In Platform Theater I argued that organizations perform platform language over funnel structure, and that the performance hides an empty column where cross participant value should sit. I want to take that further, because the pattern is more honest, and for that reason more dangerous, than performance. Nobody backstage knows better. The word ecosystem gets used because it still sells, and because the funnel is the only structure most of the industry has ever been able to see. The funnel gets poured into the word, and the substitution goes unnoticed, because there is nothing on hand to compare it against.

This is worse than theater, and the reason it is worse is the reason it cannot fix itself. Theater implies a backstage where the truth is known and a front of house where it is hidden. A performance can be corrected, because someone behind it knows what is real. What the field actually shows is that there is no backstage. The people building these things believe they are building ecosystems. They have taken the one structure they understand and renamed it, and lost the ability to notice, because the concept that would expose the swap, the network effect, is precisely the concept their model has no room for. A mistake nobody can see is a mistake that scales without resistance.

I will keep this to structures and patterns. The names do not matter, and I hold nothing against the people inside them. Most of them learned to navigate their systems extremely well. The problem is the system, not the navigation.

Pattern 1: The toll on what the customer already owns

A capability ships inside the product. The hardware is already installed, already paid for, sitting under the customer. The organization, having watched the software world discover subscriptions and function on demand, puts a gate in front of the capability and charges rent to unlock it.

The reasoning is pure funnel. A capability exists, so gate it and collect recurring revenue. What this misses is the thing that makes function on demand work in a platform. The value there comes from giving a participant a capability they did not have before. New access is something a person will choose to pay for. Charging to unlock hardware the customer already bought is a toll on their own property, and the market reads the funnel under the language at once. The reaction is not confusion about price. It is the recognition of being charged twice for one thing.

The instructive part is what survives this. Software that genuinely adds a capability the product did not have gets accepted by the same customers who reject the toll. They come back to it because they want it, not because a gate forces them to. The customer was never against paying for new value. The customer was against paying rent on owned property dressed as new value. The market draws the line between ecosystem and funnel with its wallet, in real time, and hands the organization the diagnosis it could not produce on its own.

Pattern 2: The network run as a fleet

The deeper version appears whenever an asset owner tries to build a usage network. Significant capital goes in. The language is explicitly platform: take on the new entrants, build the service, change the game. The assets are there, the users are there, the footprint is there. And then it gets shut down, with a reason that contains the entire misunderstanding in a single phrase: rising infrastructure cost, and the level of investment necessary to sustain operations.

They experienced the network as a cost. That is the tell. The insight behind the platforms they were trying to match is that the participants supply the assets and the value rises with every participant who joins. The platform owns little and captures a fraction of what the participants generate between themselves. Each new participant makes the whole more valuable to everyone already in it.

The asset owner builds the opposite and calls it the same thing. It puts its own assets at the center, which depreciate. It carries the infrastructure, which costs more as it grows. It charges per use, which is linear revenue against a fixed and falling asset. Every unit of expansion is more owned asset to buy, maintain, and replace. Scale does not lift the value. Scale raises the cost. So when the structure produces what funnels produce at scale, falling margin against rising cost, the conclusion is that the market was volatile and the investment unsustainable. The market was not the problem. They built a decreasing returns machine and expected it to behave like an increasing returns network, because they used the network’s vocabulary in the announcement. They quit because in a funnel, scale hurts. They never reached the point where scale helps, because they never built the structure where it does.

This pattern is worth pausing on, because it is the case where the proof arrives in the subject’s own words. An organization that actually held the network effect as a concept could not experience a growing network as mainly a rising cost. In that concept, each added participant is added value, and the cost of the next unit of expansion is offset by what it brings to everyone already inside. The only way scale reads as pure cost is if the participants are not generating compounding value for each other, which means the structure underneath was never a network. So the exit statement, given as an explanation of a hard market, is actually a description of the missing dimension. The organization names the empty column while believing it is naming something else. It does not have to be interpreted into agreement with the thesis. It states the thesis about itself, without knowing it has.

Pattern 3: The platform organization with no decision rights

The same error runs inside the org chart. An incumbent stands up a dedicated unit to build the software platform it knows it needs. Large headcount arrives quickly. Large budgets flow. And the platforms slip, restart, and slip again.

The structural reason is consistent wherever this happens. The unit is told to deliver the software but cannot make the decisions. The money comes from the established parts of the business, and those parts keep the power. A platform organization needs its decision rights located where the building happens. The point of a platform, inside a company as much as in a market, is that the people building on it can act without seeking permission upward at every step, because the platform owns the choices that govern its own work. That is what lets a software organization move at software speed. The decisions do not have to be spread evenly, but they cannot be severed from delivery. Give the unit the mandate to become a platform and withhold the authority that makes it possible, and it keeps the funnel’s command structure: requirements issued from above, a delivery layer below executing without deciding. The capital pours into the gap between the two.

The software cannot become a platform because the organization never stopped being a funnel with a software department attached. The work slips not because the engineers are weak but because the structure they sit inside is built to prevent the thing it was asked to produce.

One misunderstanding under all 3

These are not three mistakes. They are one mistake at three altitudes.

A funnel and an ecosystem run on opposite economics. In a funnel you own the asset, you control the gate, value flows one direction, and you improve the business by squeezing: cut cost, raise the toll, optimize the line. Scale adds cost, and returns fall as you push harder. In an ecosystem the participants supply the assets, no single party controls the gate, value is created between parties who do not belong to each other, and you improve the business by opening: add participants, deepen the interactions, let value compound. Scale adds value, and returns rise as you grow.

Each pattern above is the same act. Take a structure that only pays off under increasing returns and run it on the decreasing returns logic of the funnel. Toll the asset you already own. Rent the fleet you bought. Command the platform org from above. Then, when the result is decreasing returns, because that is what was built, blame the market, the customer, or the timing. Never the structure, because the structure looks correct from inside the only model on hand.

The reason this keeps happening comes down to the one phenomenon a funnel cannot represent. A network effect is value that one participant creates for another at no cost to you, which you capture a share of. In a funnel, other participants are either customers, which is revenue, or infrastructure, which is cost. There is no cell in the funnel’s accounting for value that arises between two parties you do not own and did not pay for. So when a funnel mind models a platform, it produces a funnel, and the network effect, the entire reason platforms overtake funnels, shows up nowhere, because there is nowhere in the model for it to show up.

That is the empty column from Platform Theater, named exactly. It is the network effect column. The three patterns are what that absence costs once every other column is filled with real money. The arithmetic adds up in every line except the one that was supposed to hold the value.

How the advisory layer reproduces the error

The most consequential version of this sits not in the operators but in the firms that advise them. The three patterns show the blindness arising on its own inside each company. The advisory layer is where it gets manufactured into method and sold back to the whole sector at once. An operator can make the mistake in one building. An advisor turns the mistake into a product and ships it to every building.

Read the field’s own descriptions of transformation work and the verbs repeat from firm to firm. Reduce cost. Increase effectiveness. Shorten time to market. Optimize the chain. Accelerate. Standardize. Every one is a single actor verb. Each describes one organization doing something to its own cost or its own speed. None describes value arising between participants, even when the word ecosystem sits in the same sentence.

A vocabulary is not a proof of what its users can or cannot think. A firm could write cost language and still hold the network effect somewhere behind it. So the verbs on their own settle nothing. What makes them worth reading is the absence, sustained and total, of the other vocabulary. If the network effect were present in the thinking, it would surface somewhere in the language, in how a firm describes the value it creates, at least once, because people reach for the words for the things they actually attend to. Across the layer it does not surface. The label comes from the platform world and sits on top. Every verb underneath, without exception, comes from the funnel. One missing word is noise. A missing dimension, absent everywhere the work is described, is a signal worth following to the method itself.

The verbs reveal the blindness in the language. One approach reveals it in the method, by turning the same blindness into a product that can be sold. It has become common in advisory work, usually offered as a minimum viable ecosystem. Start small, the pitch goes, then grow, and realize a business ecosystem quickly and successfully. It sounds like platform thinking. It is the funnel with the platform’s vocabulary stretched over it, and the proof is in what the approach contains and what it leaves out.

What it leaves out is the only part that makes an ecosystem an ecosystem. There is no participant validation. An ecosystem begins with at least two participants who actually exist and a problem between them that is real, both established with data rather than assumed. There is no challenge between participants as the unit of value, the place where one participant solves something for another and the value moves between them as a transaction. There is no account of the value streams that follow once there is more than one participant: money, data, collaboration, and innovation, each moving more than once because there is more than one party for it to move between. And there is no network effect, no mechanism by which adding a participant raises the value for everyone already inside. What remains is a single party deciding what to build, building it, and scaling it outward. That is a funnel. Calling it minimum viable does not change its shape. It only sets the bar at viability, which means the smallest thing that survives, rather than at the thing a participant returns to without being asked.

The word that should sit at the center is not viable. It is lovable, in the precise sense that a participant comes back to it on their own, which is the only thing that makes the transactions recur, which is the only thing that makes the flywheel turn. Recurrence is what separates the two structures. A funnel can run on a single transaction that never repeats. An ecosystem only exists once the transactions come back, and they only come back when the participant wants them to. An ecosystem ignites at the point where there is enough interconnected value that adding one more participant raises the value for everyone already inside. Below that point you do not have a smaller ecosystem. You have a funnel. The minimum viable framing never reaches that point because it never required participant validation, never made the challenge between participants the unit, and never built for increasing returns. It optimizes for getting something live. An ecosystem optimizes for participants generating value through the thing, repeatedly, because they want to.

This is why the approach can be offered sincerely and still fail reliably. The firm offering it takes itself to be selling ecosystem genesis. What it sells is a faster way to build a funnel, because a funnel is the only structure its model can construct, and the validation step that would distinguish the two was never in the method. The pattern holds across the advisory layer and shifts by distance from the operator. The closer a firm sits to the operator, the more the transformation language thins, until at the layer that places engineers against a shortage the work is measured plainly, in who is known and how many billable people move through the door. At that end of the layer, transformation and staffing have become the same activity under two different words. This is not a charge against the people doing it. It is what the structure produces when the missing concept is absent at every level of it.

What this means

The industry is not short on platform vision. It is short on the one concept that separates a platform from a funnel, and it has covered the absence with the word. It builds funnels, names them ecosystems, runs them on the math of diminishing returns, and reads the predictable failure as proof that the future was oversold.

The future was not oversold. It was misread, by organizations that could only see the structure they already knew, advised by firms that could only sell the work they already did, in a setting where every layer has a reason to keep the funnel intact. A real ecosystem redistributes value across participants and dissolves the position from which each layer extracts. So the blindness is not only cognitive. It is load bearing. There is money in not seeing the thing that would change how the money is made.

The organizations that win the next era will not have the best transformation decks. They will hold the network effect as a real variable, build for increasing returns on purpose, validate the participants and the problem before building anything, and let value compound between participants instead of squeezing it out of a line they own. That is a different way of seeing, not a better way of selling. Until it is learned, the capital will keep flowing into every cell of the spreadsheet except the one that matters, and the empty column will keep sending the invoice.

