Crime in the Digital Age: An Anthropological Autopsy of What Was Lost Between Giants and Startups

The crime scene: when everyone wins and the user keeps complaining

The call came in first thing in the morning: “We’ve got another case. All the innovation indicators are up, NPS looks fine, funding rounds are successful… and yet people keep reporting frustration, distrust, and digital fatigue.”

As an anthropologist, I get invited more and more to strange crime scenes: banks and neobanks with brilliant apps that fail to build lasting trust; hospitals and healthtechs full of data, but with patients still lost in physical or virtual corridors; retailers and marketplaces selling more than ever while customers feel like they’re shopping on autopilot.

This isn’t a classic murder. No one has killed anyone. But there is a clear victim: the value that should have been created for people.

On the digital coroner’s table, there are two recurring fingerprints:

• That of the incumbent: heavy, regulated, obsessed with efficiency.
• That of the startup: agile, externally funded, obsessed with growth.

Both claim the same thing: “we put the user at the center.” Yet what I find in the autopsy of each sector is different: the user appears, at most, on the perimeter. At the center, what we actually have are tribal rituals of internal legitimization: metrics, funding rounds, compliance, roadmaps.

This report is the forensic record of that discrepancy.

1. Where the wound starts: from physical assets to the prestige of moving fast

Before digitalization, the tribal code was clear. In almost every sector, competitive advantage was measured by:

• How much physical infrastructure you controlled (bank branches, logistics networks, stores, campuses, hospitals).
• The scale of your distribution network.
• The strength of your assets on the balance sheet and your ability to withstand crises.

The traditional company lived off executing what was already known and defending its positions. Internal prestige resided in keeping the machine stable.

With digitalization and cloud technologies, the equation changed silently but radically. Tangible assets yielded some of their protagonism to:

• Ability to iterate fast: short test‑and‑learn cycles, frequent releases, a “fail fast” culture.
• Intensive use of data to personalize decisions, automate processes, and fine‑tune pricing.
• Experience design that reduces friction and increases conversion.

Startups were born inside this new framework: lean structures, external growth‑hungry funding, and an almost religious mandate toward disruption. They became the new heroic tribes of progress.

Meanwhile, incumbents tried to incorporate this new code without giving up the old one. The result: two‑headed organizations, where the rhetoric of agility coexists with legacy systems, rigid hierarchies, and fear of failure.

From the outside, it looks like a familiar story: slow vs. fast, heavy vs. light. On the crime scene, however, I see something else: two tribes optimizing for different metrics and leaving an empty space between them.

That space is where stable, understandable, human value for people should live.

2. The invisible conflict: when the user is an alibi, not the protagonist

On paper, both incumbents and startups seek to “create value for the customer.” But their daily rites tell another story.

• In the incumbent, the foundational myth is stability: protecting the balance sheet, complying with regulation, not “breaking” the system. Tribal prestige indicators include budget size, number of people managed, and absence of regulatory shocks.
• In the startup, the myth is exponential growth: capturing market share, raising successive rounds, scaling to new countries. Prestige resides in speed, media presence, and hitting the next milestone before the cash runs out.

The user appears in both narratives, but if you watch the rituals —committees, stand‑ups, investor pitches, portfolio reviews— the central figure is not the person using the service, but the KPI that legitimizes the internal tribe.

The crime, then, isn’t that one “wins” over the other. The crime is their convergence toward the same omission:

• The user’s quality of life, real understanding of the service, and autonomy are rarely core KPIs.
• The long‑term relationship is sacrificed to quarterly goals (incumbents) or next‑round goals (startups).

Let’s see how this void shows up, sector by sector.

3. Fintech/Financial services: the promise of simplicity and the trail of anxiety

3.1 Business models: fees, interest, and the fiction of “free”

In traditional banking, the scheme is still recognizable:

• Main revenues: interest margins, service fees (accounts, cards, transfers), investment and insurance products.
• Costs: branch network, staff, legacy systems, heavy regulatory compliance.

The promised value is security, trust, and a broad product offering.

