How AI Platforms Reshape Innovation: Subordination, Risk, and Workflow Strategy
Rethinking Product Strategy Under Model Governance
how-ai-platforms-reshape-innovation-subordination-risk-and-workflow-strategy
AI Ecosystem
Product Growth Strategy
Surviving Value Migration in AI Platforms
Explore how AI platform governance reshapes product innovation and strategy. Learn actionable approaches to build resilient, workflow-driven AI products that survive value migration and platform constraints.
Agility requires a stable field of optionality. But in today’s AI development cycle, agility is often an illusion.
When upstream model releases become the gravitational center, product teams are pulled into a reactive loop. Instead of responding to user behavior, roadmaps get locked to the cadence of system updates, GPT releases, Claude capabilities, or the latest embeddings API. You don’t iterate. You orbit.
We’ve seen this play out across the industry:
A productivity SaaS team postponed their onboarding revamp because GPT-5.5 was rumored to support multi-modal memory, which would “change everything.” Weeks later, the release didn’t land, and the backlog was frozen in speculative anticipation.
Scenario 1: OpenAI API instability derailed product roadmaps
In 2023, several AI tools like Notion AI, Jasper, and Copy.ai ran into the same wall: frequent changes in API token pricing, rate limits, and model behaviors. Teams that built around GPT-4 or 3.5-turbo found themselves forced to delay launches or rewrite entire prompt architectures. Agility didn’t matter, when upstream behavior is unpredictable, speed becomes irrelevant.
Scenario 2: Twitter API pricing shift wiped out an entire product category
Products like Tweet Hunter and Typefully built growth tools around Twitter’s API, enabling real-time content analysis and automation. After Elon Musk’s acquisition, API access was heavily restricted and monetized. Many tools relying on automated posting or engagement insights were forced to pivot or shut down entirely. Agility only works when there’s still something to adapt to. Once the options are gone, it’s not agility, it’s survival mode.
Scenario 3: Figma’s plugin policy change killed indie developer momentum
Figma once allowed high plugin flexibility, enabling solo builders and startups to launch popular tools, from design asset libraries to one-click templates. But in 2022, Figma changed its Marketplace policies, restricting new plugin submissions. Many creators lost distribution overnight. Even the fastest teams couldn’t “iterate” their way out. There simply wasn’t a viable route left to take.
When value is rapidly migrating across industries and between firms, proactively substituting key elements of the primary business model provides a better fit with the new value landscape than launching secondary business models in parallel.
(Bjorkdahl & Holmén, 2017)
In AI ecosystems, this means rebuilding the operational core, not adding endpoints.
In this environment, product management turns into risk arbitration.
As model capabilities expand, the application layer loses differentiation.
Features that once stood as core advantages collapse into defaults embedded at the infrastructure level.
Users bypass intermediaries and interact directly with platform-native solutions.
What What What - by Ryoji Arai
Capital vs Conversion Limits
Capital vs Conversion Limits
Raising capital doesn’t translate to leverage.
Foundation model development depends on capital-intensive infrastructure, data centers, GPUs, massive compute, exclusive datasets.
Building models like GPT-4, Claude 3, or Gemini isn’t a product decision. It’s a capital game. Most companies are not invited.
Even if your product generates revenue, it cannot buy access to control layers, the critical levers that actually govern model behavior and output logic. Control layers include things like:
How much you can customize API responses
The timing and priority of model updates
The ability to calibrate output trustworthiness (alignment controls)
Most importantly, the training data and model parameters themselves
These controls sit tightly in the hands of a few model developers. Your product can use the model, but it can’t dictate its rules.
Because of this, raising capital doesn’t directly translate into leverage or influence over the platform. API pricing, token usage costs, and compliance complexity all squeeze the capital-to-output ratio.
Scale can extend survival, but it rarely shifts trajectory. In a centralized compute landscape, your levers are limited. You can maintain operations but not reshape your position in the ecosystem.
And as Hacklin et al. (2018) point out: “Adding parallel strategies won’t shift your orbit. The only thing that moves your position is structural reallocation of the core.”
Their study—based on 14 case studies and 68 interviews—outlines four mechanisms that move the needle: Firm-market matching, resource redeployment, attention steering, and complexity de-escalation.
In short: depth wins when scale is capped.
Behind the surface, this reflects a deeper shift in how innovation resources are reallocated and refocused within organizations.
It’s not just about launching new features, it’s about how internal gravity shifts.
According to Hacklin et al. (2018), four mechanisms drive this type of structural realignment:
Firm-market matching
The tendency for firms to adapt their offerings, market definitions, and target segments in response to structural industry shifts.
Companies start redefining the problem they solve and the language they use.
Product features take a backseat to new frames like “co-pilot,” “decision engine,” or “autonomous workflow”, not because they’re trendy, but because that’s what the market now understands as relevant.
It’s less about what you build, more about how you frame it inside a new industrial logic.
Resource Redeployment
Reallocating people, capital, and capabilities toward initiatives that are perceived as more promising in light of changes.