A note on what this exercise settled, for my own record. Platform Theater began as a claim that the empty column exists. Tracing it across the operator patterns and the advisory method was a way of testing whether the claim survives contact with how the field actually behaves. It does. The same absence appears in the toll, in the fleet, in the captive software org, and in the method sold to fix all three, and it appears in the same shape each time, which is what a real structural property looks like rather than a collection of unrelated errors. The strongest of the four is the fleet, because there the subject states the proof itself. Asked to explain a failure, the organization described the missing dimension in its own words and took the description for something else. The cases were not selected to fit. Each organization named its own failure, in its own words, in terms that map onto the same structure without being prompted to. That the convergence is unprompted, and that it holds across actors who do not share notes, is what separates a structural property from a set of stories arranged to agree. The column is not a metaphor. It is a thing that can be located, named, and watched to recur. That is the finding.

The next question, the one worth the work, is not why the column is empty but what it takes to fill it on purpose.

Want to read more from the Sebastian Thielke? Look here at Substack or go sebastianthielke.com.

Most companies calling themselves platforms have never been platforms. They are funnel businesses with platform vocabulary.

That sentence is harder to say in a room than it sounds here. The strategy deck says platform. The org chart says platform. The press release says platform. The job descriptions say platform. But the business model, the AI portfolio, the incentive structure, and the cognitive model of every person running the operation say funnel. Linear. Sequential. One participant managed at a time. Value delivered, not generated.

Platform Theatre is the performance of platform economics without the underlying shift that makes platform economics real.

The Tell

I have been inside enough platform transformations to know the tell instantly.

Ask the team how their AI handles both sides of the business simultaneously. Not one side, then the other. Both, at the same time, in a single initiative that reads the relationship between participants and produces an output that reflects that relationship.

In a real platform, that question has an answer. In Platform Theatre, it produces a pause and then a redirect to something single-sided. The buyer-facing chatbot. The seller-facing recommendation engine. Each one optimising one participant in isolation. Neither one touching the relationship between them.

That is a funnel. A funnel with two mouths, but a funnel.

What a Funnel Business Actually Does

A funnel business has a direction. Value flows one way. The organisation controls the flow. Each stage is owned, measured, and optimised independently. The customer is a target, not a participant. The supplier is a vendor, not a co-creator of value. The relationship between them is managed by the business, not generated by their interaction.

Funnel economics are well understood. Conversion rates. Cost per acquisition. Lifetime value. Pipeline velocity. These are the metrics of a business that moves things through a controlled sequence.

Platform economics are completely different. Value in a platform is not delivered through the business. It emerges from the interactions between participants. The platform creates the conditions. The participants create the value. The business captures a fraction of what they generate together.

That distinction is not subtle. It is the entire difference between two fundamentally different business models. And yet the language of platform economics is applied constantly to businesses that are operating on funnel logic, measuring funnel metrics, and wondering why the platform economics never materialise.

Why the Theatre Persists

Platform vocabulary became prestigious around 2015 and has never lost that status. Every strategy team in every large organisation learned the language at roughly the same time. Two-sided markets. Network effects. Flywheel dynamics. Ecosystem thinking. The language spread faster than the understanding.

The result is an entire generation of organisations that can describe platform mechanics with precision and then go back to managing their channels and funnels.

The theatre persists for 3 reasons.

First, the language is genuinely difficult to disagree with in a room. If you say your organisation is building a platform, the vocabulary of platform economics is so compelling and so loosely defined that almost any initiative can be framed to fit it. A loyalty programme becomes an ecosystem. A referral mechanism becomes a network effect. A two-sided sales process becomes a marketplace. The language is elastic enough to cover the original shape of the funnel without changing it.

Second, the cognitive shift required to actually operate as a platform is harder than any strategy document acknowledges. Thinking in participant relationships rather than customer segments, seeing value as something that emerges from interactions rather than something you deliver, managing conditions rather than outcomes: these are not strategic adjustments. They are perceptual ones. You either see the participants as interdependent actors or you see them as nodes in your pipeline. Most organisations, under pressure, see the pipeline.

Third, there is no accountability mechanism for the gap between the language and the reality. Nobody measures whether the business is actually creating participant interdependency. Nobody tracks whether AI initiatives are reading both sides of the business simultaneously. The metrics that exist are all funnel metrics. The platform language floats above them, unchallenged.

The AI Portfolio Does Not Lie

This is where it gets concrete.

A company’s AI portfolio is the clearest available signal of what the business actually believes it is doing. Not what the strategy says. Not what the press release claims. What the investment logic reveals when you map every initiative against the dimensions it can and cannot see.

When I run that analysis on a company that describes itself as a platform, I look for one thing first. Whether any single AI initiative processes signals from both participant types simultaneously and produces an output that reflects the relationship between them.

In Platform Theatre, that initiative does not exist. The buyer-facing AI reads buyer behaviour. The seller-facing AI reads seller behaviour. They are designed, funded, and measured independently. No initiative holds both signals at once. No initiative was given a brief to do so.

The cross-participant dimension of the matrix is empty across every row.

That emptiness is not a technology gap. It is not a roadmap item. It is a structural revelation about what the business actually commissions and what it does not. You cannot accidentally score In Scope on cross-participant intelligence. It requires a deliberate decision to ask what the relationship between participants is worth optimising, which requires first believing that the relationship is the thing, not the channel.

Funnel businesses do not believe that. They believe the channel is the thing.

The Deeper Diagnosis

Platform Theatre is not dishonesty. Most of the people performing it believe they are building a platform. They have read the same books, attended the same conferences, hired the same consultants. They are genuinely trying.

The failure is cognitive before it is strategic. They are trying to install platform mechanics on top of a funnel mental model, and the installation keeps failing because the foundation is wrong.

A funnel has a direction. A platform has a gravitational field. You cannot manage a gravitational field by adding more stages to your pipeline. You cannot create participant interdependency by optimising each participant independently and hoping the network effects appear. They do not appear. They are not a consequence of scale. They are a consequence of design.

The design question that separates a real platform from Platform Theatre is simple and almost never asked: what does the relationship between our participants produce, and what would it take to make AI responsible for optimising that relationship rather than each participant separately?

Until that question is commissioned, the rest is theatre.

The people who already know this are sitting in the room where the strategy deck is being presented. They are not the ones presenting it.

Sebastian Thielke is a Systems Synthesist. He builds frameworks from lived, multi-level experience across platform economics, enterprise transformation, and agentic systems. He writes at schwarzpfad.substack.com. Look at sebastianthielke.com for more insights.

There is a problem with platform theory that no one in the field will say out loud.

The canonical texts, Parker and Van Alstyne’s Platform Revolution, Choudary’s Platform Scale, Rochet and Tirole’s foundational economics, are built entirely on post-hoc case analysis. Uber. Airbnb. Amazon. Apple. The theory was assembled by looking at companies that had already won and working backwards to explain why. It describes a change. It does not tell you how to navigate one from the inside.

That is not a minor limitation. That is the whole problem.

The Retrospective Trap

The canonical platform books, Parker, Van Alstyne, and Choudary with Platform Revolution, Eisenmann, Rochet and Tirole, all work retrospectively. They study Amazon, Uber, and Airbnb after the network effects are already visible. They describe what happened and call it a model.

The problem is not that they are wrong. The problem is that retrospective patterns look causal but are not. When you analyse Amazon after the fact, the participant interactions are clear. The flywheel is obvious. The network effects are measurable. But that clarity does not explain how you recognise and activate those same dynamics inside an organisation before they are visible. It does not explain why the identical model failed somewhere else.

Survivorship bias is so deeply embedded in platform literature that most practitioners never notice it. The frameworks feel universal because they are only ever applied to companies that, in retrospect, look inevitable. Of course Airbnb disrupted hotels. Of course Uber dissolved the taxi model. The logic is airtight once you already know the outcome.

But you are not reading Platform Revolution after the fact. You are reading it because your organisation is currently trying to build something, transform something, or explain to a board why the economics do not yet look like the case studies. And at that moment, the books fail you completely.

They describe the destination. They have nothing to say about the terrain.

The Connection Nobody Made

Amazon appears in the intro of this article as one of the companies the canonical books studied retrospectively. That is correct. It is also the sharpest possible example of the problem, because Amazon is the case where the connection was sitting in plain sight and nobody made it.

The flywheel was not even Amazon’s invention. Jim Collins introduced Bezos to the concept in 2000, when Amazon was struggling to survive the dot-com collapse. Collins later reflected that Bezos had always been an instinctive flywheel-level thinker but simply lacked the language for it. Bezos recognised Collins’ framework as a crystallisation of something he had already been doing intuitively, and sketched the loop on a napkin. Lower prices attract customers. Customers attract third-party sellers. Sellers expand selection. Selection attracts more customers. The loop compounds.

Nobody in that room named what was actually happening. Collins named the loop mechanics. Bezos named the growth engine. What neither of them named is that the flywheel only works because producers and consumers mutually constitute each other’s value. Remove either participant group and the loop collapses immediately. That is not a growth engine. That is a multisided system where participant interdependency is the mechanism. The flywheel is a platform model. Amazon built it without knowing that. Collins framed it without knowing that.

The platform theorists then arrived. They studied Amazon, extracted the network effects, and held the flywheel up as confirmation of their models. They also missed it. They saw the network effects as an outcome. They did not name the participant interdependency as the cause. The mutual constitution between producers and consumers, the fact that what one side does continuously reshapes the value equation for the other, was not identified as the operative mechanism by anyone who built it, advised it, or studied it.

I connected it not from the literature but from watching the same failure repeat. Organisations attempting the flywheel, managing producers and consumers as separate channels, wondering why the loop would not close. The participant interdependency was not visible to them because nobody had named it as the mechanism. Once I named it, the failure mode became immediately legible and so did the fix.

That connection is not in the literature. It has not been made in any of the canonical texts. It is the operative insight that the entire field has been circling without landing on.

Which means the academics who have studied this most carefully are sitting on top of an admission they cannot yet explain.

The Gap the Academics Already Know About

The researchers know this gap exists. They have named it formally.

Studies on digital industrial platforms document that network effects have not been sufficiently exploited in practice to generate and capture value, that there is a measurable gap between the theory and its actual operationalisation in complex B2B settings. The academics are not unaware of the problem. They simply have not solved it. Even the scholars studying platform creation admit that the literature provides only a thin understanding of how organisations actually create indirect network effects in practice.

This is not a niche finding. It is a structural admission buried in the footnotes of papers that most practitioners never read.

What is missing from almost every textbook is the operative middle. The literature describes structure and outcome but not the process of transformation. How does the internal cognition of an organisation actually shift from channel-thinking to participant-thinking? Who drives it? At what point does it fail, and why? These questions are not treated as central problems. They are treated as implementation details, left to the reader, assumed away.

They are not details. They are the whole difficulty.

Part of why that difficulty persists is structural. The model the field inherited was never built for the complexity it is now being asked to explain.

Why the Two-Sided Model Breaks

The theory was built by economists studying consumer platforms. Two sides. Clean symmetry. Riders and drivers. Buyers and sellers. The mathematics works elegantly in that frame.

The moment you add a third participant, a regulatory body, an implementation partner, a channel that also competes, an ecosystem actor with its own agenda, the two-sided model starts to produce wrong answers. Cross-side network effects between multiple sides exhibit complex dynamics that the existing models cannot capture. The researchers acknowledge this. What they do not acknowledge is why organisations keep trying to apply the two-sided model anyway.

