a political leader. Once we talked through his insight as co-theorists, but with him taking the lead, he said

And that’s what you’re doing for those kids.

Shortly after, we thanked each other for a great conversation as we arrived at his stop. As he stepped off the train, he said

Thanks for talking with me. I learned a lot from you.

For the rest of the train ride, I reflected on what is to this day is the best interview I’ve ever seen, and it was conducted by an artist who turned me into the anthropological subject.

This 20-minute conversation helped me realize:

1. I’m an anthropologist because it’s in my nature to cope with my existence by trying to understand it better.

2. This compelled me to teach anthropology to students of color in high school. I wanted them to be able to make some sense of a society that was getting crazier by the day.

3. And people try to make sense of the human experience by expressing it as artists as well as understanding it as scientists.

In the span of our short conversation, an artist who didn’t know what anthropologists did before striking up a conversation with me revealed himself to be one of the most talented I’d ever seen. And I reflected on this as I continued uptown with a new sense of purpose.

So expression and understanding have a natural tendency to reinforce each other and, as a result, great artists and scientists tend to be one and the same. And although there are clear differences between thought leaders and public intellectuals, the best of both find ways to work at the intersection of art and science — expression and understanding.

Dunning-Kruger & Epistemic Trespassing

It’s no surprise that as 2020 brought a global pandemic alongside widespread social unrest, experts and public intellectuals have tried to raise their voices to help make sense of the world.

Among the most culturally relevant nowadays are psychologists David Dunning and Justin Kruger, known for discovering the Dunning-Kruger Effect. Although they offer fairly clear explanations for what it is as scientists, my favorite comes from John Cleese, an artist.

The Dunning-Kruger effect can explain a lot about the human condition right now, such as why people who lack an understanding of virology or epidemiology confidently disagree with experts like Anthony Fauci about COVID-19 or propose ways to treat it that have no scientific bases. And the related concept of epistemic trespassing, explored by Nathan Ballantyne, explains how some people erroneously believe their expertise in one domain qualifies them as experts in another area.

In fact, epistemic trespassing can be used to great effect for a person’s career. For example, William Happer is a scientist who has deep expertise in adaptive optics, a technology that allows ground-based telescopes to correct for atmospheric turbulence and dramatically improves the quality of images captured by them. He doesn’t think carbon dioxide emissions cause climate change but he also isn’t a climate scientist. However, because he has earned credibility and credentials from his work on optics and pushes a narrative that climate change deniers want validated, he was appointed to leadership roles related to climate science in the US government in 2018 and 2019.

This is important to understand when you see something like a debate between William Happer and Bill Nye about climate change. They are two people who have impacted public discourse related to the topic a great deal, despite neither one of them actually being subject matter experts on the science of it. And they’ve shaped public opinion about climate change more than any climate scientist has. They’re both thought leaders, with Nye promoting a narrative informed by scientific consensus and Happer’s based on his work, which doesn’t explore the interconnected systems that have to be understood to have a valid scientific perspective about climate change. The most qualified scientists often don’t communicate in ways that engage public audiences. And the voices that traditional media elevate tend to be better for entertainment than providing the public with an understanding of issues.

So what about social media, which can keep traditional gatekeepers at bay.

The Role of Social Media

Unfortunately, the far reaching adoption of social media amplifies the societal implications of the Dunning-Kruger Effect and epistemic trespassing. It can allow influential people to spread bad, even dangerous ideas more easily. Public intellectuals can leverage social media effectively for their careers and some use it to impact public discourse in meaningful ways, but this can lead them to align themselves with polemic perspectives and echo chambers online. Although influencers and thought leaders could appear to be better at using social media to shape public discourse, their success largely stems from how social platforms are designed. They often don’t convey nuance very well to large audiences, and this is where experts add the most value to discussions. The Devil is in the details, but learning about them takes time that many people don’t feel like they have when they’re on short form or image based social media. The nuances about the world the way it actually is have to be communicated thoughtfully and artfully to be meaningful to audiences outside of the expert communities studying them. And public intellectuals need to leverage technological tools that allow them to provide insights about issues that aren’t buried in discussion threads that often get seen by only the most intellectually curious people. And while public audiences may be somewhat inclined to defer to experts when it comes to issues that are inherently political and technical, like foreign policy, when it comes to more fundamental human science, public intellectuals lack the influence of thought leaders on social media designed to prioritize entertainment over informational value.