As innovation gains narrative gravity, teams begin to shift.
Infra engineers get pulled into the AI squad. Legal and ops resources pivot toward compliance for LLM deployments. These aren't official org chart moves, they’re more like internal venture capital bets that naturally follow signal strength.
Attention Steering
Managers and employees become increasingly focused on emerging domains, redirecting problem-solving efforts and conversations.
The Slack threads, team standups, and roadmap priorities start orbiting a new center.
Even if legacy products aren’t shut down, they quietly lose strategic mindshare. Nobody says it, but the attention is elsewhere, and attention is what drives energy.
Complexity De-escalation
Simplifying or discontinuing existing initiatives to free up capacity for emergent priorities.
Not all resources can be repurposed. Some legacy systems are too bloated, PMF too fuzzy, or debt too deep to salvage.
So teams make the call: kill the project. Not because it failed, but because the cost of maintaining it blocks the real work.
These four dynamics aren’t written into roadmaps, but they shape them.
They’re how platform gravity works on the inside: through budget shifts, reorg murmurs, calendar compression, and quiet resource drain.
AI isn’t just a tool, it’s a reconfiguration force.
Hacklin et al. (2018) identify four core mechanisms, Firm-market matching, Resource Redeployment, Attention steering, and Complexity de-escalation, that drive successful business model adaptation during rapid value migration
Orbital Hierarchies of Ecosystems
There’s a hidden gravity in the AI industry, one that most product teams rarely name out loud.
Unlike consumer tech, where speed comes from user data, or enterprise SaaS, where roadmap is shaped by customer demands, AI products move to the rhythm of upstream model updates. Your product development cadence no longer belongs to your team. It belongs to OpenAI’s API changelog. Anthropic’s next paper. Google’s model card release.
This shift reorders the entire logic of product building. Suddenly, your iteration isn’t driven by user signals. It’s driven by research breakthroughs. What used to be a feedback loop is now a dependency chain, and that chain sets your tempo.
The result? Teams aren’t building in an open field. They’re building on tectonic plates that shift without warning. It’s not just about keeping up. It’s about staying stable while the ground moves beneath you.
Left by Mrzyk & Moriceau, Right by David Shrigley
Systemic Dependency
Platform governance defines your risk topology.
Model versions, API limits, and policy shifts aren’t just operational concerns, they’re existential.
In traditional product development, agile works because your product decisions are grounded in a stable set of choices, what users do, what you can build, and what the system can handle.
But with AI-native products, that stability vanishes.
When your roadmap depends on OpenAI’s next release, rather than your users' needs, product innovation shifts from being user-led to model-led.
What used to be a feedback loop between product and user has shifted. You're now aligning your roadmap with the timing and direction of upstream model changes, not user needs.
Strategic Response
The industry is entering a phase where traditional defensibility no longer holds. As large foundation models become the new upstream gravity wells, media and AI infrastructure startups face a binary choice: adapt or dissolve into abstraction. Strategic responses are less about incremental optimization and more about systemic repositioning.
“We suggest four underlying mechanisms that link business model innovation, value migration and subsequent outcomes.”（Strategies for business model innovation: How firms reel in migrating value）
After dozens of conversations and experiments, four patterns keep showing up.
Wrapper Mode:
Ship fast, ship first. These teams take the output of LLM APIs and wrap it in a UX layer, often without differentiation. The moat here is speed, not value, and the business risk compounds over time as upstream platforms start to integrate the same capabilities natively.
Promptware Mode:
Instead of building net-new experiences, products compete on prompt design. Teams turn into prompt engineers, but their differentiation is ephemeral. There’s no control over quality decay, and latency or reliability still depends on upstream providers.
AI as Interface:
This group rethinks core interactions, turning structured inputs into conversations, and dashboards into copilots. They rebuild workflows, not just features. But success here hinges on having proprietary distribution or highly engaged users, or else the UX gains aren’t enough to defend against commoditization.
Model Shaping: The Next Strategic Moat
Only a handful of companies can truly shape foundation models. Not just use them, but actively influence how they behave.
The rarest and riskiest path, using proprietary data or usage signals to influence or fine-tune upstream model behavior. It’s a bet on long-term defensibility, and it only works when you own the input layer and can shape demand loops (e.g. via volume, relevance, or trust). Few early-stage teams have this leverage.
To do this, two things must be true:
You control the input layer
You own a steady stream of user-generated inputs, what people ask, how they phrase it, and the volume at which they engage.You generate a self-reinforcing demand loop
Your product doesn’t just attract users. It becomes a feedback engine that foundation models want to learn from.
This isn’t prompt engineering. It’s training influence, changing the upstream model by controlling the inputs it values most.
Inverting the Stack: From API User to Data Source
Most companies are downstream:
→ They consume APIs.
→ They adapt to model changes.
→ They have zero leverage over the model's direction.
But the few who own the data layer flip the stack:
Reddit trains models on its threads, and now licenses that data to OpenAI.