The answer connects directly to the participant interdependency problem already named above. Most people, when they hear “multisided,” think it means having different groups of participants. Producers and consumers. Buyers and sellers. Enterprise customers and developers. The assumption is that multisided means multiple lanes running in parallel.

It does not. Multisided means the participants shape each other. What one side does changes the value equation for every other side. A new category of enterprise customer on one side does not just add volume. It changes what developers build, which changes what end users can do, which changes what the enterprise customer is actually buying. The value is not delivered through the platform. It is generated by the interactions between participants, and those interactions run in every direction simultaneously.

This is why the one-to-one relationship model fails completely in a platform context. Managing a partner as a bilateral relationship, a contract, a channel, a pipeline, treats that participant as a static node. But every participant in a real platform is also influencing every other participant, often in ways that are not visible to the organisation in the middle. The platform does not distribute value. It creates the conditions for value to emerge from participant interaction. That is a fundamentally different job.

The answer is not ignorance of complexity. The answer is that organisations do not actually see what they have. They see channels. They manage participants as if they were nodes in a distribution pipeline rather than actors in a value-creating system. The platform theory is applied on top of a channel-management cognitive model, and the collision produces exactly the confusion that every platform transformation generates: the strategy is theoretically coherent and operationally useless.

The Root Cause Is Cognitive, Not Strategic

This is the thing the textbooks do not say.

Organisations do not fail at platform transformation because they lack platform theory. Most have read the books. Many have hired consultants who have also read the books. They fail because the cognitive model underneath the strategy is wrong. They are still thinking in funnels, linear flows, controlled handoffs, owned relationships, while trying to execute a strategy that requires them to think in multisided transactions where value is created by the interaction, not the delivery.

A funnel has a direction. A platform has a gravitational field. You cannot manage a gravitational field by adding more stages to your pipeline.

The shift from channel-thinking to participant-thinking is not a strategic adjustment. It is a perceptual one. Until an organisation can see its partners, users, and ecosystem actors as participants with their own value equations, not as segments to be managed, the platform theory sits on top of the wrong foundation and produces nothing.

What Lived Experience Actually Shows

I have been inside this problem across multiple large-scale platform transformations, building the Platform Economics Acceleration practice at AWS from zero.

What I saw repeatedly was the flywheel logic being attempted and failing. Not because the organisations lacked ambition or resources. Because the people running the transformation were managing producers and consumers as separate channels, optimising each side independently, while wondering why the loop would not close. The connection between participant interdependency and platform mechanics had not been made. Once it was made, the failure mode became immediately legible. The economics did not start working because the theory improved. They started working because the cognitive model underneath the strategy finally matched the reality it was being applied to.

The books describe that shift. They do not produce it. That is the gap.

The Article the Literature Cannot Write

Platform theory is retrospective and survivorship-biased. It describes winners after the fact and calls the description a model. The operative gap, how you actually move an organisation from channel-thinking to participant-thinking, from inside, under pressure, with legacy systems and legacy cognition intact, is documented in the academic literature as an unsolved problem.

It is unsolved because the people who built the canonical cases did not know what they were building. It is unsolved because the theorists who studied those cases extracted outcomes and called them mechanisms. And it is unsolved because the connection between the flywheel and platform theory as a unified explanation of participant interdependency was never made by anyone who was inside it.

That connection is what makes the operative middle visible. Once you see that the flywheel works because producers and consumers mutually constitute each other’s value, the transformation question changes completely. It is no longer about installing a platform model. It is about shifting the organisation’s perception of what its participants actually are and what they do to each other.

That naming is not in any textbook. It is the work that remains to be done.

Sebastian Thielke is a Systems Synthesist. He builds frameworks from lived, multi-level experience across platform economics, enterprise transformation, and agentic systems. He writes at schwarzpfad.substack.com. Have a look at sebastianthielke.com for more insights.

It sounds intriguing: “Let’s build a platform, harness the power of the network effect, even build a marketplace and become an ecosystem.” Easy words to spin up. But throughout my work in this area, I’ve seen that 70% of these initiatives end up being funnel or channel models, never true platform businesses or even close to marketplaces. So, I asked myself why. What are the misleading factors causing businesses and enthusiasts to strive for the best but end up with business as usual?

1. Misleading Conversations: A Common Pitfall

One of the biggest traps is how the concept of a platform is communicated. The journey from idea to execution is long, and there’s a high chance the original vision gets lost in translation. Often, the people and entities you engage with don’t fully grasp what you’re aiming for because it’s something entirely new. They fall back on their existing knowledge and experience, which often leads them to see your platform idea as merely another channel.

How to Handle This Situation:

1. Acknowledge Experience Bias: Start your discussions by highlighting that previous experiences might not apply.

2. Provide Tangible Examples: Use well-known platforms like Airbnb or PayPal as examples. Tailor examples to your industry, or use unique ones like a gravel marketplace.

3. Clarify Your Vision: Be clear that you’re building a multi-sided platform, not just a channel.

2. Misleading Approach: More Than Just Solving Problems

Another common mistake is focusing solely on solving problems for a single customer groups. While that’s important, the end goal shouldn’t be the platform itself. Instead, it should be the outcome of interconnected products creating value for each other. At least three interacting products form the foundation of a successful platform.

How to Handle This Situation:

1. Solve Multi-Group Problems: Ensure your platform addresses the needs of multiple customer groups from the start.

2. Think Interconnection: Design products that add value to each other, forming a cohesive ecosystem.

3. Build Incrementally: Start with individual products and let the platform evolve from their interactions.

3. Misleading Value Streams: Money Isn’t Everything

Focusing solely on financial transactions limits potential. Data, collaboration, and innovation are equally important value streams. Successful platforms often thrive on the value generated through interactions between multiple products, not just direct monetary gains. Think about how a data value stream adds up for generating new insights and from this forming new products that interconnect with the existing portfolio. The pitfall is to apply monetization to everything.

How to Handle This Situation:

1. Diversify Value Stream: Consider data, collaboration, and innovation as crucial value streams.

2. Encourage Interactions: Facilitate and enhance interactions between multiple products on your platform.

3. Design for Value Creation: Ensure your platform design supports diverse value streams beyond just financial transactions.

4. Misleading Efforts and Speed to Market: Patience is Key

Many assume that a fast speed to market is crucial. However, platforms created just for the sake of having a platform rarely succeed. Real platforms derived from interconnected products take time to develop and mature. Typically, true platform businesses see significant turnover only after three to four years.

How to Handle This Situation:

1. Emphasize Patience: Understand that building a platform is a long-term effort.

2. Start with Strong Products: Begin with customer-focused products and build related ones to solve interconnected problems.

3. Set Realistic Expectations: Allow time for products to establish themselves and interact effectively.

5. Misleading Point of View: Think Beyond Single Participants

A platform economy involves multiple participants. Starting with just one group limits your innovation potential. Take a holistic view of your use case, considering all participants and working backward from each perspective to uncover new products and shared capabilities.

How to Handle This Situation:

1. Adopt a Multi-Participant Perspective: Consider all stakeholders involved in your use case.

2. Map Out Needs: Understand each participant’s needs and contributions to the ecosystem.

3. Rethink Your Business Model: Align your business model with platform economics and embrace the opportunities it brings.

6. Good Orientation: Leverage Proven Frameworks

The flywheel effect and well-architected frameworks can guide your platform development. These concepts help you understand the importance of creating interactions between products and participants, driving network effects.

How to Handle This Situation:

1. Utilize Proven Frameworks: Apply concepts like the flywheel effect to your platform design.

2. Facilitate Interactions: Ensure your platform architecture supports seamless interactions between products.

3. Follow a Roadmap: Use the progression from product to portfolio to platform as a guide for developing a sustainable platform business.

Building a successful platform business is not just about leveraging buzzwords and trendy concepts. It requires a deep understanding of multi-sided markets, diverse value streams, and the patience to see through a long-term strategy. By addressing these common pitfalls with clear strategies, you can move beyond the misleading approaches and build a thriving platform ecosystem. Let’s start this conversation and share your thoughts on how we can collectively overcome these challenges.

The journey of ShopSync, a fictional e-commerce company, offers invaluable insights into overcoming common pitfalls and strategically navigating the path to platform success.

Misleading Conversations: Aligning Stakeholder Vision

Miscommunication often leads stakeholders to misunderstand the platform concept, perceiving it as just another sales channel. ShopSync tackled this by highlighting that traditional e-commerce models might not apply and using tangible examples like Airbnb and Etsy to clarify their multi-sided platform vision. This approach ensured everyone was aligned from the outset, fostering a shared understanding of the long-term goals.

Beyond Problem Solving: Creating Interconnected Value

A common mistake is focusing solely on solving immediate problems for different customer groups. ShopSync recognized the importance of developing interconnected products that generate mutual value. By addressing the needs of both retailers and consumers and designing features that complemented each other, they built a cohesive ecosystem. Starting with strong inventory tools and gradually adding customer analytics and marketing support allowed their platform to evolve organically.

Diversifying Value Streams: More Than Just Transactions

Focusing exclusively on financial transactions limits a platform’s potential. ShopSync realized the importance of data, collaboration, and innovation as critical value streams. By incorporating data analytics services for retailers, collaboration tools for customer engagement, and innovation labs for product development, they ensured the platform thrived on diverse interactions, not just direct financial gains.

Patience Over Speed: Strategic Growth

The pressure for a rapid market entry can be misleading. ShopSync’s leadership initially pushed for a quick launch but soon understood that building a sustainable platform required patience. They adjusted their timeline, emphasizing long-term growth. By starting with well-developed inventory management tools and setting realistic growth milestones, they allowed time for their products to establish and interact effectively.

Holistic Perspective: Multi-Participant Ecosystem

A successful platform economy involves multiple participants. Initially focused on retailers, ShopSync broadened their perspective to include consumers and third-party service providers, unlocking greater innovation potential. By mapping out the needs and contributions of all stakeholders, they aligned their business model with platform economics, fully leveraging the opportunities.

Proven Frameworks: Driving Growth with the Flywheel Effect

Frameworks like the flywheel effect are crucial for platform development. ShopSync adopted this concept, designing their platform to generate momentum through continuous interactions. By facilitating seamless interactions between retailers, consumers, and third-party services, and following a strategic roadmap from individual products to a fully integrated platform, they drove sustainable growth.

Building a successful platform business in retail and e-commerce requires more than buzzwords and trendy concepts. It demands a deep understanding of multi-sided markets, diverse value streams, and the patience for a long-term strategy. ShopSync’s journey offers a compelling blueprint for moving beyond misleading approaches and building a thriving platform ecosystem. This is the conversation C-suites should be having to navigate the complexities and unlock the full potential of platform businesses.

In an era where digital transformation is reshaping industries, understanding platform economics is more important than ever. But what exactly does it mean, and why should it matter to you? Today, I’m diving into the fundamentals of platform economics to unravel its critical role in modern business success, with examples from the retail sector to make the concept relatable.

Defining Platform Economics

Platform economics revolves around the principles that drive the creation, growth, and sustainability of platforms. These are businesses that facilitate exchanges between two or more interdependent groups, typically consumers and producers. Think of them as digital marketplaces or ecosystems where value is co-created by users.

Looking at this definition it sounds easy. The truth is it is not. Because what the simplification of a definition hides is the effort to get there. Describing a “as is”-situation needs only my perception. Getting to this “as is” is way more challenging and incorporates core components not to consider but as inherent.

