When No One Wins Anymore: A Philosopher Looks at the Exhausting Stalemate Between Giants and Startups

1. The final scene: everyone optimized, no one happy (The Hook)

Picture the year 2032.

Your bank replies in seconds, but you can’t remember the last time you spoke to a human who could make an exception.
Your purchases arrive in hours, but the city is saturated with vans, warehouses, and exhausted riders.
Your doctor sees you on a video call in five minutes, but your medical history is still fragmented across systems that don’t talk to each other.
You always arrive on time because an app calculates your perfect route, even though the city you cross has been redesigned more for algorithms than for people.

Everything works. Nothing matters very much.

There are no major tech collapses: banks don’t sink, commerce doesn’t disappear, hospitals stay open, buses run, universities keep awarding degrees. The failure is subtler: after decades of silent war between traditional industry and startups, the result is a massive draw in efficiency… and a quiet loss of purpose.

As a classical philosopher, I start where Socrates felt most at home: in aporia, that moment when everyone thinks they’ve won and yet no one can explain what for.

Let’s rewind.

2. How we built this efficiency trap (Genesis)

For years, we were told a simple story:

• Incumbents were slow, bureaucratic, stuck in legacy systems and heavy business models.
• Startups were agile, user‑centric, with scalable models and cloud‑native technologies, microservices, open APIs, and CI/CD cycles.

In financial services, neobanks, P2P platforms, and insurtechs appeared; in retail, global marketplaces and Shopify‑type platforms; in health, telemedicine and data‑driven healthtech; in mobility, aggregator apps and on‑demand operators; in education, online course platforms and edtech solutions.

The narrative seemed clear: every sector had traditional players with high cost structures—branches, stores, hospitals, fleets, campuses—and new digital players able to grow fast with less physical CAPEX, more automation, and better user experience.

The data backed it up:

• Banking digitalization brought branchless digital banks, built on cloud architectures and open APIs, while commercial banks bore legacy systems and heavy regulatory obligations.
• In retail, major chains integrated e‑commerce and mobile apps for online purchase and in‑store pickup, relying on data analytics for personalization.
• In health, telemedicine and data analytics opened up new modes of care, while hospitals and clinics began adopting digital platforms for remote consultations and big data for tailored treatments.
• Mobility and logistics leaned more and more on optimization algorithms, and in manufacturing, IoT gave rise to smart factories with real‑time sensors.

Governments reinforced the storyline. In Spain, for example, the Startups Law 28/2022 offered tax incentives and administrative simplification. CEIN in Navarre supported the creation of 95 new companies in 2024, generating 129 jobs and backing 474 projects. The social message was clear: more startups, more digital, more competitive.

At the same time, reports on biomed spin‑offs in Spain showed that between 2001 and 2023, 199 new biomedical companies were created from public research, but with a serious lack of later‑stage investors.

Startups learned, through success and failure, some almost canonical lessons:

• Pivot in time, like Slack, which turned an internal tool into a global product.
• Validate the model before expanding, to avoid repeating the agony of Webvan or MoviePass, which grew without economic sustainability.
• Scale with prudence and active customer listening, as in the case of Shopify, which turned its own need into a platform for third parties.
• Manage with financial discipline, aware that cheap capital is not eternal.

Meanwhile, S&P 500 companies showed that sustained R&D strategies could improve financial performance even in adverse cycles, suggesting that pro‑innovation public policies and counter‑cyclical investments made strategic sense.

Everything looked aligned: regulation, capital, technology, business models, UX. And yet, the final scene we imagined at the beginning is not a utopia, but a strange place where everything is optimized… and the human experience is impoverished.

What conflict did we miss when we compared incumbents and startups, sector by sector?

3. The invisible conflict: functionally better, existentially poorer

In Socratic fashion, it’s worth asking an uncomfortable question: what if the criteria we use to compare incumbents and startups were flawed from the outset?

Most analyses—including yours—are built around three axes:

1. Business model: revenue streams, cost structure, scalability, growth strategy, customer relationship.
2. Technology: architecture, speed of iteration, data usage, integration, security and risk.
3. User experience: channels, friction, personalization, retention.