Perplexity captures real-time search queries and click signals, prime data for tuning retrieval and ranking models.
Hugging Face isn’t just hosting models, it’s defining the language around datasets, prompts, and evaluation.
What This Builds: A Moat Based on Upstream Influence
When you control inputs that models need to evolve, you gain:
Negotiation leverage
(Licensing, partnerships, data exclusivity)Strategic defensibility
(Models trained on your ecosystem behave closer to your UX patterns)Pacing power
(You’re less affected by upstream volatility, you help drive it)
In a world where model capabilities are commoditizing,
influence becomes the new infrastructure.
It’s not only products; we this generation are constantly going through our own process of finding product-market fit.
Complexity is inherent in any meaningful transformation, and the path forward is rarely clear from the outset.
History reminds us that vast, intricate systems rarely result from careful blueprinting; instead, they emerge through ongoing negotiation between countless actors and shifting incentives.
Openness isn’t optional, it’s the foundation for innovation. A future controlled by a single dominant player, making deals behind closed doors and exploiting leftover scraps, would be far less dynamic than one fueled by competitive markets, transparent incentives, and shared participation.
The Resilience Equation
Survival Strength = (How Deeply You're Woven into Workflows)² × Control Over Your Data × Speed of Core Reinvention
When the ground shifts beneath us, products rooted in users' daily operations don't just survive, they become the new landscape. This isn't about chasing agility. It's the art of redesigning your place in the ecosystem.
Product-market fit isn’t a fixed point anymore, it’s an ongoing process of adapting as the market and rules constantly shift beneath you.
Anchor Articles and Updates
How AI Platforms Reshape Innovation: Subordination, Risk, and Workflow Strategy — Rethinking Product Strategy Under Model Governance
How AI Understands User Intent Before They Search: Commercial Decision Loops and the Invisible Funnel — Why pre-intent behavior is becoming more legible and monetizable
Inspired by Lenny’s Growth Inflections: AI and Personal Brands Rewiring Startup Growth — AI automation and personal brands are redefining startup growth inspired by Lenny Rachitsky’s Growth Inflections.
Why Growth Marketing Is Not Digital Marketing and Why This Distinction Matters — It’s not that your marketing strategy is flawed. You might just be addressing the wrong problem.
When AI Products Can’t Find PMF, Build a Landing Client Instead — PMF isn’t always found in the product, Sometimes, it starts with one strategic client
Content as a Revenue Tool: Shortening Time-to-Close in Startup Sales — Content that shortens sales cycles, Not just builds traffic
Building Revenue Systems When Scale Isn’t an Option — Profitability First: How Startup Teams Can Drive Revenue in Constrained Markets
Case Studies
Mountain Gentleman — They knew they needed to go digital but had no idea how to start.So we saw things through the rider’s eyes.It wasn’t just about buying gear because it felt like building out your dream GTR.Every part of the journey was designed to match that thrill.
CoinRank — CoinRank needed a fresh way to stand out in crypto. We created a short video strategy that turns complex info into quick, engaging clips that grab attention fast.
Latest Updates
(GQ® — 02)
©2025
Latest Updates
(GQ® — 02)
©2025
FAQ
FAQ
01
What does a project look like?
02
How is the pricing structure?
03
Are all projects fixed scope?
04
Can I adjust the project scope after we start?
05
How do we measure success?
06
Do you offer ongoing support after project completion?
07
How long does a typical project last?
08
Is there a minimum commitment?
01
What does a project look like?
02
How is the pricing structure?
03
Are all projects fixed scope?
04
Can I adjust the project scope after we start?
05
How do we measure success?
06
Do you offer ongoing support after project completion?
07
How long does a typical project last?
08
Is there a minimum commitment?
How AI Platforms Reshape Innovation: Subordination, Risk, and Workflow Strategy
Rethinking Product Strategy Under Model Governance
how-ai-platforms-reshape-innovation-subordination-risk-and-workflow-strategy
AI Ecosystem
Product Growth Strategy
Surviving Value Migration in AI Platforms
Explore how AI platform governance reshapes product innovation and strategy. Learn actionable approaches to build resilient, workflow-driven AI products that survive value migration and platform constraints.
Agility requires a stable field of optionality. But in today’s AI development cycle, agility is often an illusion.
When upstream model releases become the gravitational center, product teams are pulled into a reactive loop. Instead of responding to user behavior, roadmaps get locked to the cadence of system updates, GPT releases, Claude capabilities, or the latest embeddings API. You don’t iterate. You orbit.
We’ve seen this play out across the industry:
A productivity SaaS team postponed their onboarding revamp because GPT-5.5 was rumored to support multi-modal memory, which would “change everything.” Weeks later, the release didn’t land, and the backlog was frozen in speculative anticipation.
Scenario 1: OpenAI API instability derailed product roadmaps
In 2023, several AI tools like Notion AI, Jasper, and Copy.ai ran into the same wall: frequent changes in API token pricing, rate limits, and model behaviors. Teams that built around GPT-4 or 3.5-turbo found themselves forced to delay launches or rewrite entire prompt architectures. Agility didn’t matter, when upstream behavior is unpredictable, speed becomes irrelevant.