Neobanks and fintechs operate under a different story:

• Accounts with no visible fees, “free” cards, flawless mobile experience.
• Revenues through interchange fees, premium subscriptions, investment products, or BaaS (banking as a service) embedded in third‑party platforms.
• Light cost structures, mostly cloud‑based, no physical network.

Anthropologically, what changes is the psychological contract: the solemnity of the bank‑as‑temple is replaced by the app‑as‑companion. But the economic information asymmetry remains. The average user understands traditional bank fees better than the revenue model of a “free” fintech. Anxiety doesn’t disappear; it just changes shape.

3.2 Technology: mainframes vs. microservices

The technological fingerprints are clear:

• Incumbents: mainframes, monolithic applications, long release cycles, costly testing, strong dependency on long‑term vendors.
• Startups: cloud‑native architectures, open APIs, microservices, DevOps, CI/CD, frequent A/B tests.

This gives fintechs a much higher iteration speed and better data use for risk scoring and personalization. Yet the crime doesn’t lie in the technology, but in its social use: what is this capability mainly used for?

In today’s scene, advanced analytics focuses on maximizing engagement and cross‑selling, not necessarily on reinforcing the user’s financial health.

3.3 User experience: flawless onboarding, opaque decisions

Fintechs excel at:

• 100% digital onboarding, remote ID verification, instant virtual cards.
• Clear interfaces, expense categorization, real‑time notifications.

Traditional banks, though improving, still drag along in‑person processes, physical paperwork, and fragmented journeys.

However, when you look at user testimonies, a pattern emerges:

• Higher satisfaction with usability in fintech.
• Persistence or even increase in insecurity around financial decisions: investment, debt, planning.

The crime scene here: operational friction has dropped, but the void of financial education and real support has not been filled.

4. Retail/E‑commerce: an abundance of choice, a scarcity of meaning

4.1 Business models: margin per square meter vs. commission per transaction

Traditional retailers live off the margin on products sold in physical stores:

• Costs: rent, store staff, own logistics, POS systems.
• Varying vertical integration (from full supply‑chain control to heavy supplier dependence).

Marketplaces and digital‑native e‑commerce draw their power from:

• Commissions from third‑party sellers, logistics services, brand advertising, subscriptions (e.g. “prime”‑like programs).
• Costs: cloud infrastructure, automated warehouses, large investments in tech and large‑scale logistics.

Here, the startup doesn’t sell “less friction” so much as infinite choice. But consumer anthropology reveals another kind of fatigue: buying without context, nudged by algorithmic recommendations.

4.2 Technology: rigid systems vs. permanent lab

• Traditional retail: inventory and POS systems that are hard to interconnect, limited data analytics, little online experimentation.
• Digital‑native e‑commerce: cloud platforms, advanced analytics, behavioral tracking to the click, systematic A/B testing on prices, layouts, and promotions.

Technology turns the marketplace into a huge lab of human behavior. Yet the experiment isn’t designed to improve users’ lives, but to optimize conversion and basket size.

4.3 User experience: human contact vs. anonymous efficiency

• Physical store: more sensory experience, the possibility of human advice, but constrained by hours, travel, queues.
• E‑commerce: convenience, fast delivery, easy returns, but an impersonal experience mediated by filters and reviews.

In the field, I see users oscillating between both ecosystems according to their emotional needs: efficient online purchases, searches for meaning and relationship in neighborhood shops. The crime here is subtle: in the race toward omnichannel, no one takes responsibility for the total cognitive load people bear in managing so much abundance.

5. Health/Healthtech: data everywhere, emotional relief nowhere

5.1 Business models: medical acts vs. intermediation platforms

• Hospitals and insurers: revenue from medical acts, stays, procedures, premiums; high cost structures, medical staff, equipment, facilities.
• Healthtechs: platform models connecting patients and professionals (appointments, telemedicine), revenue from commissions, subscriptions, software licenses for clinics.

The startup adds a digital intermediation layer to a highly regulated sector. It delivers efficiency and access but rarely controls the physical environment where the patient will ultimately be treated.

5.2 Technology: captive medical records vs. fragmented app data

• Incumbents: electronic health record systems, often closed to one another, hard to update, with heavy emphasis on security and compliance.
• Healthtechs: mobile apps, cloud platforms, telemedicine tools, analytics.