These axes are reasonable for a strategist. But a philosopher asks: what values are left out? What dimensions of well‑being, autonomy, and justice don’t fit into these categories?

The invisible conflict is not “startups vs. incumbents.” The real conflict is an instrumental view of the person vs. a teleological (ends‑oriented) view of the person.

• For almost all players—old or new—the user is ultimately a vector of metrics: CAC, LTV, churn, NPS, usage frequency.
• Efficiency has become an end in itself, not a means subordinate to a higher purpose.

When we analyze sector by sector, we tend to ask: “Who reduces friction more? Who scales better? Who personalizes more?” The Socratic question would be different: who helps the citizen live better, not just consume better?

Let’s now look at the sectors, comparing as a strategist would, but questioning as a philosopher would.

4. Finance: from marble to mobile, without asking what money is for

4.1 Opposing archetypes

Incumbents

• Universal commercial banks.
• Generalist insurance companies.
• Investment banks and asset managers.

Startups

• 100% digital neobanks.
• P2P lending platforms.
• Insurtechs with modular products and flexible subscriptions.

4.2 Business models: different ways of selling the same anxiety

Commercial banks live off interest, fees, and FX margins. They carry massive cost structures: branches, staff, strict regulatory compliance. Their scalability is constrained by regulatory capital, licenses, and CAPEX. They grow via geographic expansion, mergers and acquisitions; the predominant relationship is B2C and B2B, with a strong in‑person and institutional component.

Neobanks and P2P platforms operate with much lighter structures: subscription income, transaction fees, and premium services; reduced costs thanks to the absence of a physical network and a more agile organization. Their scalability is high: cloud‑native architectures, open APIs, ability to enter new countries before opening an office. They grow via digital expansion, partnerships, and network effects in their financial product marketplaces.

Insurtechs, focused on modular policies, micro‑insurance, and on‑demand coverage, rely on recurring premium income and added services, with costs concentrated in technology and digital marketing. Their scale depends on trust and their ability to navigate complex regulation.

The tragic draw: incumbents are still perceived as safer; startups as more convenient. But almost no one reframes the main question: do these entities help citizens make wiser financial decisions, or just consume financial products faster?

4.3 Technology: solid legacy vs. conditioned agility

Commercial banks drag along legacy systems, expensive and hard to update. Long development cycles, waterfall approaches, moderate automation. Their use of data, though massive in volume, is often limited by rigid architectures and a culture more regulatory than experimental.

Neobanks and P2P platforms run on microservices, cloud, and CI/CD. They experiment with scoring models based on alternative data, tailor interfaces and flows, and integrate with third parties through open banking. Insurtechs use AI to underwrite risk, automate claims, and explore blockchain for traceability.

4.4 User experience: less friction, more temptation

• In traditional banking, onboarding can be slow, with in‑person requirements and long forms.
• In neobanking, opening an account takes minutes; the design is mobile‑first and the customization of alerts and spending categories makes the app almost playful.

This UX superiority isn’t neutral. It supports impulsive spending habits just as much as disciplined saving. Personalization can become hyper‑segmentation of risk and exclusion of less profitable profiles.

The long‑term failure isn’t technological: it’s moral. A sector that controls the flow of resources for the whole of society rarely asks whether its goal is to maximize credit volume or strengthen people’s financial resilience.

5. Retail: the customer is always right, even when they’re wrong

5.1 Opposing archetypes

Incumbents

• Large supermarket chains and physical retailers.
• Department stores and specialty chains with store networks.

Startups

• Generalist e‑commerce platforms.
• Specialized marketplaces (fashion, niches, crafts, etc.).
• Quick‑commerce and last‑mile logistics startups.

5.2 Business models: volume vs. extreme granularity

Traditional retailers depend on direct sales, tight margins, and economies of scale. Their cost structure: warehouses, stores, store staff, internal logistics. They scale by opening more outlets and optimizing the supply chain. They are B2C, with some B2B deals.