Scenario 2: Twitter API pricing shift wiped out an entire product category
Products like Tweet Hunter and Typefully built growth tools around Twitter’s API, enabling real-time content analysis and automation. After Elon Musk’s acquisition, API access was heavily restricted and monetized. Many tools relying on automated posting or engagement insights were forced to pivot or shut down entirely. Agility only works when there’s still something to adapt to. Once the options are gone, it’s not agility, it’s survival mode.
Scenario 3: Figma’s plugin policy change killed indie developer momentum
Figma once allowed high plugin flexibility, enabling solo builders and startups to launch popular tools, from design asset libraries to one-click templates. But in 2022, Figma changed its Marketplace policies, restricting new plugin submissions. Many creators lost distribution overnight. Even the fastest teams couldn’t “iterate” their way out. There simply wasn’t a viable route left to take.
When value is rapidly migrating across industries and between firms, proactively substituting key elements of the primary business model provides a better fit with the new value landscape than launching secondary business models in parallel.
(Bjorkdahl & Holmén, 2017)
In AI ecosystems, this means rebuilding the operational core, not adding endpoints.
In this environment, product management turns into risk arbitration.
As model capabilities expand, the application layer loses differentiation.
Features that once stood as core advantages collapse into defaults embedded at the infrastructure level.
Users bypass intermediaries and interact directly with platform-native solutions.
What What What - by Ryoji Arai
Capital vs Conversion Limits
Raising capital doesn’t translate to leverage.
Foundation model development depends on capital-intensive infrastructure, data centers, GPUs, massive compute, exclusive datasets.
Building models like GPT-4, Claude 3, or Gemini isn’t a product decision. It’s a capital game. Most companies are not invited.
Even if your product generates revenue, it cannot buy access to control layers, the critical levers that actually govern model behavior and output logic. Control layers include things like:
How much you can customize API responses
The timing and priority of model updates
The ability to calibrate output trustworthiness (alignment controls)
Most importantly, the training data and model parameters themselves
These controls sit tightly in the hands of a few model developers. Your product can use the model, but it can’t dictate its rules.
Because of this, raising capital doesn’t directly translate into leverage or influence over the platform. API pricing, token usage costs, and compliance complexity all squeeze the capital-to-output ratio.
Scale can extend survival, but it rarely shifts trajectory. In a centralized compute landscape, your levers are limited. You can maintain operations but not reshape your position in the ecosystem.
And as Hacklin et al. (2018) point out: “Adding parallel strategies won’t shift your orbit. The only thing that moves your position is structural reallocation of the core.”
Their study—based on 14 case studies and 68 interviews—outlines four mechanisms that move the needle: Firm-market matching, resource redeployment, attention steering, and complexity de-escalation.
In short: depth wins when scale is capped.
Behind the surface, this reflects a deeper shift in how innovation resources are reallocated and refocused within organizations.
It’s not just about launching new features, it’s about how internal gravity shifts.
According to Hacklin et al. (2018), four mechanisms drive this type of structural realignment:
Firm-market matching
The tendency for firms to adapt their offerings, market definitions, and target segments in response to structural industry shifts.
Companies start redefining the problem they solve and the language they use.
Product features take a backseat to new frames like “co-pilot,” “decision engine,” or “autonomous workflow”, not because they’re trendy, but because that’s what the market now understands as relevant.
It’s less about what you build, more about how you frame it inside a new industrial logic.
Resource Redeployment
Reallocating people, capital, and capabilities toward initiatives that are perceived as more promising in light of changes.
As innovation gains narrative gravity, teams begin to shift.
Infra engineers get pulled into the AI squad. Legal and ops resources pivot toward compliance for LLM deployments. These aren't official org chart moves, they’re more like internal venture capital bets that naturally follow signal strength.
Attention Steering
Managers and employees become increasingly focused on emerging domains, redirecting problem-solving efforts and conversations.
The Slack threads, team standups, and roadmap priorities start orbiting a new center.
Even if legacy products aren’t shut down, they quietly lose strategic mindshare. Nobody says it, but the attention is elsewhere, and attention is what drives energy.
Complexity De-escalation
Simplifying or discontinuing existing initiatives to free up capacity for emergent priorities.
Not all resources can be repurposed. Some legacy systems are too bloated, PMF too fuzzy, or debt too deep to salvage.
So teams make the call: kill the project. Not because it failed, but because the cost of maintaining it blocks the real work.
These four dynamics aren’t written into roadmaps, but they shape them.
They’re how platform gravity works on the inside: through budget shifts, reorg murmurs, calendar compression, and quiet resource drain.
AI isn’t just a tool, it’s a reconfiguration force.