Result: more data than ever, but scattered across hospital silos and personal apps. No single actor holds an integrated, understandable picture for the person.

5.3 User experience: physical corridors and digital corridors

• Traditional sector: long waits, bureaucracy, little transparent communication, technical language.
• Startups: agile appointment booking, reminders, video calls, remote monitoring.

It would seem healthtechs “win” on experience. Yet when the case is serious, the person still faces a hospital whose internal logic hasn’t changed: the app takes them to the door, but the critical emotional experience remains governed by the old hospital tribe.

The crime: we’ve increased access efficiency, not necessarily the dignity or understanding of the care process.

6. Mobility/Logistics: optimized routes, precarious relationships

6.1 Business models: heavy assets vs. intermediation platforms

• Traditional logistics: companies with their own fleets or long‑term contracts, revenue from transport and warehousing services, high investment in physical assets.
• Mobility/logistics startups: platforms that connect demand and supply (e.g., shippers and carriers), charging commissions, dynamic pricing, value‑added services.

Economically, the startup reduces owned assets and becomes manager of a swarm of operators.

6.2 Technology: closed systems vs. real‑time orchestration

• Incumbents: fleet management systems, route planning tools often rigid, limited integration with customer systems.
• Startups: intensive use of IoT, geolocation, route optimization algorithms, real‑time customer dashboards, APIs to integrate with other systems.

The social consequence: greater transparency for the customer and greater pressure on the driver, monitored and adjusted by the second.

6.3 User experience: total visibility, minimal empathy

• End customer: real‑time tracking, tighter delivery windows, less uncertainty.
• Operator (driver, courier): more algorithmic control, less decision space, growing dependence on a platform that can change conditions unilaterally.

The crime here isn’t technological but relational: value for the customer is bought at the cost of perceived degradation of provider conditions. It’s an imbalance rarely exposed in innovation narratives.

7. Education/Edtech: courses without campus, diplomas without tribe

7.1 Business models: recurring tuition vs. modular subscriptions

• Traditional education: universities and schools funded by tuition, fees, subsidies; cost‑intensive in teaching staff, physical campus, infrastructure.
• Edtechs: online course platforms, remote tutoring, modular content; revenue from subscriptions, pay‑per‑course, B2B licenses for companies or institutions.

The startup’s promise is democratized access: more options, lower marginal cost per student, no need for physical presence.

7.2 Technology: virtual campus as add‑on vs. platform as core

• Incumbents: virtual campuses that replicate in‑person classes, with limited data use and personalization.
• Startups: scalable cloud platforms, progress analytics, content recommenders, automated assessments.

The edtech system captures detailed metrics on attention, progress, and dropout, but who are those data really serving?

7.3 User experience: solid credentials, absent community

• Traditional institutions: physical community, rites of passage (exams, graduation), alumni networks.
• Edtech: flexibility, self‑paced learning, up‑to‑date content, but a solitary experience with weak ties among students.

Anthropologically, education is not just knowledge transmission, but identity and tribe building. The crime here is turning education into content consumption, with little care for the social fabric that sustains deep learning.

8. Cross‑sector forensic report: recurring patterns

Aligning the sector findings reveals clear patterns in how startups challenge industries and how incumbents respond.

8.1 Startup challenge strategies

• Price and cost structure: light models, no heavy physical assets, fewer permanent staff; ability to offer lower prices or freemium.
• Convenience: mobile access, 24/7, instant onboarding, simplified processes.
• Neglected niches: focus on specific segments (young people with no credit history, chronic patients, self‑taught learners).
• Innovative revenue models: subscriptions, pay‑per‑use, third‑party commissions, B2B2C models.
• Superior experience: polished interfaces, coherent journeys, data‑driven personalization.

8.2 Typical incumbent responses

• Internal innovation: agile teams, intrapreneurship programs, cultural transformation with mixed results.
• Innovation labs: semi‑independent spaces for experimenting, often disconnected from the core.
• Corporate venture capital (CVC): investment in sector startups to learn, monitor competitors, or enable future acquisitions.
• Acquisitions: buying startups to accelerate digital capabilities, with the risk of “suffocating” them inside the legacy structure.
• Alliances: mutual distribution agreements, API integrations, white‑label services.