E‑commerce platforms based on Shopify‑type models are funded by SaaS subscriptions and transaction fees. Marketplaces charge commissions, sell internal ads, and offer financial services to sellers. Quick‑commerce monetizes rush delivery fees and brand deals.

Digital models scale through low marginal cost per new user, network effects, and the ability to aggregate supply without owning inventory. But CAPEX comes back in through another door: warehouses, dark stores, externalized fleets.

The consumer gets more choice, faster, cheaper. The philosophical question: to what extent is turning every immediate desire into a product delivered in minutes progress… and not just training impulse?

5.3 Technology: the store as algorithm

Traditional retailers move toward integrated management systems, data analytics for assortment and pricing, and apps for click‑and‑collect. Adoption is gradual, tied to legacy systems and complex physical processes.

Startups put data at the core: personalized recommendations, dynamic pricing, continuous A/B testing in UX, routing algorithms to optimize delivery routes.

5.4 User experience: omnichannel vs. app‑only

• The incumbent tries to make physical store, web, and app coexist; it strives for a coherent experience, but the customer often feels two separate worlds.
• The startup is usually app‑first or app‑only: instant onboarding, stored payment, one‑click reorder.

Less friction, more frequency of use. Success is measured in recurrence, not deep satisfaction. The experience is optimized for the act of buying, rarely for the sensible use of what’s bought.

6. Health: smart data, fragile bonds

6.1 Opposing archetypes

Incumbents

• Public and private hospitals.
• Clinic networks and health insurers.

Startups

• Telemedicine platforms.
• Healthtech data integration and analytics (like Innovaccer, which reached $250M in annual recurring revenue through alliances with health systems).
• Highly specialized clinical AI startups.

6.2 Business models: the patient as cost or as data

Hospitals and insurers run on fee‑for‑service, capitation, or insurance models. Extremely high fixed costs: infrastructure, medical staff, equipment. Scalability limited by regulation, talent availability, and CAPEX.

Healthtech companies sell SaaS subscriptions to hospitals, data integration projects, and population‑level analytics. Telemedicine platforms operate with per‑consultation fees, subscriptions, or deals with insurers.

Startups’ scalability is constrained by strict regulation and the difficulty of integrating scattered data. The report on Spanish biomedical spin‑offs makes another limit clear: the lack of later‑stage funding, which stops many innovations from going beyond pilots to standard of care.

6.3 Technology: promise of integration, reality of fragmentation

Hospitals adopt electronic health records, basic teleconsultation, and analytics tools, but often run multiple non‑interoperable systems.

Health data startups chase integration: they aggregate, clean, and analyze clinical data, aiming at personalized treatments and proactive management. AI startups propose prediction models, assisted diagnosis, and automation of administrative tasks.

The paradox: technically, integration is more possible than ever. Organizationally, it remains elusive.

6.4 User experience: patient or “use case”?

Telemedicine reduces waiting times and eliminates travel. UX‑oriented platforms provide simple onboarding, appointment reminders, and follow‑up on results.

But something is lost: the longitudinal relationship with a professional who knows the person’s full context. The risk is that the patient becomes an “clinical event” in a data timeline.

The deeper failure would be a health system that efficiently manages biomarkers… without asking the classic question: what does it mean to live well with an illness?, not just how do we reduce a number on a panel?

7. Mobility and logistics: the city at the service of the algorithm

7.1 Opposing archetypes

Incumbents

• Public transport operators (bus, metro, trains).
• Traditional logistics companies and transport fleets.

Startups

• On‑demand mobility platforms (ride‑hailing, VTC, car sharing).
• Route and multimodal ticket aggregators.
• Last‑mile logistics and algorithmic optimization startups.

7.2 Business models: stable ticket vs. dynamic pricing

Public transport lives on regulated fares and subsidies. High infrastructure and staff costs. Low scalability, but with economies of scale where user density is high.

Mobility platforms live off commissions per ride and sometimes ads or premium services. They externalize many costs (vehicles, maintenance) to drivers or small operators. They scale rapidly in dense cities, activating network effects: more drivers attract more users, and vice versa.

In logistics, incumbents combine long‑term B2B contracts with capital‑intensive structures and fleets. Startups bet on optimization software, platform models, and courier networks.