Hacklin et al. (2018) identify four core mechanisms, Firm-market matching, Resource Redeployment, Attention steering, and Complexity de-escalation, that drive successful business model adaptation during rapid value migration
Orbital Hierarchies of Ecosystems
There’s a hidden gravity in the AI industry, one that most product teams rarely name out loud.
Unlike consumer tech, where speed comes from user data, or enterprise SaaS, where roadmap is shaped by customer demands, AI products move to the rhythm of upstream model updates. Your product development cadence no longer belongs to your team. It belongs to OpenAI’s API changelog. Anthropic’s next paper. Google’s model card release.
This shift reorders the entire logic of product building. Suddenly, your iteration isn’t driven by user signals. It’s driven by research breakthroughs. What used to be a feedback loop is now a dependency chain, and that chain sets your tempo.
The result? Teams aren’t building in an open field. They’re building on tectonic plates that shift without warning. It’s not just about keeping up. It’s about staying stable while the ground moves beneath you.
Left by Mrzyk & Moriceau, Right by David Shrigley
Systemic Dependency
Platform governance defines your risk topology.
Model versions, API limits, and policy shifts aren’t just operational concerns, they’re existential.
In traditional product development, agile works because your product decisions are grounded in a stable set of choices, what users do, what you can build, and what the system can handle.
But with AI-native products, that stability vanishes.
When your roadmap depends on OpenAI’s next release, rather than your users' needs, product innovation shifts from being user-led to model-led.
What used to be a feedback loop between product and user has shifted. You're now aligning your roadmap with the timing and direction of upstream model changes, not user needs.
Strategic Response
The industry is entering a phase where traditional defensibility no longer holds. As large foundation models become the new upstream gravity wells, media and AI infrastructure startups face a binary choice: adapt or dissolve into abstraction. Strategic responses are less about incremental optimization and more about systemic repositioning.
“We suggest four underlying mechanisms that link business model innovation, value migration and subsequent outcomes.”（Strategies for business model innovation: How firms reel in migrating value）
After dozens of conversations and experiments, four patterns keep showing up.
Wrapper Mode:
Ship fast, ship first. These teams take the output of LLM APIs and wrap it in a UX layer, often without differentiation. The moat here is speed, not value, and the business risk compounds over time as upstream platforms start to integrate the same capabilities natively.
Promptware Mode:
Instead of building net-new experiences, products compete on prompt design. Teams turn into prompt engineers, but their differentiation is ephemeral. There’s no control over quality decay, and latency or reliability still depends on upstream providers.
AI as Interface:
This group rethinks core interactions, turning structured inputs into conversations, and dashboards into copilots. They rebuild workflows, not just features. But success here hinges on having proprietary distribution or highly engaged users, or else the UX gains aren’t enough to defend against commoditization.
Model Shaping: The Next Strategic Moat
Only a handful of companies can truly shape foundation models. Not just use them, but actively influence how they behave.
The rarest and riskiest path, using proprietary data or usage signals to influence or fine-tune upstream model behavior. It’s a bet on long-term defensibility, and it only works when you own the input layer and can shape demand loops (e.g. via volume, relevance, or trust). Few early-stage teams have this leverage.
To do this, two things must be true:
You control the input layer
You own a steady stream of user-generated inputs, what people ask, how they phrase it, and the volume at which they engage.You generate a self-reinforcing demand loop
Your product doesn’t just attract users. It becomes a feedback engine that foundation models want to learn from.
This isn’t prompt engineering. It’s training influence, changing the upstream model by controlling the inputs it values most.
Inverting the Stack: From API User to Data Source
Most companies are downstream:
→ They consume APIs.
→ They adapt to model changes.
→ They have zero leverage over the model's direction.
But the few who own the data layer flip the stack:
Reddit trains models on its threads, and now licenses that data to OpenAI.
Perplexity captures real-time search queries and click signals, prime data for tuning retrieval and ranking models.
Hugging Face isn’t just hosting models, it’s defining the language around datasets, prompts, and evaluation.
What This Builds: A Moat Based on Upstream Influence
When you control inputs that models need to evolve, you gain:
Negotiation leverage
(Licensing, partnerships, data exclusivity)Strategic defensibility
(Models trained on your ecosystem behave closer to your UX patterns)Pacing power
(You’re less affected by upstream volatility, you help drive it)
In a world where model capabilities are commoditizing,
influence becomes the new infrastructure.
It’s not only products; we this generation are constantly going through our own process of finding product-market fit.
Complexity is inherent in any meaningful transformation, and the path forward is rarely clear from the outset.
History reminds us that vast, intricate systems rarely result from careful blueprinting; instead, they emerge through ongoing negotiation between countless actors and shifting incentives.
Openness isn’t optional, it’s the foundation for innovation. A future controlled by a single dominant player, making deals behind closed doors and exploiting leftover scraps, would be far less dynamic than one fueled by competitive markets, transparent incentives, and shared participation.
The Resilience Equation
Survival Strength = (How Deeply You're Woven into Workflows)² × Control Over Your Data × Speed of Core Reinvention
When the ground shifts beneath us, products rooted in users' daily operations don't just survive, they become the new landscape. This isn't about chasing agility. It's the art of redesigning your place in the ecosystem.