Core Components of Platform Economics

Network Effects: The value of a platform increases as more users join. For instance, Facebook’s user base grew from 100 million in 2008 to over 2.9 billion in 2021, illustrating how network effects can drive exponential growth.

Monetization Models: Platforms can monetize through various strategies, such as subscription fees, transaction commissions, and advertising. For example, Amazon generated $31.16 billion from third-party seller services in Q1 2021 alone.

Data as a Currency: Platforms collect and leverage data to enhance user experience, tailor offerings, and drive engagement. Netflix uses data analytics to recommend shows, leading to an 80% viewing recommendation accuracy.

Multi-Sided Markets: Platforms often connect different user groups that depend on each other. For example, Alibaba’s Tmall connects over 180,000 brands with millions of consumers and logistics providers, creating a robust ecosystem.

Ask yourself

These are components that are essential to think about platform. It is important to understand that these components don’t create the platform. Even more important a platform is not created for the sake of being a platform. The core thought here are:

• What customer challenges does my product solve?

• Are there different customer groups looking at this solution from different angles?

• Do I need a different approach for another customer group?

• Is there an interaction between solution A and B?

• Is there an interaction between customer group 1 and 2?

• Do I harness these connections?

Why It Matters

Scalability: Platforms can scale rapidly with lower marginal costs. For instance, Uber’s global expansion from San Francisco to over 900 metropolitan areas was facilitated by its scalable platform model.

Innovation Driver: Platforms facilitate innovation by connecting diverse groups. Shopify enabled over 1.7 million businesses to set up online stores quickly, significantly reducing time to market.

Market Disruption: Platforms disrupt traditional industries by offering more efficient and user-centric solutions. Amazon’s e-commerce platform, for instance, contributed to a 50% decline in traditional retail store visits between 2010 and 2019.

Real-World Success Stories

Amazon: Revolutionized the retail sector with a marketplace that connects buyers with millions of sellers. Prime members, who receive exclusive benefits, spend an average of $1,400 per year compared to $600 for non-members.

Alibaba: As one of the largest e-commerce platforms globally, Alibaba connects manufacturers, retailers, and consumers through its ecosystem. In 2020, Alibaba’s Singles’ Day event generated $74.1 billion in sales, showcasing the platform’s massive reach and efficiency.

Actionable Steps for Leaders and Product Owners

Use this actions as a first step and status quo analyze. By looking at these points you should know an answer of YES or NO and the maturity within your organization.

Leverage Data Analytics: Invest in data analytics to understand user behavior, preferences, and trends. This enables personalized experiences that drive engagement and loyalty. For example, use data insights to optimize product recommendations and marketing campaigns.

Foster Network Effects: Encourage user participation and interaction to build a vibrant community. Implement referral programs and loyalty incentives to attract more users, thereby enhancing the platform’s value.

Diversify Monetization Strategies: Explore various revenue models to maximize profitability. This could include subscription services, transaction fees, or premium features. For instance, offer value-added services to sellers and personalized experiences to buyers.

Innovate Continuously: Stay ahead by fostering a culture of innovation. Regularly update your platform with new features and improvements based on user feedback and emerging trends. This keeps your platform relevant and competitive.

Collaborate with Partners: Build strategic partnerships with other businesses to expand your ecosystem. For example, integrate with logistics providers to offer seamless delivery solutions or partner with financial institutions for payment services.

Your mantra to follow

“Innovate, Collaborate, Lead: Pioneering Future Growth through Platform Excellence.” Repeat.

What’s your take on the rise of platform-based business models across industries? Have you seen platforms transforming your sector?

The manufacturing sector has seen significant transformations over the past decade, driven by advancements in technology and the rise of the platform economy. Platforms facilitate connectivity, collaboration, and innovation, enabling manufacturers to optimize processes and deliver superior products. Let’s explore a successful example from the manufacturing sector to illustrate this concept.

Haier: A Leader in Manufacturing Platforms

Haier, a leading global home appliance and consumer electronics company, has effectively utilized platform-based approaches within its manufacturing operations. Their platform, COSMOPlat, exemplifies the potential of integrating digital platforms into manufacturing.

Key Components of Haier’s Platform Strategy

1. Integrated IoT Solutions

Haier’s COSMOPlat offers a comprehensive suite of services that enable seamless integration of IoT devices, data analytics, and automation systems. This capability ensures efficient data flow from sensors on the factory floor to analytics platforms in the cloud.

In one of Haier’s smart factories, IoT sensors monitor machine performance in real-time. For instance, in Haier’s refrigerator production line, sensors track the assembly process, ensuring each unit meets quality standards before moving to the next stage. This real-time monitoring has reduced downtime by 20% and increased productivity by 15% over the past two years.

2. Collaborative Ecosystem

By creating an open platform, Haier facilitates collaboration between different stakeholders, including suppliers, partners, and customers. This ecosystem approach fosters innovation and accelerates the development of new solutions.

Haier collaborates with various industry partners and startups through its platform to co-develop new technologies. This collaboration has led to innovations such as energy-efficient appliances and smart home integration. For example, Haier’s partnership with an AI startup resulted in the development of a smart air conditioner that adjusts settings based on user behavior and environmental conditions. Over the past three years, this collaboration has resulted in a 25% increase in the efficiency of their smart home products.

3. Data-Driven Decision Making

The COSMOPlat harnesses the power of big data and machine learning to provide actionable insights. This data-driven approach helps manufacturers make informed decisions, optimize operations, and improve product quality.

In a Haier plant, machine learning algorithms analyze production data to identify patterns and anomalies. This proactive approach helps in fine-tuning manufacturing processes, reducing defects, and ensuring higher quality products. For instance, by analyzing data from its washing machine production line, Haier was able to significantly reduce the rate of defects by 30% and enhance overall product reliability.

Business Outcomes of Haier’s Platform

Haier’s adoption of a platform economy has yielded impressive business outcomes, demonstrating the platform’s effectiveness in driving operational excellence and innovation.

Increased Revenue:

Haier reported a 15% increase in annual revenue attributed to the efficiencies and innovations introduced through their platform business. The platform’s ability to streamline operations and accelerate product development cycles has been pivotal in achieving this growth.

Enhanced Customer Satisfaction:

Customer satisfaction scores have improved by 20%, thanks to the high-quality products and responsive customer service enabled by real-time data insights and predictive maintenance capabilities.

Market Expansion:

Leveraging COSMOPlat, Haier has successfully entered new markets and expanded its global footprint. The platform’s scalability and flexibility have allowed the company to adapt to varying market demands quickly and efficiently.

Innovation and Development:

The collaborative ecosystem fostered by their platform business has led to a 30% increase in new product introductions. By partnering with startups and industry leaders, Haier continues to push the boundaries of innovation in the home appliance sector.

Multi-Sided Platform Dynamics

Haier’s COSMOPlat is a prime example of a multi-sided platform that brings together various customer groups, facilitating interactions and transactions among them. This dynamic enhances value creation for all parties involved.

Haier’s platform connects manufacturers, suppliers, and end consumers. For instance, appliance manufacturers can source components more efficiently through the platform, suppliers gain access to a broader market, and consumers benefit from customized products and faster delivery times. In the smart appliance segment, consumers can directly interact with manufacturers to customize their products, leading to a personalized user experience and higher customer satisfaction.

The success of Haier’s platform approach in manufacturing demonstrates the transformative potential of the platform economy. By integrating IoT solutions, fostering collaboration, leveraging data, and harnessing multi-sided platform dynamics, manufacturers can enhance efficiency, drive innovation, and stay competitive in an increasingly digital world.

5-Step Guide to Implementing Platform Economy Learnings

1. Identify Core Stakeholders: Determine the key participants in your ecosystem, including suppliers, partners, and customers.

2. Solve your Stakeholders challenges: If you identify your stakeholders you need to look for their challenges and prevailing problems. Solving these means successfully integrating these stakeholders into your platform economics.

3. Foster Open Collaboration: Create mechanisms for stakeholders to co-create and innovate together.

4. Enable Customization: Develop capabilities for mass customization to meet diverse customer needs.

5. Measure and Optimize: Continuously monitor performance and seek feedback to drive improvements and innovation.

Are you ready to embrace the platform economy in your manufacturing operations? Let’s discuss how you can leverage similar strategies to drive growth and innovation in your business.T

n my experience working in digital ecosystems and understanding the intricacies of platform businesses, it needs to be clear what is the success factor: Network Effects. When I first dove into the world of platforms, I was fascinated by how some companies grew exponentially, while others struggled to find their footing. It wasn’t just about having a great product or service - it was about creating value through connections. It became clear to me that platforms like Amazon, thrive because of the network of interactions they facilitate, not merely from the products sold.

Network effects can make or break a platform. They transform a simple marketplace into a powerful ecosystem that scales as more users, sellers, developers, or partners join. Over time, I’ve learned that understanding these dynamics is crucial for anyone wanting to succeed in the platform economy. What keeps a platform alive and flourishing is not just the technology behind it but the increasing value it delivers as more participants engage with it. That’s the magic of network effects, and it’s something every platform business needs to master.

What are Network Effects?

At their core, network effects occur when a product or service becomes more valuable as more people use it. For example, think about social media platforms like Facebook or LinkedIn. The more users that join, the more valuable the platform becomes for everyone. Users benefit from a larger community to connect with, businesses gain access to a larger audience, and advertisers find a more extensive market for their products.

Network effects come in two main forms:

• Direct Network Effects: These happen when the value of a product increases with the number of users. A good example is a messaging app like WhatsApp - its value to users depends on how many of their friends or colleagues are also using the app.

• Indirect Network Effects: These occur when an increase in one type of user attracts more of another type. For instance, in app ecosystems like Apple’s App Store or Google Play, the more users a platform has, the more developers are drawn to create apps, which in turn attracts even more users.

The Power of Network Effects in Ecosystems

For platform businesses, network effects are the foundation of their success. Unlike traditional linear businesses, which focus on producing goods or services and selling them to customers, platforms create value by facilitating interactions between multiple parties. These parties can include consumers, producers, service providers, and developers, depending on the platform’s structure.

Amazon, for example, is a multi-sided platform connecting buyers and sellers. As more sellers join Amazon’s marketplace, they offer a broader range of products, which attracts more buyers. This, in turn, encourages even more sellers to join, creating a self-reinforcing loop of value creation driven by network effects.

Network effects create a virtuous cycle for platform ecosystems, where:

• More users lead to more value for existing users.

• Increased activity attracts new participants, making the platform more robust and diverse.

• Scaling becomes more efficient as platforms can grow without significantly increasing costs.

Real-World Examples of Network Effects

Consider Alibaba, which operates one of the largest e-commerce platforms in the world. Alibaba’s business model capitalizes on both direct and indirect network effects. As more buyers use the platform, more sellers are drawn to list their products, which in turn attracts even more buyers. Additionally, its extensive marketplace ecosystem includes third-party logistics providers, payment systems, and advertisers, all of which enhance the overall value for participants.

Another example is Uber, which connects riders with drivers. As more riders use the app, more drivers are incentivized to join because they can expect higher demand. Conversely, a larger pool of drivers ensures better availability and shorter wait times, attracting even more riders.