8.3 Scorecard: strengths and weaknesses by tribe

The table only tells part of the story. It doesn’t capture the shared void: the lack of metrics that track relational quality and the user’s real understanding.

9. Obstacles, walls, and regulatory traps

9.1 Incumbent barriers

• Tech legacy: mainframes and monolithic systems that make any change slow and expensive.
• Organizational culture: hierarchical structures, risk aversion, complex approval chains.
• Regulation: strict requirements (especially in finance and health) that limit experimentation but also protect against unserious competitors.
• Internal incentives: bonuses tied to stability and compliance, not to effective innovation.

9.2 Startup barriers

• Access to capital: constant need for funding, dependence on investors with often short horizons.
• Scaling: moving from niche to mass without losing quality or focus.
• Customer acquisition: high marketing and sales costs, saturation of digital channels.
• Third‑party dependence: cloud infrastructure, distribution platforms (app stores, marketplaces), regulators.
• Regulatory risk: rule changes that can invalidate the business model, especially in fintech and healthtech.

9.3 Regulation as shield and sword

In finance and health, regulation acts as both barrier to entry and safety net:

• It protects incumbents, who already invested in compliance, against improvised competitors.
• It forces startups to specialize, often as service layers on top of existing regulated infrastructure.

At the same time, regulatory changes —open banking APIs, interoperable health record frameworks— can benefit startups by weakening traditional silos.

The crime scene here is paradoxical: incumbents and startups use regulation rhetorically as a weapon as needed —sometimes as a shield to slow others’ innovation, other times as a banner demanding “level playing fields”— but rarely as a tool to redirect real value to citizens.

10. The convergence timeline: from antagonists to accomplices

Reconstructing the scene over time, we don’t see replacement so much as convergence.

Current convergence suggests a future less about “winners vs. losers” and more about specific capability combinations.

But while we argue over who will lead the ecosystem, the victim remains unanswered: who takes responsibility for people’s digital wellbeing?

11. Strategic turning point: change KPIs or keep dressing up the autopsy

Anthropologically, what defines a tribe is not only its technology, but what it devotes its internal prestige to.

Today, core rites are:

• For incumbents: flawless compliance, operational efficiency, stable financial results.
• For startups: user growth, geographic expansion, rising valuations.

If we want the user not to show up as collateral damage in the next crime scene, we need code changes, not just new “features.”

11.1 Strategic shifts for incumbents

1. Redefine success beyond the quarter

   • Introduce metrics that capture user stability and understanding: responsible indebtedness, healthy service use, sustained satisfaction.
   • Link part of executive bonuses to these indicators.

2. Modernize the stack without discarding useful legacy

   • Decoupling strategies: surround the legacy core with API layers to innovate at the edges without breaking the system.
   • Gradual migrations to cloud architectures, prioritizing areas with direct experience impact.

3. From “innovation labs” to “embedded innovation”

   • Integrate agile capabilities in core business units, not only in isolated hubs.
   • Build mixed teams where tech, business, and user experience make decisions together.

4. Leverage their own tribal assets

   • Use brand and customer base to introduce genuinely useful services (financial literacy, preventive health, lifelong learning), not just more products.
   • Turn the regulatory relationship into a competitive edge: be the best at translating regulatory complexity into simplicity for citizens.

11.2 Strategic shifts for startups

1. Design complementary, not just substitutive, offerings

   • Identify where incumbents add value that’s hard to replicate (license, risk absorption, physical network) and build on it instead of copying the entire model.
   • Choose B2B2C strategies: be the experience or data‑intelligence layer on top of existing infrastructure.

2. Seek revenue models aligned with user wellbeing

   • Avoid relying solely on “engagement” metrics; explore schemes that tie economic success to real outcomes (health, learning, financial stability).
   • Be transparent about how you make money; users who understand the model trust more.

3. Build regulatory reputation from day one

   • See regulation as rules of the game, not just as a hurdle; engage early with supervisors.
   • Stand out from opportunistic competitors through responsible practices in data, risk, and treatment of providers.