Product-market fit isn’t a fixed point anymore, it’s an ongoing process of adapting as the market and rules constantly shift beneath you.
Anchor Articles and Updates
How AI Platforms Reshape Innovation: Subordination, Risk, and Workflow Strategy — Rethinking Product Strategy Under Model Governance
How AI Understands User Intent Before They Search: Commercial Decision Loops and the Invisible Funnel — Why pre-intent behavior is becoming more legible and monetizable
Inspired by Lenny’s Growth Inflections: AI and Personal Brands Rewiring Startup Growth — AI automation and personal brands are redefining startup growth inspired by Lenny Rachitsky’s Growth Inflections.
Why Growth Marketing Is Not Digital Marketing and Why This Distinction Matters — It’s not that your marketing strategy is flawed. You might just be addressing the wrong problem.
When AI Products Can’t Find PMF, Build a Landing Client Instead — PMF isn’t always found in the product, Sometimes, it starts with one strategic client
Content as a Revenue Tool: Shortening Time-to-Close in Startup Sales — Content that shortens sales cycles, Not just builds traffic
Building Revenue Systems When Scale Isn’t an Option — Profitability First: How Startup Teams Can Drive Revenue in Constrained Markets
Case Studies
Mountain Gentleman — They knew they needed to go digital but had no idea how to start.So we saw things through the rider’s eyes.It wasn’t just about buying gear because it felt like building out your dream GTR.Every part of the journey was designed to match that thrill.
CoinRank — CoinRank needed a fresh way to stand out in crypto. We created a short video strategy that turns complex info into quick, engaging clips that grab attention fast.
FAQ
01
What does a project look like?
02
How is the pricing structure?
03
Are all projects fixed scope?
04
Can I adjust the project scope after we start?
05
How do we measure success?
06
Do you offer ongoing support after project completion?
07
How long does a typical project last?
08
Is there a minimum commitment?
How AI Platforms Reshape Innovation: Subordination, Risk, and Workflow Strategy
Rethinking Product Strategy Under Model Governance
how-ai-platforms-reshape-innovation-subordination-risk-and-workflow-strategy
AI Ecosystem
Product Growth Strategy
Surviving Value Migration in AI Platforms
Explore how AI platform governance reshapes product innovation and strategy. Learn actionable approaches to build resilient, workflow-driven AI products that survive value migration and platform constraints.
Agility requires a stable field of optionality. But in today’s AI development cycle, agility is often an illusion.
When upstream model releases become the gravitational center, product teams are pulled into a reactive loop. Instead of responding to user behavior, roadmaps get locked to the cadence of system updates, GPT releases, Claude capabilities, or the latest embeddings API. You don’t iterate. You orbit.
We’ve seen this play out across the industry:
A productivity SaaS team postponed their onboarding revamp because GPT-5.5 was rumored to support multi-modal memory, which would “change everything.” Weeks later, the release didn’t land, and the backlog was frozen in speculative anticipation.
Scenario 1: OpenAI API instability derailed product roadmaps
In 2023, several AI tools like Notion AI, Jasper, and Copy.ai ran into the same wall: frequent changes in API token pricing, rate limits, and model behaviors. Teams that built around GPT-4 or 3.5-turbo found themselves forced to delay launches or rewrite entire prompt architectures. Agility didn’t matter, when upstream behavior is unpredictable, speed becomes irrelevant.
Scenario 2: Twitter API pricing shift wiped out an entire product category
Products like Tweet Hunter and Typefully built growth tools around Twitter’s API, enabling real-time content analysis and automation. After Elon Musk’s acquisition, API access was heavily restricted and monetized. Many tools relying on automated posting or engagement insights were forced to pivot or shut down entirely. Agility only works when there’s still something to adapt to. Once the options are gone, it’s not agility, it’s survival mode.
Scenario 3: Figma’s plugin policy change killed indie developer momentum
Figma once allowed high plugin flexibility, enabling solo builders and startups to launch popular tools, from design asset libraries to one-click templates. But in 2022, Figma changed its Marketplace policies, restricting new plugin submissions. Many creators lost distribution overnight. Even the fastest teams couldn’t “iterate” their way out. There simply wasn’t a viable route left to take.
When value is rapidly migrating across industries and between firms, proactively substituting key elements of the primary business model provides a better fit with the new value landscape than launching secondary business models in parallel.
(Bjorkdahl & Holmén, 2017)
In AI ecosystems, this means rebuilding the operational core, not adding endpoints.
In this environment, product management turns into risk arbitration.
As model capabilities expand, the application layer loses differentiation.
Features that once stood as core advantages collapse into defaults embedded at the infrastructure level.
Users bypass intermediaries and interact directly with platform-native solutions.
What What What - by Ryoji Arai
Capital vs Conversion Limits
Raising capital doesn’t translate to leverage.
Foundation model development depends on capital-intensive infrastructure, data centers, GPUs, massive compute, exclusive datasets.