Network Effects vs. Traditional Business Models

In traditional business models, value creation relies heavily on ownership and control of resources. For example, a manufacturing company might invest in expensive machinery, raw materials, and labor to produce goods. Its growth is typically linear, requiring more investment and resources as it scales.

In contrast, platforms that harness network effects can scale exponentially without the same resource-heavy investments. This scalability allows platforms to dominate markets faster and build competitive moats that are difficult for traditional businesses to breach. Once a platform reaches critical mass, it becomes increasingly hard for competitors to challenge its position.

The Role of Network Effects in Creating Ecosystems

Network effects are the lifeblood of platform ecosystems. In such ecosystems, value creation doesn’t just come from the company itself but from the interactions between its participants. For example, an app store ecosystem like Google Play thrives because developers create apps for users, who, in turn, generate revenue for developers through downloads and in-app purchases.Successful platforms foster ecosystems where multiple parties can derive value, often without the platform owning or directly managing the resources or services being exchanged. For instance, Airbnb doesn’t own any properties, yet it operates one of the largest accommodation platforms in the world. By facilitating interactions between hosts and guests, Airbnb creates immense value without the capital-intensive investments traditional hotels require.

Strategic Importance of Network Effects for Businesses

For businesses looking to transition into platform models or build sustainable ecosystems, network effects should be a primary consideration. Here are a few strategic insights for leveraging network effects:

1. Enable multi-sided participation: Encourage different user groups (e.g., consumers, sellers, developers) to join and engage with the platform.

2. Prioritize user experience: Ensure that the platform creates seamless and valuable interactions for all parties.

3. Scale through partnerships: Collaborate with third parties to expand the ecosystem’s offerings and attract more participants.

4. Leverage data: Use insights from user interactions to improve services and personalize offerings, which can further fuel growth.

5. Build trust and reliability: Ensure that the platform is safe and reliable for all participants, as trust is crucial in maintaining engagement in ecosystem-based platforms.

Transactions and network effects are immanent

Network effects and transactions are deeply interconnected, especially in platform ecosystems. The real value of a network effect lies not just in increasing the number of users but in facilitating transactions between them. As more participants join a platform, the opportunity for interactions (whether buying, selling, exchanging services, or sharing information) multiplies. Each transaction that occurs adds incremental value to the ecosystem, reinforcing the platform’s attractiveness to new users.

For example, in a marketplace like Amazon, each purchase (transaction) not only brings value to the buyer and seller but also strengthens the platform’s appeal by increasing trust, product variety, and the likelihood of future interactions. Thus, platforms that effectively scale transactions as part of their network effects can create a self-sustaining cycle of growth, where more participants lead to more transactions, which in turn attract even more participants.

What to take away

Network effects are not just a feature of platform ecosystems - they are the engine that drives growth, scalability, and long-term success. Understanding and harnessing these effects can be the difference between a stagnant traditional business and a thriving platform ecosystem. With transition to more ecosystem-based models, recognizing the power of network effects and designing platforms that amplify them will be critical for sustaining competitive advantage in today’s digital economy.

Here is a piece of thought to spin in your head: Amazon has a model called the Flywheel. It is a framework that shows how input of energy generates new energy and keep a wheel spinning. The bigger idea is that growth is handled through this perception. Dealing a lot of times with this business model frame 1.5 years ago it hit me like a truck: The flywheel is one of the best representations of the network effect and multi-participation approaches out there. Why? It inherits more than one participant and it shows how growth happening by the network effect.

Think about and let me know in the comments if you share this point of view.

By now everyone needs to be aware that data is the driving factor in the digital world. Please ignore the concept of data as oil. This picture does not work. Why? Oil is limited. Data is not. Using data will provide you with more data. It is a “resource” growing with use and sharing. It is the opposite of oil.With this clarification out of the way let me tackle the importance of data within platform economics and ecosystems. Data takes a vast role in this view:

• Resource

• Value and Value stream

• Enabler

• Innovation

• Success factor

Just talking about data as an asset will not bring any outcome. Today, most organisations are having such an amount of data that the only talking about it paralysis the company. It is hard to get a grip on it. Using it, seems to be even more abstract. With the current genAI dawn, companies need to act on these data. The dawn of genAI gives companies the capability to harness and use their data and not just collect and starring at it.

1. Introduction: The Role of Data in Modern Platforms

Data is the cornerstone of today’s platform economies. Every action a click, a purchase, or a review, leads into a data ecosystem that powers growth and innovation. Platforms like Amazon, Uber, and Airbnb thrive because they don’t just collect data. They utilize it to refine their services and create seamless, efficient user experiences.

In platform businesses, data isn’t just an output it is the powerful push to bring in the network effects. The more data platforms gather from user interactions, the better they become at understanding preferences, predicting behavior, and facilitating transactions. This cycle is what accelerates the growth of platforms. A higher volume of data translates to more accurate algorithms, which in turn improve the value of the platform for each participant.

It’s a continuous loop where data and platform value reinforce each other, leading to exponential scaling. Think of data as the heavy push for a flywheel.

From building the underlying infrastructure to ensuring real-time data processing, data engineering is an essential part of platforms to fully leverage their data potential. Whether it’s creating pipelines for streaming analytics or designing scalable storage solutions, data engineering is what enables platforms to turn raw data into actionable insights that benefit users and businesses alike.

2. Data-driven nature of platform economies and ecosystems

Platform economies thrive on data. Every interaction on a platform, from a user searching for a product to a transaction being completed, produces valuable data that fuels the entire ecosystem. This data-driven nature allows platforms to continuously refine their offerings and improve user experiences. For example, platforms like Amazon and Netflix use data to track user behavior, which helps them recommend products and content with high accuracy. The more users interact with these platforms, the more data is collected, creating feedback loops that optimize algorithms and personalize services.

In addition to improving user experiences, data enables platforms to increase transaction efficiency. For instance, Uber’s data allows for real-time matching of drivers and riders, while Airbnb uses data to set competitive pricing for rentals. By analyzing these huge amounts of data, platforms can fine-tune their systems to reduce friction. This ensures that transactions between users are seamless and efficient. Platforms rely on data not just for insights but to create a continuously improving, value-adding cycle that benefits both the business and its users.

3. Data as a catalyst for network effects

Data is the backbone that amplifies network effects within platform economies, as it plays a critical role in enhancing the value users gain from the platform, both directly and indirectly. At the core of these network effects is the efficiency of transactions facilitated by platforms. Platforms like Amazon and Uber use data to streamline their operations by improving matching, reducing search costs, and ensuring users can complete transactions as frictionless as possible.

This directly increases user satisfaction and engagement, as the more efficiently a platform can facilitate transactions, the more valuable it becomes to its users.

Direct network effects

Direct network effects occur when the value of the platform increases as more users join. Data makes this possible b helping platforms personalize services and improve recommendations. For example, on a social media platform like TikTok, the more data the platform gathers about its users’ preferences, the more effectively it can suggest relevant connections, content, or groups to new users. In turn, as more users connect and engage, the platform becomes even more valuable to its existing users.

Indirect network effects

Indirect network effects also play a crucial role, particularly when there are multiple user groups interacting on a platform. Like Airbnb or Uber rely on both supply-side and demand-side users (hosts and guests, or drivers and riders). Data allows these platforms to optimize interactions between these groups by improving search algorithms, dynamic pricing, and real-time matching. The more riders join Uber, for instance, the more data Uber can collect on ride patterns, which helps improve driver allocation and reduces wait times. As a result, the experience is enhanced for both drivers and riders, increasing the platform’s overall value.

In both direct and indirect network effects, data enhances the transaction efficiency, creating a feedback loop that accelerates platform growth.

As more users participate, more data is generated, which further enhances the platform’s capabilities, thus attracting even more users. This virtuous cycle is what makes data such a powerful driver of network effects in platform economies.

4. Leveraging data to expand ecosystems and platform economies

Data plays a crucial role in the expansion of ecosystems within platform economies by enabling scalability, efficiency, and the continuous improvement of interactions across various user groups. As platforms grow, data acts as the connective tissue that facilitates the integration of new participants, partners, and services into the ecosystem.

• Scalability through Personalization: As ecosystems expand, the ability to personalize experiences for a diverse and growing user base is essential. Data enables platforms to understand user preferences, behaviors, and needs at scale. For example, Amazon uses it to provide personalized recommendations that cater to a global audience, ensuring that each user finds the platform increasingly valuable, regardless of how large it grows.

• Efficiency in Onboarding New Participants: When new users or partners join a platform, data-driven insights streamline the onboarding process. Platforms like Shopify leverage data to help new merchants quickly set up their stores, optimize product listings, and connect with the right customer segments. This data-driven onboarding ensures that as the platform grows, the addition of new participants enhances the overall value of the ecosystem without the sour taste of onboarding friction.

• Enhancing Transaction Flows: In an expanding ecosystem, transaction efficiency is critical for maintaining the quality of user experience. Data enables platforms to optimize transaction processes as the number of users, services, and products grows. Uber, for instance, uses real-time data to optimize driver allocation, ensuring that even as new riders and drivers join the platform, wait times remain low and ride availability stays high.

• Facilitating Cross-Side Interactions: Ecosystems often involve multiple user groups (buyers and sellers, content creators and consumers, or service providers and client). As ecosystems expand, data is key to ensuring smooth interactions between these groups. For example, Airbnb uses data analytics to match guests with the best available hosts, using reviews, past behaviors, and other factors to create a pairings. This keeps both sides of the platform engaged and satisfied, contributing to growth.

• Continuous Improvement and Innovation: Data is the key driver of innovation in an expanding ecosystem. Platforms that leverage data can identify new market opportunities, build on existing services, and introduce new features that cater to a larger and more diverse audience.

As you can see many of the before mentioned themes return in the different parts of the platform economy and ecosystems. You find every of the above mentioned aspects in this f5 points of platform expansion. There is even a model to describe this: The flywheel.

5. A flywheel - the model of network effects, multi-sided platform and growth of platform business

Data strengthens each element of the flywheel in platform economics by boosting performance, scalability, and user satisfaction at every stage:

• Customer Acquisition: Data helps platforms target potential users more precisely through data-driven marketing and personalization. By analyzing user behavior, platforms can create tailored campaigns and recommendations that attract the right users more effectively, fueling the growth of the platform.

• Customer Engagement: Once users are onboard, data is key to maintaining engagement. Platforms use data to personalize content, offers, and experiences, making the platform giving relevance and value to users. This leads to increased interaction, building the network effects that strengthen the platform.

• Optimization of User Experience: Data enables continuous improvement of the user experience. Platforms collect data on user interactions and feedback, which helps in refining features, improving transaction efficiency, and reducing friction. For example, Amazon uses data to enhance its product recommendations and streamline the checkout process, improving user satisfaction.

• Increased Transactions: As data enhances the user experience and engagement, it naturally leads to more transactions. Data helps optimize pricing, inventory management, and matching algorithms, making it easier for users to complete transactions, which in turn increases platform activity.

• Boosting Growth and Scale: With each additional transaction, the platform gathers more data, which is then used to further improve the customer acquisition, engagement, and optimization processes. This creates a virtuous cycle where data continuously fuels the flywheel, driving faster and more sustainable growth.

Data is the key force that amplifies each stage of the flywheel, creating a self-reinforcing cycle of growth and efficiency. Platforms that effectively leverage data scale more rapidly while maintaining high levels of user satisfaction and engagement.

