As Waseem Alshikh, Co-founder and CTO of Writer, brilliantly put it: "If your enterprise AI 'strategy' is calling OpenAI's API...You don't have a strategy. You have a bill. Real winners build on a full-stack AI platform—models, data, agents, everything."
Let that sink in for a moment.
The False Economy of API Dependency
When a CTO proudly announces that their company has "implemented AI" by integrating OpenAI into customer service workflows, what's often left unsaid is the strategic tradeoff that's just been made: the company is now tethered to a single vendor's pricing model, technical roadmap, and operational stability.
This kind of API dependency introduces several significant challenges:
- Runaway Costs at Scale:What begins as a low-cost experiment can quickly spiral into a major expense. One mid-sized professional services firm I spoke with recently saw their monthly OpenAI bill jump from 130,000 in just four months—without a proportional return in customer satisfaction or revenue uplift. While these models can deliver impressive functionality, cost predictability is often elusive, especially as usage grows and pricing tiers shift.
- Erosion of Differentiation:If every bank, retailer, and healthcare provider uses the same large language model via the same API, then user experiences begin to converge. Differentiation becomes increasingly difficult when core capabilities are commoditized and centrally managed. Your customer interactions risk sounding like everyone else's—because, effectively, they are.
- Volatile Technical Ground:APIs evolve. Models get updated, decommissioned, or retrained. Output formats shift. Terms of service change. In this environment, your finely-tuned prompts and workflows can break without notice. Companies relying heavily on third-party LLMs have had to scramble after unexpected model changes that altered how responses were generated or filtered.
- Data and IP Exposure:Prompt data, user interactions, and internal knowledge shared with API-based systems may be logged and used to train future models—models that you do not control and that could end up embedded in competing products. While OpenAI and others offer opt-out mechanisms for data retention, the risks around sensitive or proprietary information remain real, particularly when operating in regulated or IP-sensitive industries.
One senior executive at a Fortune 100 retailer recently put it bluntly: "We spent millions integrating GPT-4 across our customer service operations, only to realize six months later that we were essentially subsidizing the training of a next-generation model that will now be used to power our competitors."
A Better Approach
Adopting foundation models via API can be a powerful tool—but it shouldn't be your whole stack. Companies need to think strategically about when to rent intelligence and when to build it. Fine-tuning open models, developing internal prompt engineering expertise, or even hosting self-managed LLMs are increasingly viable paths that preserve flexibility, control costs, and protect competitive advantage.
Real winners in the enterprise AI race are building full-stack AI platforms—owning not just the application layer, but the entire technological stack that creates sustainable competitive advantage.
1. Proprietary Data Architecture
Your data isn't just fuel for someone else's models—it's your most valuable strategic asset. A genuine AI strategy starts with a proprietary data architecture that:
- Captures unique data streams unavailable to competitors
- Creates virtuous data flywheels where AI applications generate more high-quality data
- Maintains control over how and where your data is used
- Synthesizes insights across previously siloed information
Anthropic, Midjourney, and Google didn't become AI powerhouses by making API calls to their competitors. They built infrastructure to collect, process, and leverage data in ways that continuously strengthen their competitive position.
2. Custom Model Development
The one-size-fits-all approach of general-purpose models is inherently limiting. Forward-thinking enterprises are:
- Fine-tuning foundation models on proprietary data
- Building specialized models for domain-specific applications
- Creating model architectures optimized for their specific use cases
- Developing evaluation frameworks that align with business outcomes, not generic benchmarks
When JPMorgan developed its own LLM (IndexGPT) for analyzing financial documents, they weren't just saving on API costs—they were building a capability that perfectly aligned with their specific business needs in ways no general-purpose model could match.
3. Integrated Agent Ecosystems
The real magic happens when individual AI capabilities are orchestrated into systems of specialized agents that work together. This means:
- Creating purpose-built AI agents with specific roles and expertise
- Developing orchestration layers that coordinate complex workflows
- Building evaluation and oversight mechanisms for agent interactions
- Designing human-AI collaboration interfaces that maximize complementary strengths
Netflix doesn't just use recommendation algorithms—they've built an ecosystem of specialized AI systems handling everything from content tagging to personalization to thumbnail selection, all working in concert to create a cohesive user experience no competitor can easily replicate.