7.3 Technology: choreographing movement

Traditional operators modernize ticketing and planning systems, but remain constrained by physical infrastructure and long investment cycles.

Startups use real‑time matching algorithms, dynamic pricing, and smart routing. API after API, the city turns into a graph that’s constantly recalculated.

7.4 User experience: simplicity for the individual, complexity for everyone

From their phone, the citizen sees only convenience: plan a trip, buy a ticket, request a vehicle. But the sum of individually optimized solutions can degrade the collective system: more traffic, more emissions, less investment in public transport.

The Socratic question is uncomfortable: what kind of city does each model serve? The city as a common good, or the city as a playing board for dynamic fares?

8. Education: infinite courses, scarce meaning

8.1 Opposing archetypes

Incumbents

• Public and private universities.
• Vocational schools and business schools.

Startups

• Massive open online course (MOOC) platforms.
• Specialized training marketplaces.
• SaaS solutions for campus management and assessment.

8.2 Business models: degrees vs. modules

Universities are funded by tuition, public funding, and in some cases research and corporate services. Their cost structure includes campuses, tenured faculty, administration. They scale by opening new programs or campuses, with strong regulatory constraints.

Content‑based edtech companies sell subscriptions, per‑course payments, or B2B licenses to companies for continuous training. Marketplace‑type platforms charge commissions to instructors and businesses.

Startup models scale well: low marginal cost per new learner, global distribution, network effects in their catalog. But they face a question UX can’t solve: who really certifies meaningful learning?

8.3 Technology and experience: micro‑learning, macro‑confusion

Universities adopt LMSs, virtual classrooms, and videoconferencing tools, often as a “layer” on top of old structures. Edtech companies design learning journeys with smooth onboarding, data‑driven recommendations, progress metrics, and automated quizzes.

Yet more courses do not necessarily mean better judgment. The risk is an education fragmented into skill modules, where the learner piles up credentials but fails to develop an integrated understanding of the world.

9. The hidden scoreboard: who really wins, who really loses

We can summarize the current outcome in a chart that no pitch deck ever shows.

Table 1. Hidden scorecard: incumbents vs. startups

The scoreboard shows tactical winners but hides the structural loser: the citizen turned into user, the patient turned into data, the student turned into an engagement KPI.

10. Cross‑cutting patterns: uncomfortable symmetries

Across all sectors, some patterns repeat:

1. Selective disintermediation: startups remove costly intermediaries but often become new, more invisible data intermediaries with stronger lock‑in power.
2. Defensive response by incumbents: creation of digital units, corporate VC, startup acquisitions, modernization of legacy systems. They look more like startups on the surface, but not necessarily in purpose.
3. Recurring startup failures: trouble scaling real‑world operations, clashes with regulation, lack of sustainable profitability, critical dependence on “patient capital” that doesn’t always arrive—as with Spanish biomed spin‑offs stuck for lack of late‑stage investment.
4. Persistent incumbent shortcomings: slow product innovation, poor UX, change‑averse internal cultures, prioritizing compliance over genuine experience improvement.

The result is a strange convergence: incumbents adopting startup language and methods; startups adopting big‑company practices (committees, processes, compliance) just to survive.

Table 2. Timeline of a strange convergence

If we follow this trajectory without questioning the ultimate purpose, we end up in 2032 as described: everyone has gained efficiency; everyone has lost something of their soul.

11. The strategic turn: from “how” to “what for”

The central Socratic question: what would count today as rational strategy if the goal were not just to optimize, but to preserve and improve the human condition in each sector?

11.1 Changing the success metric

• In financial services, move beyond margin and fee income to measure clients’ financial resilience: available savings, capacity to absorb shocks, understanding of products. Strategy: design products that reduce anxiety and fragility, not those that exploit them.
• In retail, go beyond basket size and frequency. Measure impact on health, sustainability, and the customer’s time. Strategy: prioritize offerings that improve the client’s life, even if that means less product turnover.
• In health, complement clinical KPIs with perceived quality of life and continuous doctor‑patient relationships. Strategy: design systems where AI frees up time for professionals to focus on the human, not to maximize consults per hour.
• In mobility, replace obsession with trips per user with fair accessibility and urban sustainability metrics.
• In education, value the ability to think critically and act ethically, not just immediate employability.