6. Steps to Leverage Data in Platform Economics

Running through this wall of information was not easy till this point. I know. Therefore I kept the action list short and easy. Here’s a short and easy-to-follow list for leveraging data in platform economics:

1. Set Up Data Infrastructure: Collect user data efficiently through scalable systems to track behaviors and interactions.

2. Analyze & Segment Data: Use data to categorize users and uncover trends, helping tailor experiences.

3. Personalize User Experience: Improve engagement by offering personalized content or services.

4. Enhance Transaction Efficiency: Use data to streamline processes, making transactions faster and smoother.

5. Drive Innovation: Continuously analyze data to optimize or introduce new products and improved iterations.

6. Foster Network Effects: Use data insights to attract more users, strengthening the platform.

7. Ensure Data Privacy: Protect user data and build trust through robust security practices.

As you have seen and experienced here, the topic of data is a great endeavor on its own regarding platform economics and ecosystems. I would encourage you to take the steps listed above to have a starting point. Data is important. Period. Nothing to say more.

Just to give you the perspective: By writing this post, I gathered data (head, books, publications) and put it into a format that represents known data in a specific context. This alone creates new data. Now help me to do more. Create more and better data by sharing your insights and enhancing this data piece.

So much has been said, written, unwritten, neglected and banished in regards of agile methodologies that I cannot count it anymore. My learning out of this whole development of up and down, or better said the 2 extremes of a pendulum, is:

We cannot copy and paste.

We need to look for your product.

We need to integrate our customers.

We need to solve real problems for real customers.

Agile methodologies come with more than just relabeling. They come with organization, mindset, innovation and power shifting.

Many of the approaches I have seen focus on the team level. And that is important. Beyond the team level we arrive at the stage how to connect interconnected and platformed products. Here we touch ground with a well fitting term: Platform Teams. It has its origins in Team Topologies* and maybe even earlier. I guess there is even someone claiming to called it that in the 90s.

In the journey to develop scalable and adaptable platforms, the role of Platform Teams cannot be overstated. As businesses increasingly move towards platform-based business models, platform teams become crucial in supporting agile software delivery by enabling other teams to move faster and with greater autonomy.

A short introduction

A core concept in Team Topologies is stream-aligned team. It is structured around a specific flow of work or a “value stream” that delivers directly to the customer or end-users. The purpose of this team is to focus on end-to-end delivery for a particular product, service, or business capability. Unlike traditional team setups where different groups handle distinct phases (like development, testing, or operations), stream-aligned teams take full ownership of the entire lifecycle of their work: From design and development to deployment and maintenance.

This alignment helps improve speed, efficiency, and focus, as the team remains dedicated to a single stream of work, which allows for continuous feedback loops and faster delivery of value. These teams have the autonomy to make decisions and often work cross-functionally, meaning they consist of all the necessary roles (e.g., developers, QA, operations) to ensure smooth and agile delivery. The stream-aligned model minimizes handovers and dependencies on other teams, fostering better collaboration and faster iteration.

The Core Role of Platform Teams

Platform teams are designed to build and maintain internal services that provide reusable components, tools, and frameworks for stream-aligned teams. Their focus is on creating a foundational layer that reduces complexity for other teams, allowing them to focus on delivering value without reinventing the wheel.

For example, if stream-aligned teams are responsible for building customer-facing features, the platform team creates the infrastructure, APIs, or cloud services that streamline development. This separation of duties speeds up delivery and reduces cognitive load across the organization.

Enabling Agility at Scale

Agile principles like fast feedback loops and continuous delivery are often challenged by bottlenecks when teams rely on shared resources or external services. Platform teams prevent these bottlenecks by ensuring that the necessary infrastructure is in place and scalable. By offering “platform-as-a-service” to other teams, platform teams enable rapid experimentation, faster deployments, and ultimately higher-quality products.

Cross-Team Collaboration

Platform teams aren’t just isolated from the rest of the organization. They work closely with stream-aligned teams to ensure their services meet the evolving needs of the business. This X-as-a-Service interaction model is key to ensuring agility. As stream-aligned teams encounter new challenges, the platform team adapts its services, ensuring continuous improvement and faster iteration.

Example in Action: AWS

At AWS, platform teams play a crucial role in empowering product teams by maintaining core services like cloud infrastructure, machine learning tools, and scalable storage systems. These services allow product teams to innovate without worrying about the underlying technology. This enables AWS to deliver new features faster and maintain its competitive edge in cloud services.

Platform teams are the backbone of agile platform development, ensuring that innovation doesn’t get bogged down by complexity. By building scalable services and empowering stream-aligned teams, platform teams play a critical role in driving efficiency, collaboration, and continuous delivery.

As someone who has been deeply involved in the platform economy, I can attest to the critical importance of getting monetization and value stream strategies right. Platforms are fundamentally different beasts compared to traditional businesses. They require a mindset shift in how we think about monetization and value creation. It’s not just about selling a product or service, but about facilitating interactions and exchanges in a way that benefits all participants.

Money

The beauty of platforms is that they can generate value from multiple sources. It’s not a one-size-fits-all approach. Successful platforms often combine different monetization models to capture value from various touchpoints within their ecosystem. Transaction fees, subscriptions, advertising, freemium models, and data monetization are all viable options, but they need to be carefully tailored to the specific dynamics of the platform.

Value

Value stream optimization is the real secret sauce. Platforms need to continuously enhance the value they provide to all sides of their ecosystem. It’s not just about the core transaction, but about creating a holistic experience that keeps users engaged and coming back for more. This is where strategies like leveraging network effects, encouraging user participation, utilizing data-driven insights, and offering ancillary services come into play.

Take a platform like Amazon, for instance. Their monetization strategy is multi-faceted, with revenue coming from transaction fees (Amazon Marketplace), subscription services (Prime), advertising, and AWS. But what truly sets them apart is their ability to create multiple value streams (retail, cloud services, and digital content) allowing them to generate revenue at every point in the value chain. It’s a masterclass in ecosystem building.

Or consider Spotify’s freemium model, which has been highly successful in attracting millions of users with a free tier and then converting many to paid subscribers through premium services like offline listening and ad-free streaming. They’ve also monetized through targeted advertising, creating value for both users and advertisers. It’s a brilliant example of how platforms can effectively blend different monetization models and value streams.

Blurring lines

As platforms continue to evolve, the lines between traditional monetization models and new value streams are blurring. The key to success is flexibility. Being able to adapt to changing user demands and industry dynamics. The future of platforms is all about agility and innovation. Successful platforms will be the ones that can quickly adapt their monetization strategies and value streams to meet the ever-changing needs of their users. They’ll need to continuously experiment, iterate, and find new ways to create value for their ecosystem.

The future of platforms lies in building ecosystems where users, service providers, and third parties all derive value. Monetization is not a one-size-fits-all model. It’s about creating multiple, interconnected value streams that deliver sustainable, long-term results. Consider other sources of value like data, innovation, collaboration and concretion. From a simple perspective they all end up supporting the monetisation but they are values on their own.

For example data: What most companies do not realised regarding the ongoing AI development is the fact, that existing data is used to refine, enhance and develop this data into the next layer of better data. It comes from raw to refined. And it grows. Consider a fundamental resources that grows with it usage. This is data and its value.

In an era where platforms dominate the business landscape, a strategic approach to monetization and value stream optimization is crucial. For any platform to thrive, it must leverage its network, optimize transactions, and continually refine its offering based on data insights. By mastering these elements, platforms can create ecosystems that are not only profitable but also resilient and adaptable in a rapidly changing digital world.

Future point of view

Platforms are shaping the future of business. Those that can master the art of monetization and value stream optimization will be the ones that thrive in this new era. It’s not just about technology; it’s about understanding the dynamics of ecosystems and creating value for all participants. As someone who has been deeply involved in this space, I’m excited to see how platforms will continue to evolve and disrupt traditional business models.

Spending some time with platforms and ecosystems, I’ve learned that great user experiences rarely happen by accident. I remember when our team launched what we thought was a beautifully designed marketplace, only to watch users struggle with even basic navigation. That failure taught me more than any success could have.

The Hidden Architecture of User Journeys

User journey mapping is fundamental to platform design. Key features should be prominently placed where users can easily find them. Last year, we completely redesigned a client’s B2B platform after discovering that their most valuable tools were buried three clicks deep. Post-launch analytics showed that feature adoption doubled.Sometimes the obvious solution isn’t the right one.

Personalization vs. Privacy: Finding Balance

Data drives personalization, but it comes with responsibility. One startup I advised went all-in on personalization, tracking every user interaction. Their engagement metrics soared initially, but user trust plummeted when they realized how much data was being collected. We had to scale back and find a middle ground.The real challenge is creating personalized experiences that feel helpful rather than intrusive.

Managing Diverse User Groups

Building for multiple user types is complex. Take our recent marketplace project – we had sellers wanting deep analytics and buyers seeking simplicity. The solution? Modular design. Core features stayed simple, while power users could progressive unlock advanced capabilities. It failed spectacularly at first.We rebuilt it three times before finding the right balance. That’s the reality of platform design – it’s iterative and sometimes messy.

Accessibility: More Than a Checkbox

When our client’s platform failed accessibility testing, it wasn’t just embarrassing – it excluded potential users and violated regulations. The fix took two months longer than planned, but it forced us to rethink our entire design process. Now accessibility is built in from day one.The business case is clear: accessible platforms reach more users and often work better for everyone.

Continuous Evolution

Platforms are never truly finished. Our team logs every user complaint, feature request, and system hiccup. Sometimes the smallest feedback leads to the biggest improvements. When our user testing showed people struggling with checkout, we redesigned it completely. The bounce rate dropped by 30% within a week.

The Core Triangle: Desirability, Feasibility, Viability

Users need to want your platform. Without desirability, even the most technically sophisticated features will go unused. I’ve seen million-dollar projects fail because they solved problems nobody had.

Feasibility isn’t just about technical possibilities – it’s about sustainable execution. One client insisted on building a real-time translation feature for their community platform. The technology existed, but the processing costs would have bankrupted them within months. We opted for a simpler solution that actually served their needs.

As for viability, I’ve watched brilliant platforms fail because they couldn’t generate revenue. While working with a promising social platform startup, we had to make tough choices between user growth and monetization. They chose growth. Two years later, they shut down – plenty of users, but no sustainable business model.

Getting Real

Platform design isn’t about perfection. It’s about constant improvement, learning from failures, and sometimes making uncomfortable compromises. The most successful platforms I’ve worked on weren’t necessarily the most innovative or technically impressive. They were the ones that solved real problems while building sustainable businesses.

That’s what keeps me fascinated with this field after all these years. Every project brings new challenges, unexpected problems, and occasional breakthroughs. Some days it feels like solving a puzzle while the pieces keep changing shape.

But when it works – when users actually benefit from something you’ve built – that’s what makes all the iterations worthwhile.

Building a thriving multi-sided platform from scratch can feel like an uphill battle. You have a vision of connecting different user groups and fostering valuable interactions, but how do you get it off the ground? Through my different experiences and engagements I uncovered that the biggest hurdle is the starting point. What to do first and what the heck at all. The key is to start with a Minimal Lovable Platform (MLPlat) approach centered around working backwards from your interconnected customers’ needs.