Building models like GPT-4, Claude 3, or Gemini isn’t a product decision. It’s a capital game. Most companies are not invited.
Even if your product generates revenue, it cannot buy access to control layers, the critical levers that actually govern model behavior and output logic. Control layers include things like:
How much you can customize API responses
The timing and priority of model updates
The ability to calibrate output trustworthiness (alignment controls)
Most importantly, the training data and model parameters themselves
These controls sit tightly in the hands of a few model developers. Your product can use the model, but it can’t dictate its rules.
Because of this, raising capital doesn’t directly translate into leverage or influence over the platform. API pricing, token usage costs, and compliance complexity all squeeze the capital-to-output ratio.
Scale can extend survival, but it rarely shifts trajectory. In a centralized compute landscape, your levers are limited. You can maintain operations but not reshape your position in the ecosystem.
And as Hacklin et al. (2018) point out: “Adding parallel strategies won’t shift your orbit. The only thing that moves your position is structural reallocation of the core.”
Their study—based on 14 case studies and 68 interviews—outlines four mechanisms that move the needle: Firm-market matching, resource redeployment, attention steering, and complexity de-escalation.
In short: depth wins when scale is capped.
Behind the surface, this reflects a deeper shift in how innovation resources are reallocated and refocused within organizations.
It’s not just about launching new features, it’s about how internal gravity shifts.
According to Hacklin et al. (2018), four mechanisms drive this type of structural realignment:
Firm-market matching
The tendency for firms to adapt their offerings, market definitions, and target segments in response to structural industry shifts.
Companies start redefining the problem they solve and the language they use.
Product features take a backseat to new frames like “co-pilot,” “decision engine,” or “autonomous workflow”, not because they’re trendy, but because that’s what the market now understands as relevant.
It’s less about what you build, more about how you frame it inside a new industrial logic.
Resource Redeployment
Reallocating people, capital, and capabilities toward initiatives that are perceived as more promising in light of changes.
As innovation gains narrative gravity, teams begin to shift.
Infra engineers get pulled into the AI squad. Legal and ops resources pivot toward compliance for LLM deployments. These aren't official org chart moves, they’re more like internal venture capital bets that naturally follow signal strength.
Attention Steering
Managers and employees become increasingly focused on emerging domains, redirecting problem-solving efforts and conversations.
The Slack threads, team standups, and roadmap priorities start orbiting a new center.
Even if legacy products aren’t shut down, they quietly lose strategic mindshare. Nobody says it, but the attention is elsewhere, and attention is what drives energy.
Complexity De-escalation
Simplifying or discontinuing existing initiatives to free up capacity for emergent priorities.
Not all resources can be repurposed. Some legacy systems are too bloated, PMF too fuzzy, or debt too deep to salvage.
So teams make the call: kill the project. Not because it failed, but because the cost of maintaining it blocks the real work.
These four dynamics aren’t written into roadmaps, but they shape them.
They’re how platform gravity works on the inside: through budget shifts, reorg murmurs, calendar compression, and quiet resource drain.
AI isn’t just a tool, it’s a reconfiguration force.
Hacklin et al. (2018) identify four core mechanisms, Firm-market matching, Resource Redeployment, Attention steering, and Complexity de-escalation, that drive successful business model adaptation during rapid value migration
Orbital Hierarchies of Ecosystems
There’s a hidden gravity in the AI industry, one that most product teams rarely name out loud.
Unlike consumer tech, where speed comes from user data, or enterprise SaaS, where roadmap is shaped by customer demands, AI products move to the rhythm of upstream model updates. Your product development cadence no longer belongs to your team. It belongs to OpenAI’s API changelog. Anthropic’s next paper. Google’s model card release.
This shift reorders the entire logic of product building. Suddenly, your iteration isn’t driven by user signals. It’s driven by research breakthroughs. What used to be a feedback loop is now a dependency chain, and that chain sets your tempo.
The result? Teams aren’t building in an open field. They’re building on tectonic plates that shift without warning. It’s not just about keeping up. It’s about staying stable while the ground moves beneath you.
Left by Mrzyk & Moriceau, Right by David Shrigley
Systemic Dependency
Platform governance defines your risk topology.
Model versions, API limits, and policy shifts aren’t just operational concerns, they’re existential.
In traditional product development, agile works because your product decisions are grounded in a stable set of choices, what users do, what you can build, and what the system can handle.
But with AI-native products, that stability vanishes.
When your roadmap depends on OpenAI’s next release, rather than your users' needs, product innovation shifts from being user-led to model-led.
What used to be a feedback loop between product and user has shifted. You're now aligning your roadmap with the timing and direction of upstream model changes, not user needs.
Strategic Response
The industry is entering a phase where traditional defensibility no longer holds. As large foundation models become the new upstream gravity wells, media and AI infrastructure startups face a binary choice: adapt or dissolve into abstraction. Strategic responses are less about incremental optimization and more about systemic repositioning.