11.2 Redesign the customer relationship as a relationship with a person

Incumbents and startups could converge, but in a different direction:

• Adopt B2C or B2B2C models that explicitly share the benefits of efficiency with the user in the form of greater autonomy, not just lower prices or more convenience.
• Introduce deliberate friction where impulsive behavior is harmful (unreflective credit purchases, unnecessary treatments, polluting mobility).

11.3 Reinterpret regulation as a moral contract

Regulation is already a key framework: from Spain’s Startups Law to health or transport rules. But it’s usually treated as a limit.

Another reading is to see it as a minimal moral contract between technology and citizenship:

• Design frameworks that reward business models with documented positive impacts beyond internal KPIs.
• Encourage counter‑cyclical innovation policies, as the S&P 500 R&D study suggests, but oriented to basic problems: financial resilience, public health, fair mobility, critical education.

11.4 Invest in responsible scalability

The lessons of Slack, Shopify, Innovaccer, and the collapse of Webvan and MoviePass are already clear: scalability without clear purpose is just a fast way to reach a dead end.

For incumbents:

• Don’t just copy the startup form (squads, sticky notes, KPIs); embed the habit of asking “why?” in every digital initiative.

For startups:

• From day one, design models that can withstand tougher capital markets, stricter regulation, and more demanding social impact metrics.

12. The big framing: who pays the ultimate cost

Back to the beginning. By 2032, each sector has reached a strange equilibrium:

• Banks and fintechs share data infrastructures and APIs; differences are mostly in branding.
• Physical retail and e‑commerce blend into hybrid experiences.
• Health mixes in‑person visits with telemedicine and advanced analytics systems.
• Mobility merges public transport, private platforms, and micromobility, all coordinated by aggregators.
• Education blends formal degrees with online learning bites.

The war between giants and startups ends not with one side’s victory, but with mutual assimilation. The risk is that, along the way, we forget that banks, retailers, hospitals, transport operators, and universities do not exist to maximize internal metrics.

From a classical perspective, each of these sectors once took part in a larger good:

• Banking in prudence and security.
• Commerce in just provision.
• Health in the care of the body and, by extension, of the good life.
• Mobility in enabling encounter and participation in the city.
• Education in the formation of character and reason.

If incumbents and startups forget that telos, the ultimate cost won’t be a financial crisis or stock crashes. It will be something less visible: a society where everything is more convenient, but people are less free inwardly, less capable of deliberation, more subject to incentives and rankings.

The strategic synthesis is, paradoxically, deeply philosophical:

• The question is not who wins between giants and startups.
• The question is whether either side has the courage to ask publicly: “Why do we exist, beyond our KPIs?”

If that dialogue does not happen—in boardrooms, among regulators, in product teams—the 2032 scene won’t be fiction but routine. Everything will be optimized and yet the sense of failure will be hard to describe… except for those who remember that technology, in itself, was never the end.

13. References

1. Socialtargeter.com – Learning from failure: case studies of startups that pivoted successfully after initial setbacks.
2. Geeksgrow.com – Top 5 worst startup fails: lessons learned.
3. Gafowler.medium.com – Lessons from founders who scaled to millions.
4. Foundr.com – Startup case studies: failure.
5. Narwhalproject.org – Scaling up (on prudent, sustainable growth).
6. LinkedIn – AI startup dynamics: failures and success case studies (Innovaccer case and AI challenges in health).
7. Cadena SER – CEIN Navarra 2024 data (95 new companies, 129 jobs, 474 projects supported).
8. Cinco Días / El País – Spain reaches 199 biomedical spin‑offs but suffers from a lack of specialized late‑stage investors.
9. Arxiv.org – Study on R&D intensity and financial performance in S&P 500 firms (1998–2023).
10. Law 28/2022, of 21 December, on promoting the ecosystem of emerging companies (Spain).