Why You Need to Start with Multiple User Groups

The strength of a multi-sided platform business lies in the networks and interactions you facilitate between distinct user groups. Whether you’re connecting travelers and hosts, job seekers and employers, or any other combination - each side’s experience and value is directly tied to the other groups participating. This interconnectivity is why you can’t just build an MLPlat focused on acquiring one user group. You need to simultaneously solve for the key needs and deliver lovable value to each core user group from day one. Only then can you kickstart the network effects and self-perpetuating growth cycle that makes platforms so powerful.

What is an MLPlat and Why Does it Matter?

An MLPlat focuses on delivering the most essential, user-centric features that will make your early adopters from each interconnected user group fall in love with your platform immediately. It goes beyond just building a bare-bones product by integrating those special “lovable” factors that keep users engaged and coming back. Think about the early days of a platform like Airbnb. Their multi-sided marketplace connecting travelers and hosts didn’t just function - it provided a delightful experience for both sides from the start, setting them up for powerful growth. That’s the power of an MLPlat designed for all your core user groups.

The 3 Capability Layers for Your Minimal Loveable Multi-Sided Platform:

1. Lovability Capabilities: These are the core features that will delight each user group from day one. For a multi-sided platform, this could mean an exceptional browsing experience for demand-side users and effortless listing tools for supply-side users.

2. Platform Capabilities: As your user base grows across groups, you’ll need scalable capabilities to facilitate more interactions, transactions, trusted reviews, payments, and more - all while maintaining a seamless experience.

3. Core Capabilities: Foundational elements like secure transactions, data protection, and reliable infrastructure ensure your lovable features can thrive long-term.

Building Your Multi-Sided Love Step-by-Step:

1. Obsess Over Customer Insights: Deeply understand the behaviors, pain points, and needs of each of your target user groups through research.

2. Identify Core Lovability Features: Use those insights to create 2-3 exceptional experiences per user group that will make them obsessed and engaged.

3. Develop Foundational Capabilities: Don’t overlook basics like secure payments, identity verification, listing tools, and reliable hosting.

4. Add Platform Interaction Capabilities: Explore features that enhance cross-group interactions and value exchange like messaging, reviews, algorithms.

5. Launch and Iterate Quickly: Release a lean initial version and gather feedback from all groups to continuously refine their experiences.

6. Use Data to Guide Scaling: As you grow, analyze usage data to evolve into a full ecosystem solving more needs across user groups.

Where are you heading to?

The long-term dream is to create a self-sustaining multi-sided platform with powerful network effects. But it all starts with delivering lovable value to those first users from each group through your MLPlat.

My advice? Obsess over deeply knowing the realities, needs, and interconnected value drivers of each of your core user groups. Start small but start building your MLPlat today with laser focus on creating experiences they’ll love. Prioritize delighting all sides simultaneously as you methodically add capabilities growing your platform’s ecosystem over time. With an interconnected users-first mindset and strategic planning, you can build a wildly successful multi-sided platform business.

Let me start by saying - my job is pretty dang cool. As the Head of Platform Economy Accelerator at Amazon Web Services (AWS), I get to work with companies redefining how entire industries operate. I’ve had a front-row seat watching platforms revolutionize business models before my eyes. But you know what? The platform economy is just getting started. These trends I’m tracking are going to transform the digital world in some mind-blowing new ways. Let me take you on a journey...

Hyper-Personalization Through AI? You Ain’t Seen Nothing Yet

Some crazy trends I’m seeing is how platforms are getting hyper-intelligent about understanding us. I still remember when I first learned about real-time AI personalization - it absolutely blew my mind! Platforms like Amazon already recommend products, but just wait. Soon they’ll be predicting your needs before you even think of them using real-time data and AI models.

At AWS, I work with platform operators on tools that’ll offer experiences tailored to you in the moment. Can you even imagine what shopping will be like when the platform knows what you want before you do? Platforms will map your entire customer journey from start to finish, super-powered with AI. It’ll drive user engagement through the roof with these intelligently personalized interactions.

The Future is Platform Plug-and-Play

Here’s something I find fascinating - the transition from companies owning assets to being platform enablers. I spend a lot of time advising businesses on this shift to Platform-as-a-Service models. Stick with me here... in the future, companies won’t be self-contained vertical stacks. They’ll create plug-and-play ecosystems that let others build on top of their services and infrastructure. You can extrapolate this to the point as companies will only provide the brand to it.

AWS is already doing this, giving thousands of businesses the cloud infrastructure to innovate at scale. But I see this plug-and-play platform model going industry-wide - healthcare, finance, you name it. Companies will be like Lego bricks that snap together into bigger customer solutions through platforms. It’s a way for innovation to be truly unleashed at the ecosystem level.

Decentralized Platforms: A Paradigm Shift

A paradigm-shifting trend I’m watching is the rise of decentralized platforms. I know some of you are probably thinking - oh great, another blockchain buzzword. But stick with me here...this is huge. We’re talking about platforms governed by the actual community instead of core owners. It’s recreating the whole power structure and economy.

At AWS, we’re looking at ways to integrate this decentralized tech into existing platforms to unlock resilient ecosystems built on transparency and trust. Imagine users collectively calling the shots - the rules, transactions, even the evolution of the platform itself. It’s turning platform economies into truly open and democratic models.

A Blurring of Industry Lines

Get ready for some serious cross-pollination of platforms across industries. Traditionally, platforms have existed in their own cozy little verticals. But I see the boundaries getting blown wide open as platforms converge into interconnected cross-sector ecosystems.

Think about a healthcare platform sharing data with a fitness ecosystem to create a unified personal health hub. Or platforms for supply chain, entertainment, travel - all talking to each other. AWS’s cloud makes that interoperability possible at scale. It’s going to spawn entirely new categories of platforms providing seamless, holistic experiences across multiple spheres of life.

Sustainability: The New Competitive Advantage

Here’s a trend that can’t be ignored - platforms prioritizing sustainability as a core driving force. I work with companies re-architecting their whole business model around green principles like circular economies and carbon-neutrality. Why? Because it’s not just good ethics, it’s good business sense in the long run.

AWS is helping clients use cloud tech to drive sustainability, leveraging data and AI to optimize energy use and minimize environmental impact. Mark my words - the platform economy of the future will be led by ecosystems putting sustainability at the forefront. Those will be the winners.

Platforms Selling...Data?

One of the most fascinating developments I see coming is platforms figuring out ways to directly monetize their data - selling insights rather than just transactions. Data is like the new goldmine, and I’ve been part of some interesting conversations about platforms offering analytics or operational intelligence as a premium service.

Instead of just facilitating buying and selling, platforms could spin up whole new revenue streams around their data. Predictive forecasting, supply chain optimizations - that’s the kind of value they could commercialize and charge for. It’ll totally shift how we measure the value of platform companies. This is not a future view. This is value streaming on platforms and ecosystems.

Composable Companies

One trend I’m really excited about is what is called “composable companies”. Think about it: Why should a company be limited to what’s under its own roof? The future is going to be about companies seamlessly combining their services and capabilities into custom solutions through platforms. It’s like company Lego!

At AWS, we’re building the tools to make that possible - renting out data, algorithms, cloud services - you name it. Companies will be able to snap together exactly what they need for any use case through platforms. It’s going to spark a renaissance of business model innovation.

Agent Swarms

Okay, let me blow your mind for a second. Ever heard of agent swarms? It’s this cutting-edge AI concept where intelligent software agents work together as a decentralized collective intelligence. Like a hive mind, but digital.

These swarms could end up powering everything from automated factories to self-optimizing supply chains. I can’t wait to see platforms emerge that let companies deploy agent swarms as a service. Just think about having an AI workforce that operates at supernatural speeds and dynamically scales to any size on demand. That’s the kind of force-multiplier that will drive crazy innovation through platforms.

The Platform Economy’s Inevitable Trajectory

As you can see, I’m incredibly excited about where the platform economy is headed and ALL the mind-blowing innovations yet to come. Hyper-personalized experiences. Democratized ecosystems. Seamless cross-industry solutions. Sustainability as the norm. This is the inevitable trajectory.

For companies leading the charge, the future will require bold creativity and a obsessive focus on the user experience. Those that embrace these trends won’t just survive, they’ll thrive as platform revolutionaries.

I feel like a kid in a candy store watching this all unfold. The platform economy isn’t just transforming business, it’s transforming life as we know it. So buckle up and get ready, this rocket ship’s just getting started!

Oh no, I am doing it again. I am looking at a broader perspective of platform economics. This time I took business models as a perfect target. I took some time...

Traditional, or classical business models, which dominated the industrial era, are characterized by linear value chains and direct ownership of assets. In this model, value is created internally, and businesses rely on tight control over production and distribution. A typical classical business manufactures products or services, sells them to consumers, and owns most of the assets and processes involved. For example, companies like Ford or General Electric epitomize this model, where innovation happens internally, and revenue is primarily generated through direct sales.

In contrast, platform ecosystems represent a modern shift towards network-based models. Unlike classical businesses, platforms do not directly produce goods or services. Instead, they facilitate interactions between various parties, such as consumers, producers, and third-party developers. The value is co-created by these participants, with platforms acting as intermediaries. Companies like Amazon, Alibaba, and Uber are prime examples, where the business model relies on bringing different user groups together and leveraging network effects. The more users that participate, the more valuable the platform becomes for everyone involved. These ecosystems thrive on openness, external partnerships, and the continuous adaptation of their platforms to user needs and behaviors.

Comparative Analysis

When analyzing classical business models against modern platform-based ecosystems, the differences are not just about how products or services are delivered but how value is created and scaled. Let’s dive into a comparative analysis of these two paradigms. A word of warning: This is a lot to read and digest. Don’t push yourself too much.

1. Value Creation

In a classical business model, value is created internally, often through vertical integration. A company produces a product or service, controls its distribution, and directly profits from sales. For instance, a traditional manufacturer like Ford creates value through the physical production of vehicles, ensuring quality through ownership of every step in the production chain.

In contrast, a platform business like Amazon or Alibaba generates value through interactions between users. Instead of owning the assets or products, platforms enable others to create value. For example, Amazon doesn’t own most of the products sold on its site; it simply facilitates transactions between buyers and sellers. This creates value from participation, where network effects become key drivers of growth. The more users that join the platform, the more valuable it becomes for everyone involved, as the diversity and volume of interactions increase.

2. Customer Relationships

Classical businesses typically operate with a one-2-many customer relationship. The company produces goods, and customers purchase them, with limited feedback loops or direct interaction. The focus is on product delivery and maintaining brand loyalty through quality, advertising, and customer service.

Platform businesses, however, thrive on many-2-many relationships. On platforms like Uber or Airbnb, customers can also be producers (drivers or hosts), meaning that value is co-created. The platform is responsible for maintaining the rules and the infrastructure, but the ecosystem participants build and enhance the service through their contributions. This dynamic creates continuous feedback and a more personalized user experience, as data from interactions can be quickly used to adapt and improve the platform.

3. Revenue Generation

Classical businesses earn revenue through direct sales, with the price of a product or service tied to production costs and margins. For example, a company like General Motors makes money by selling cars, with revenue growth directly tied to the number of units sold and the efficiency of its production.

In a platform model, revenue generation can come from multiple sources. Take Amazon Marketplace as an example: Amazon charges fees to sellers, earns commissions on transactions, and profits from additional services like advertising and logistics. The beauty of platforms is that they create multi-sided revenue streams, which are more scalable than the linear sales process of classical businesses.