Take the topic of race for example, which is one of the most studied in the social, behavioral, and biological sciences. A lot is understood now and there are comprehensive resources available to help the public understand this topic holistically. But when Brett Stephens, a journalist, writes about race in a way that conveys a misunderstanding of it, his ideas are more likely to go viral as they’re enthusiastically received and spread by people who share his biases while another group helps the article trend on social media by discussing the problems with it. Short-form and image based formats in particular allow simplistic narratives and disinformation to get traction over those focused on providing the details necessary for a better understanding of the world.

The Medium is Still the Message

The way discussions take place makes a big difference and some formats are better suited to public intellectuals and subject matter experts than others. For example: take the topic of artificial intelligence. The content conveyed through a TED talk about AI would be very different from a subject matter expert interview about AI. A TED talk would convey intriguing ideas about it that likely wouldn’t require deep expertise to develop or understand. On the other hand, organizations often conduct expert interviews to help understand AI problems or develop AI offerings, but most people in the general public would not want to sit through one. There’s a big difference between helping people understand something to address real-world issues and creating interesting content about it. And some thought leaders are great at inspiring new thinking in areas where they don’t have particularly deep expertise.

TED talks use a format with a thought leader or public intellectual on stage giving their perspective through a monologue. There’s usually no space for criticism from the audience, domain experts challenging ideas broached by the speech, or proposing improvements. However, it is possible to create space for public intellectualism, which benefits from an exchange of ideas between experts.

Over the past 15 years or so, people have been able to use technologies developed by scientists to convince each other to distrust scientists. So, even the most basic scientific facts, like the Earth being round and vaccines being good, are falling out of favor in some circles. And this can seem like an existential threat of social media, exacerbated by automation, through bots and algorithms. Human society desperately needs the revenge of the nerds.

A New Era of Public Intellectualism

Recently, a tweet from MC Hammer about philosophy and science went viral. People were intrigued by his perspective and depth of knowledge about these topics. And I saw this tweet around the time I stumbled into a room on Clubhouse, where he was discussing science and technology development with a room full of experts from different disciplines and industries. Clubhouse is a drop-in audio social media platform, kind of like a live podcast, that has become a bit of a cultural phenomenon. In fact, a cyborg anthropologist named Marcus Anderson often hosts conversations with Hammer on the platform.

Clubhouse differs from other forms of social media because it combines the sheer volume of information that can be shared through audio alongside a design that allows for many participants to contribute to conversations. As a result, it’s appears to be better suited for diverse experts discussing topics in a way that provides the nuance often lost or buried within short-form social media.

This paradigm shift may allow public intellectuals to start shaping public discourse more effectively, but it won’t be easy. It’ll mean embracing new formats like drop-in audio and perhaps even leaving academia if it fails to evolve. No matter the specific discipline, scientists must learn how to express themselves to the general public effectively to be relevant to society. So while it’s important to understand how artists like Hammer learn from and contribute to the work of scientists, it’s imperative that scientists learn from artists and express themselves more artfully.

STS is Science & Technology Studies

I’ve encountered this issue in my own work.

As a grad student from 2007-2010, I studied how a Big Science project picked a site for a telescope that, years after the decision was made, became a massive global controversy. It has huge implications for Hawaiian society and the scientific community, but it’s difficult to talk about as a social scientist because most people in the general public aren’t necessarily interested in hearing about the systemic causes of the controversy. Reductive, often false narratives about Native Hawaiians and scientists tend to get the most traction on short-form social media.

I’m also working on a training program, with backing from the Wenner-Gren Foundation, called Anthropologists on the Public Stage. We’re helping experts in anthropology and related fields learn how to shape public discourse more effectively and will launch our program over the summer.

And lastly, I’m producing content like this alongside online events though an initiative called Human Science. So please like and subscribe if this is the kind of thing you want more of. This is my first essay like this, so they’ll definitely get better.

My next one will be about the difference between thought leaders and public intellectuals.

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Thanks to Marcia Bartusiak and Mike Wesch for inspiring this essay and the participants of Anthropology on the Public Stage so far: Alfredo Artiles, Astrid Countee, Bob Mierendorf, Bob Morais, Charlie Lehman, Chip Colwell, Cool Anthropology, Dennis Dauble, Dori Tunstall, Ed Liebow, Genevieve Bell, Gillian Tett, Helen Fisher, Jeff Martin, Jim Forrer, Melissa Fisher, Mike Wesch, Paul Durrenberger & Suzan Erem, Paul Stoller, Rebecca Chamberlain-Creanga, Suanna Selby Crowley, Tim Malefyt, and Yolanda Moses.