The AI Inflection Point and Tech Debt
Three words that are on the minds of AI builders right now are Model Context Protocol, abbreviated as MCP. Heralded as the “HTTP of AI”, MCP is a public and open set of protocols that can be used to dictate how an AI model interacts with other digital actors — websites, applications, databases, and other AI models. The cool kids are all busy building MCP servers. These act as gateways and traffic cops for AI use, ensuring that an AI agent or application can only access intended data and only perform permitted actions.
Protocols are a big deal. They make it far more viable to create economies of scale and system efficiencies. HTTP — Hypertext Transfer Protocol — is the core protocol that allows browsers to talk to web servers and properly represent information on user devices. The development and deployment of HTTP enabled the web as we know it.
Until MCP, there was no core common protocol for AI interchange. Without this, interoperability at scale can’t happen. The AI community appears to be strongly embracing MCP and even building additional protocols for more specific use cases on top of MCP.
One such complementary protocol gaining attention is the Agent2Agent (A2A) Protocol, recently announced by Microsoft and Google. While MCP focuses on governing how AI models interact broadly with all kinds of digital actors, from apps to databases to other AI models, A2A zeroes in on the communication between autonomous AI agents themselves. Think of MCP as the city’s traffic control system, managing the flow between all vehicles and pedestrians, while A2A is the dedicated communication channel between a fleet of driverless cars coordinating their moves. Both are crucial, but they operate at different layers of the AI ecosystem.
Meanwhile, we’re already seeing the web’s role shift beneath our feet. It’s becoming middleware, no longer just a destination or display layer, but also the connective tissue linking AI to every screen, speaker, and device. This transformation underscores why protocols like MCP are essential: to manage and secure this sprawling, AI-driven ecosystem.
With that foundation in place, why does MCP matter so much? Beyond making it easier to secure and control AI applications and dictate permitted behaviors, MCP represents a key inflection point for the ecosystem. Major inflection points are among the most critical periods for determining how much tech debt your organization will carry going forward. As we rapidly move towards infusing artificial intelligence into every application (and possibly the majority of workflows), Progressive boards and CEOs recognize we are now squarely in the middle of one of the most significant inflection points in technology history. History shows that the technology choices you make during inflection points can determine the future of your technology capabilities — and possibly the success of your product and company.
Lessons from Previous Inflections: Web and Mobile
During the early days of the Internet, building a website was expensive. There were far fewer reusable components. Developers had to build now common capabilities like payments and log-ins from scratch. Over time, open source developers built out standardized modules and reusable code for most of these common services, but in the beginning, companies that wanted to build cutting-edge websites paid a lot of money and spent a lot of time and resources to keep these sites running. Today there are many tools that make building a compelling and sophisticated website or commerce site possible for non-tech people. Something similar is happening with AI.
Then came smartphones, led by the iPhone. Eventually, the primary entry point for the majority of web searches and interactions shifted to the handset. Many organizations had to dramatically alter their websites by removing larger images or offering multiple sizes of images, streamlining code, and designing for a mobile form factor. Then, native mobile applications — designed first to run on the phone and to live in an app store — gathered momentum. So organizations had to figure out how to provide websites for laptop and desktop users, mobile web users, and mobile app users. Often, this meant effectively building three different applications.
For most enterprises, this was unsustainable — costly, cumbersome, and a security nightmare. Yet, CTOs had no choice in many cases because users and customers came from all three platforms. Large e-commerce providers like Amazon and Walmart could not dictate what platform a user tapped for online shopping.
The emergence of this “three-headed monster” forced a hard reckoning. The best technology teams redesigned applications to make components more modular. The front-end might be three different code bases, but the backend was served by the same middleware and databases, publishing information to the front-end via common APIs. How well teams — and, by extension, their technology leaders — solved the three-headed monster problem often had a significant impact on their future. Even today, some companies have mobile websites that are downright awful, and they have entirely different development teams not only for desktop and mobile but also for iOS and Android, and very different core components.