4. Scalability

One of the most striking differences between these models is their scalability. Classical businesses, constrained by physical assets and production capacity, face diminishing returns as they scale. Expansion often requires significant capital investment (building new factories, hiring more employees, or entering new markets).

On the other hand, platform businesses can scale rapidly because they don’t directly own the products or services they facilitate. Uber, for example, doesn’t need to own cars to expand its service: its model allows for scaling through increased participation. This asset-light nature of platforms allows them to grow at a pace that classical businesses can rarely match. Growth depends more on network size and efficiency than on traditional resource constraints.

5. Innovation

In classical models, innovation is usually driven internally by R&D departments and corporate strategy. A company might invest heavily in new product development or efficiency improvements, but innovation tends to be more centralized and slower, bound by internal resources and timelines.Platforms, by contrast, harness external innovation. The platform itself is an enabler, allowing users, developers, or third parties to create new value. For example, the Apple App Store or Google Play Store enables thousands of developers to create applications, adding value to the platform without Apple or Google having to directly innovate on every feature. This openness leads to faster cycles of innovation and a broader scope of creative input.

Through this comparative analysis, it becomes clear that the classical business model is more linear, asset-heavy, and inward-focused, while platform businesses are decentralized, user-driven, and infinitely more scalable. As industries evolve, the latter is becoming the dominant force in driving economic value and innovation, fundamentally reshaping how businesses and consumers interact.

Case Studies

The landscape of business has transformed significantly over the past few decades, moving from traditional product-centric models to platform-driven ecosystems that leverage network effects and external innovation. To better understand this shift, let’s examine two key case studies that illustrate the classical business model and platform business model: General Motors (GM) and Amazon. You could imagine why I chose those. I have the better insight to Amazon and building on experience from this.

General Motors (GM) - Classical Business Model

General Motors, one of the largest car manufacturers in the world, exemplifies the classical business model. GM controls its entire value chain, from designing and manufacturing vehicles to selling them through authorized dealerships. This model, known as vertical integration, allows the company to maintain tight control over quality, but it also ties GM’s growth to its physical assets and internal processes. Expansion requires significant capital investment whether it’s building new factories, developing new models, or entering new markets.

The revenue in GM’s classical model is tied to direct sales, meaning it earns profits by selling cars to consumers or dealers. While this model has worked for over a century, it faces scalability challenges. To grow, GM must invest heavily in infrastructure and labor, which can slow down the pace of innovation and limit its ability to adapt to disruptive technologies like electric vehicles (EVs) and autonomous driving.

Amazon - Platform Business Model

In contrast, Amazon’s platform business model has redefined how value is created and scaled in the digital age. Amazon doesn’t manufacture the majority of products sold on its platform. Instead, it facilitates transactions between buyers and sellers, earning revenue through commissions, subscription services like Amazon Prime, advertising, and its cloud services via Amazon Web Services (AWS).

The platform model enables Amazon to scale rapidly without owning the products it sells. Through network effects, the more sellers and buyers that join the platform, the more valuable it becomes for all participants. Sellers gain access to a vast customer base, while buyers benefit from a wide range of competitive product offerings. This asset-light structure allows Amazon to grow exponentially, using technology and external participants to fuel its innovation.

Comparative Analysis

While GM’s classical business model relies on internal production, asset ownership, and direct control over the customer experience, Amazon’s platform model thrives on external participation, creating a marketplace where value is co-created by users. GM’s model offers more control but less flexibility, while Amazon’s ecosystem model scales rapidly through third-party interactions and diversified revenue streams.

The Role of Ecosystems in Modern Business: Owning vs. Participating

The key distinction between traditional models and platform-driven ecosystems is the shift from ownership of resources to participation in a broader network. Companies today must decide whether to focus on owning assets and processes or participating in an ecosystem where value is created through collaboration and interaction with external players.

Traditional Ownership Model

In the classical business model, companies operate by owning and controlling their entire value chain. This model is exemplified by companies like General Motors, which historically owned its production facilities, supply chains, and even financial services. The key benefits of ownership include complete control over the production process, ensuring quality, and setting the terms of engagement with customers. In this model, innovation is primarily internally driven, and scaling the business requires significant investments in physical assets like factories, labor, and supply chain infrastructure.

However, this ownership model has several limitations in today’s fast-paced digital economy. The reliance on internal resources and the capital-intensive nature of expansion can slow down innovation and limit scalability. Companies operating under this model often find it challenging to adapt quickly to market changes or disruptive technologies. While they maintain control, their ability to innovate and respond to external demands is constrained by the assets they own.

Ecosystem Participation: Platform Business Model

On the other side of the spectrum is the platform business model, where companies like Amazon thrive by creating ecosystems that connect multiple participants (consumers, producers, and service providers). In this model, companies do not need to own the assets that generate value. Instead, they act as facilitators or enablers of interactions between different players. Value is created through the participation of users, and the role of the platform is to provide the infrastructure, rules, and tools that allow others to create and exchange value.

For example, Amazon doesn’t own the products sold on its platform; instead, it brings together millions of buyers and sellers, leveraging network effects. As more users participate in the ecosystem, the platform becomes more valuable. The real strength of ecosystems lies in their ability to scale rapidly without requiring significant capital investment. Participation allows businesses to innovate faster by leveraging external contributions, such as third-party developers or sellers, who continuously enhance the platform’s offerings.

The Shift from Owning to Participating

The critical difference between owning and participating comes down to control vs. flexibility. Companies that own their value chains can dictate every aspect of their operations, but they are limited in how fast they can scale and innovate. On the other hand, businesses that participate in ecosystems benefit from external innovation and scalability, but they must cede a certain level of control over the assets that drive their growth.

In modern business ecosystems, platforms benefit from multi-sided value creation. This means that instead of just creating value internally (as in traditional businesses), platforms harness value created by users, developers, and third-party partners. For example, Apple’s App Store allows external developers to create applications, which in turn adds value to the iPhone and iOS ecosystem, driving consumer demand without Apple having to directly innovate on every front.

The Advantages of Ecosystem Participation

Rapid Scalability:Ecosystem participants can grow faster by leveraging external contributions. For example, Airbnb expanded globally without owning a single property by enabling homeowners to rent out their spaces.

Innovation at Scale:Platforms encourage external innovation. Think of how Amazon Web Services (AWS) enables businesses to build on top of its infrastructure, contributing to a continuous flow of innovation without Amazon directly developing every solution.

Diverse Revenue Streams: In ecosystems, revenue can be generated through multiple channels, such as transaction fees, subscription models, advertising, and services. For example, Amazon earns revenue through marketplace transactions, AWS, and Prime memberships, diversifying its income beyond a single source.

Network Effects: As more participants join the ecosystem, the platform becomes more valuable for everyone. A classical example is how the value of Facebook increases as more users, advertisers, and content creators join the platform.

Strategic Considerations for Businesses

Transitioning to and sustaining a platform business model requires a thoughtful strategy that addresses both the shift from traditional business models and the continuous evolution of a platform ecosystem. Below are strategic business considerations based on the comparative analysis of classical businesses and platforms like Amazon, which can guide companies in this transformation:

• Value Creation: Redefine how value is delivered by focusing on enabling interactions and exchanges within the ecosystem rather than solely producing the product or service in-house.

• Scalability and Network Effects: Foster network effects by building a highly usable, flexible platform that can scale without heavy capital investment. This may include focusing on developing APIs, infrastructure, and user-friendly interfaces to allow external parties to innovate and contribute easily.

• Monetization through Multiple Revenue Streams: Diversify income streams by offering value-added services such as data analytics, premium subscriptions, or B2B services. Platforms should also experiment with commission models, subscription fees, and advertising to drive sustainable revenue.

• Ecosystem Development and Partnerships: Develop a robust ecosystem by forging partnerships that enable third parties to innovate on the platform. This could mean engaging startups, developers, or sellers to bring in more value and diversity to the platform without internalizing all innovation efforts.

• Data and Customer Insights: Use the platform to capture and analyze data from all interactions. Leverage these insights to improve customer experience, predict market demand, and innovate faster than traditional competitors.

• Governance and Control: Implement transparent governance frameworks, ensuring that participants (e.g., sellers, developers) follow standards without stifling innovation. A balance of control and openness is critical for maintaining trust and platform integrity.

• Sustainability of the Platform: Continuously iterate the platform by launching new features, improving usability, and incorporating feedback from participants. Focus on building a loyal community through user engagement strategies and developer support.

• Customer-Focus Strategy: Build a customer-focused platform by leveraging data to create personalized experiences, optimizing engagement, and ensuring that the platform grows alongside user needs.

• Regulatory and Market Challenges: Stay ahead of regulatory concerns by focusing on transparency, fair competition practices, and ethical data usage. Be prepared to work with policymakers and regulators as the platform scales.

A short summary

Classical businesses operate with a linear value chain, focusing on production, supply, and distribution. The central aim is optimizing internal resources, ensuring efficiency, and scaling products to meet demand. Revenue is largely dependent on traditional supply chains and customer acquisition.

In contrast, platform ecosystems leverage network effects to connect multiple groups (suppliers, consumers, and third-party contributors) creating value through interactions and transactions between these participants. Ecosystem businesses, such as Amazon, are structured to thrive by harnessing synergies from multiple sides, enabling exponential growth without direct control over every resource or asset.

The transition to a platform model requires rethinking value creation, shifting from ownership to participation, and focusing on creating a space where customers, suppliers, and other stakeholders can interact fluidly. The sustainability of such a business depends on fostering trust, ensuring smooth transactions, and continuously innovating to maintain engagement across all user groups.

A wild view into the future

Where would I be if I am not looking into possibilities out there? Hybrid is an interesting stage. It takes the well experienced and combines it with new and disrupting approaches. So here we go: A different look on how we move on from classic business models vs. ecosystems. I would consider the VS. as an AND.

1. Core Business with Ecosystem Integration: A company maintains its traditional core business model but integrates with an ecosystem to enhance its value proposition. For example, a tech company might keep its main products but partner with other firms to offer complementary services or integrate with platforms that extend its reach.

2. Platform-Centric Model: Similar to ecosystems, a company could develop a platform that serves as a hub for various stakeholders, including customers, partners, and third-party developers. This approach would allow the company to benefit from network effects and collaborative innovation while still leveraging its existing business model.

3. Value Network Approach: The company operates within a network of businesses and organizations, each contributing to the overall value proposition. This model balances the stability of a traditional business model with the flexibility and innovation of an ecosystem. For instance, a manufacturer might engage in strategic alliances with suppliers, distributors, and technology providers to enhance its offerings.

4. Product-Service Ecosystem: This model involves offering products alongside complementary services and solutions, effectively creating an ecosystem around the core offering. For example, a company selling home appliances might also offer installation, maintenance, and smart home integration services.

5. Modular Ecosystem Model: Companies can adopt a modular approach where they retain control over key components of their business while allowing for the integration of external modules or services. This enables flexibility and innovation without fully relinquishing the traditional business model.

In essence, a successful hybrid model would balance the stability and control of classic business practices with the flexibility, innovation, and expanded opportunities offered by ecosystem principles.

This is a lot. I know.

Consider the following: Look at your business today and tell me where are you, what are you doing and did you already consider a different point of view?