4. Technical Infrastructure Ownership
Dependency on cloud-based API calls creates fundamental constraints on what's possible. True AI innovation requires:
- Computing infrastructure scaled to your specific needs
- Flexibility to optimize for cost, speed, or capability as business requirements dictate
- Security and compliance architectures built for your industry context
- Deployment options that span cloud, edge, and on-premise environments
Tesla didn't become an AI leader by making API calls from their cars to third-party vision systems. They built the entire stack—from custom silicon to training infrastructure to deployment pipelines—creating capabilities their competitors struggle to match.
The Strategic Imperative
Some executives will read this and think: "But building all that is expensive and time-consuming. API calls are quick and easy."
They're not wrong about the first part. Building a genuine AI capability requires investment, expertise, and organizational commitment. But they're profoundly mistaken about the long-term economics.
Consider this calculation:
A mid-sized enterprise making 10 million API calls monthly to a leading provider at current rates will spend approximately 30 million in operational expenses with:
- No asset creation
- No proprietary technology development
- No reduction in marginal costs over time
- No competitive differentiation
That same $30 million invested in building a full-stack AI platform creates:
- A proprietary technological asset with growing value
- Capabilities competitors cannot easily replicate
- Dramatically lower marginal costs for each additional use case
- Freedom from vendor dependency and pricing uncertainty
The economics become even more compelling as usage scales. From conversations with founders across different industries, I've heard of projected savings of $42 million over three years by shifting from API-based to owned AI infrastructure as usage grows.
The Execution Gap
If the strategic case is so clear, why are so many enterprises still taking the API-only approach?
The answer lies in capability gaps that exist in most traditional organizations:
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Talent shortages: Building full-stack AI requires specialized expertise across multiple disciplines—ML engineering, data architecture, infrastructure optimization, and domain knowledge.
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Organizational inertia: Existing technology organizations optimized for maintaining systems of record struggle to build systems of intelligence.
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Investment horizons: Short-term financial pressures push organizations toward immediate solutions rather than capability building.
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Leadership understanding: Many executives still see AI as a technological tool rather than a fundamental business capability.
Overcoming these gaps requires honest assessment and decisive action. Leaders need to:
- Invest in building core AI teams with the right mix of expertise
- Create organizational structures that support AI innovation
- Establish appropriate investment horizons and success metrics
- Develop their own understanding of AI capabilities and limitations
A Practical Path Forward
The good news? You don't have to build everything at once. The most successful enterprises are taking a staged approach:
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Start with data infrastructure: Before worrying about models, ensure you're capturing, organizing, and leveraging your unique data assets effectively.
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Develop domain-specific applications: Focus initial AI development efforts on narrow, high-value use cases where domain expertise creates clear advantages.
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Incrementally reduce API dependency: Use third-party APIs as scaffolding while building your own capabilities, gradually shifting workloads to proprietary systems.
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Build platform capabilities progressively: Develop reusable components, evaluation frameworks, and infrastructure that can support multiple applications.
This approach creates immediate business value while building toward sustainable competitive advantage.
The Unavoidable Conclusion
The hard truth is this: In five years, enterprises will be divided into two categories—those who built proprietary AI capabilities and those who are perpetually dependent on the companies that did.
The former will enjoy sustainable competitive advantages, continuously decreasing marginal costs, and growing technological assets. The latter will face steadily increasing operational expenses, struggle to differentiate their offerings, and remain vulnerable to vendor decisions beyond their control.
So I'll ask again, echoing Waseem Alshikh's insight: If your enterprise AI "strategy" is calling OpenAI's API, do you really have a strategy? Or just a growing line item on your cloud services bill?
The companies that will dominate their industries in the AI era aren't just consuming artificial intelligence—they're building the capability to create it. Which side of that divide will your organization be on?