4. Care for the internal human fabric

   • Resist the cult of hyper‑speed: sustainable work rhythms reduce errors and improve product quality.
   • Establish rituals that reward course‑correction based on user feedback, not just numerical growth.

12. The big picture: who are we innovating for, exactly?

Walk into a bank HQ, an e‑commerce hub, a hospital, a warehouse, or a campus, and you’ll find the same thing: people who genuinely believe they are innovating in the right direction.

The problem isn’t a lack of talent or effort. It’s that dominant tribal myths —growth, efficiency, disruption— have left little room for a basic question that is rarely asked explicitly:

What would success look like if we looked at it mainly from people’s everyday lives, instead of from our metrics dashboard?

In the comparative autopsy of incumbents and startups, the most striking thing is not how they differ, but what they unwittingly share: a partial blindness to the relational, emotional, and long‑term impact of their services.

5–10 year trends —platforms, “as a service,” generative AI, superapps— won’t make this void disappear. If anything, they may amplify it unless we redefine the cultural code.

12.1 Convergence trends reshaping the scene

• Platformization and ecosystem economies: conglomerates where incumbents provide license and brand, startups provide experience and data, and big tech supplies infrastructure. Roles blur, but the user may be pushed even further from the center of decision‑making.
• “As a service” everything: banking, insurance, health, education as a service embedded in other platforms —the bank account inside your messaging app, training inside your work tool. More convenience, higher opacity risk.
• Hyperpersonalization via generative AI: financial recommendations, treatments, learning paths, and logistics routes individualized. Without strong ethical and regulatory debates, personalization can easily become manipulation.
• Superapps vs. ultra‑specialized solutions: concentration of services in a few dominant platforms vs. a mosaic of niche apps. In both cases, users accumulate invisible dependencies.

12.2 Three possible scenarios

1. Ecosystems ruled by big tech

   • Tech giants integrate financial, health, education, and retail services into global superapps.
   • Incumbents become “invisible providers” of infrastructure; startups become feature labs.
   • User value depends on how closely a single powerful tribe aligns with collective interests.

2. Collaborative incumbent‑startup coexistence

   • Stable B2B2C agreements; shared data standards and more homogeneous regulation.
   • Each actor specializes: incumbents in resilience and risk management, startups in experience and niches.
   • Risk of complacency: convergence that reproduces the same vices with new façades.

3. Consolidation via M&A and a renewed oligopoly

   • Successful startups get absorbed; a handful of hybrid giants remain in each sector.
   • Less diversity in offerings, more homogenous experiences, higher entry barriers.
   • Depending on regulation, this could either stabilize the system or smother genuine innovation.

In all scenarios, the key isn’t technological but cultural: which tribes set the rules, and with what values.

13. Anthropologist’s epilogue: changing the cause of death

Every forensic report ends with an uncomfortable line: “cause of death.” In this case, if user value lies bruised on the table, it’s because of:

Cause of death: shared systemic negligence; excessive attention to internal metrics; lack of explicit responsibility for citizens’ wellbeing in the digital era.

Neither incumbents nor startups are villains. They’re tribes responding to inherited incentives and myths. Real change depends on rewriting those myths:

• From “being the biggest or fastest‑growing” to being the one that most reduces people’s anxiety, confusion, and wasted time.
• From “leveraging data” to being accountable for what it’s used for.
• From “user experience” as interface polish to human experience as the primary design criterion.

Next time we walk into a boardroom to talk about innovation, maybe the question shouldn’t be “what is our competition doing” or “which business model scales better,” but a much more uncomfortable and useful one:

If an anthropologist had to conduct an autopsy of our service ten years from now, would they conclude that we truly improved people’s lives… or that we merely learned to move the numbers within our own tribe?

The answer to that question, more than any technology roadmap, will determine who deserves to lead the next decade.

References

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3. Innovacionindustrial.net. “Gestión de la innovación en startups vs. empresas consolidadas: diferencias clave” (accessed 2026).
4. AJE (American Journal Experts). “Scope and delimitations in research” (accessed 2026).
5. Wikipedia. “Marco teórico” (Spanish version, accessed 2026).
6. Research context provided by the user on incumbents vs. startups in fintech, retail, health, mobility, and education.