“We suggest four underlying mechanisms that link business model innovation, value migration and subsequent outcomes.”（Strategies for business model innovation: How firms reel in migrating value）
After dozens of conversations and experiments, four patterns keep showing up.
Wrapper Mode:
Ship fast, ship first. These teams take the output of LLM APIs and wrap it in a UX layer, often without differentiation. The moat here is speed, not value, and the business risk compounds over time as upstream platforms start to integrate the same capabilities natively.
Promptware Mode:
Instead of building net-new experiences, products compete on prompt design. Teams turn into prompt engineers, but their differentiation is ephemeral. There’s no control over quality decay, and latency or reliability still depends on upstream providers.
AI as Interface:
This group rethinks core interactions, turning structured inputs into conversations, and dashboards into copilots. They rebuild workflows, not just features. But success here hinges on having proprietary distribution or highly engaged users, or else the UX gains aren’t enough to defend against commoditization.
Model Shaping: The Next Strategic Moat
Only a handful of companies can truly shape foundation models. Not just use them, but actively influence how they behave.
The rarest and riskiest path, using proprietary data or usage signals to influence or fine-tune upstream model behavior. It’s a bet on long-term defensibility, and it only works when you own the input layer and can shape demand loops (e.g. via volume, relevance, or trust). Few early-stage teams have this leverage.
To do this, two things must be true:
You control the input layer
You own a steady stream of user-generated inputs, what people ask, how they phrase it, and the volume at which they engage.You generate a self-reinforcing demand loop
Your product doesn’t just attract users. It becomes a feedback engine that foundation models want to learn from.
This isn’t prompt engineering. It’s training influence, changing the upstream model by controlling the inputs it values most.
Inverting the Stack: From API User to Data Source
Most companies are downstream:
→ They consume APIs.
→ They adapt to model changes.
→ They have zero leverage over the model's direction.
But the few who own the data layer flip the stack:
Reddit trains models on its threads, and now licenses that data to OpenAI.
Perplexity captures real-time search queries and click signals, prime data for tuning retrieval and ranking models.
Hugging Face isn’t just hosting models, it’s defining the language around datasets, prompts, and evaluation.
What This Builds: A Moat Based on Upstream Influence
When you control inputs that models need to evolve, you gain:
Negotiation leverage
(Licensing, partnerships, data exclusivity)Strategic defensibility
(Models trained on your ecosystem behave closer to your UX patterns)Pacing power
(You’re less affected by upstream volatility, you help drive it)
In a world where model capabilities are commoditizing,
influence becomes the new infrastructure.
It’s not only products; we this generation are constantly going through our own process of finding product-market fit.
Complexity is inherent in any meaningful transformation, and the path forward is rarely clear from the outset.
History reminds us that vast, intricate systems rarely result from careful blueprinting; instead, they emerge through ongoing negotiation between countless actors and shifting incentives.
Openness isn’t optional, it’s the foundation for innovation. A future controlled by a single dominant player, making deals behind closed doors and exploiting leftover scraps, would be far less dynamic than one fueled by competitive markets, transparent incentives, and shared participation.
The Resilience Equation
Survival Strength = (How Deeply You're Woven into Workflows)² × Control Over Your Data × Speed of Core Reinvention
When the ground shifts beneath us, products rooted in users' daily operations don't just survive, they become the new landscape. This isn't about chasing agility. It's the art of redesigning your place in the ecosystem.
Product-market fit isn’t a fixed point anymore, it’s an ongoing process of adapting as the market and rules constantly shift beneath you.
Anchor Articles and Updates
How AI Platforms Reshape Innovation: Subordination, Risk, and Workflow Strategy — Rethinking Product Strategy Under Model Governance
How AI Understands User Intent Before They Search: Commercial Decision Loops and the Invisible Funnel — Why pre-intent behavior is becoming more legible and monetizable
Inspired by Lenny’s Growth Inflections: AI and Personal Brands Rewiring Startup Growth — AI automation and personal brands are redefining startup growth inspired by Lenny Rachitsky’s Growth Inflections.
Why Growth Marketing Is Not Digital Marketing and Why This Distinction Matters — It’s not that your marketing strategy is flawed. You might just be addressing the wrong problem.
When AI Products Can’t Find PMF, Build a Landing Client Instead — PMF isn’t always found in the product, Sometimes, it starts with one strategic client
Content as a Revenue Tool: Shortening Time-to-Close in Startup Sales — Content that shortens sales cycles, Not just builds traffic
Building Revenue Systems When Scale Isn’t an Option — Profitability First: How Startup Teams Can Drive Revenue in Constrained Markets
Case Studies
Mountain Gentleman — They knew they needed to go digital but had no idea how to start.So we saw things through the rider’s eyes.It wasn’t just about buying gear because it felt like building out your dream GTR.Every part of the journey was designed to match that thrill.
CoinRank — CoinRank needed a fresh way to stand out in crypto. We created a short video strategy that turns complex info into quick, engaging clips that grab attention fast.
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