AI’s Emerging Tech Debt Challenge
With AI, right now, we face a similar risk. To be clear, the three-headed monster problem will likely be different for AI. APIs are already established as the core mechanism by which AI applications communicate, and API protocols like REST, gRPC, and GraphQL are mature. Most AI apps benefit from reused software components, such as open-source reverse proxies or Kubernetes to manage containers. That said, there is a huge variety of AI tool chains and AI components. For the recent launch of Qwen3, the Alibaba development team wanted comprehensive coverage of major toolchains for AI, nearly a dozen in all. This was a relatively big lift. Similarly, inside an organization, some teams may be using one AI framework, like PyTorch, and others may be using another, like TensorFlow.
At this point, we are still too early in AI to have real confidence in what tools, practices, and protocols will likely prevail and gain stability and acceptance over the next few years. This is precisely why risks of technology debt accumulation and building in ways that institute unsustainable technology practices are always greatest around inflection points. The pressure is on to build cool AI applications and create an AI infrastructure. Many CIOs and CTOs have been holding back, and for good reason. Build too much, too fast, and in the wrong way, and you will be paying for it for a very long time. Here are some ways we recommend CIOs, CTOs, and their teams think about this topic (and what CEOs can also ask their technology leadership).
More articles from Techquity:
Hidden Tech Debt: Data Governance and Compliance
AI’s hunger for data is insatiable, and with it comes a hidden tech debt few leaders spot early enough. As AI systems gulp down vast troves of sensitive personal and business data, the stakes around governance skyrocket. Neglecting this area isn’t just a compliance risk, it's a fast track to buried liabilities that will haunt your infrastructure, your brand, and your bottom line.
Building AI responsibly means baking privacy, rights, and auditability into every layer from data ingestion to model training and inference. Think clear data lineage and airtight audit trails: every piece of data needs a story you can trace and explain. With regulations like GDPR, CCPA, and HIPAA tightening their grip, companies must bake compliance and bias controls into their AI workflows, not bolt them on after the fact.
Good governance isn’t just a tech problem; it’s a company-wide muscle. CIOs and CTOs should rally legal, compliance, and engineering to co-own adaptable guardrails that evolve with your AI stack. Tools that automate policy enforcement—especially those tied into emerging protocols like MCP can turn governance from a bottleneck into a strength.
Ignore these signals, and you’ll be building on quicksand: an AI foundation riddled with invisible risks that will compound with every new model and dataset you add.
The People Side of the AI Inflection: Talent and Readiness
Technology inflections don’t happen in a vacuum, they test the readiness and adaptability of the people and teams that build and operate systems. AI is no exception. Successful AI adoption depends not just on technology choices but on equipping talent with the right skills, tools, and mindset.
CIOs and CTOs should prioritize upskilling engineering, product, and data teams in emerging AI frameworks, model lifecycle management, and integration with standards like MCP. Building cross-functional AI teams or centers of excellence helps standardize best practices and breaks down silos that otherwise lead to duplicated effort and incompatible toolchains.
It’s equally important that product managers and business leaders understand AI’s capabilities and limitations so they can set realistic expectations and foster collaboration across technical and business functions. Without clear alignment and defined roles, AI initiatives risk creating technical debt through rushed or uncoordinated development.
Forward-thinking organizations are also creating new roles, such as Chief AI Officers (often combined with Chief Data Officer), AI ethics officers, and AI operations specialists to provide focused leadership, oversee governance and compliance, and manage ongoing model health. Preparing for AI’s demands means planning for change, encouraging continuous learning, and embedding AI into existing processes, not simply layering new technology onto legacy systems.
The momentum behind AI adoption is undeniable, and the data proves it. Leading venture capital firm Andreessen Horowitz analyzed Q4 2024 earnings calls from top software, security, and cloud companies, revealing that AI isn’t just launching—it’s scaling at breakneck speed. Their key signals include:
100%+ quarter-over-quarter growth for new AI SKUs
Emerging pricing models tailored specifically for AI offerings
Rapid infrastructure shifts coupled with accelerating agentic AI use cases
CIOs, CTOs, CEOs and Boards must respond and invest in AI. But they must invest strategically, build sustainable architectures, and prioritize governance and organizational readiness to seize the opportunity without drowning in tech debt.
With the stakes this high, CIOs, CTOs, and other technology leaders must move beyond recognizing the challenges; they need a clear playbook to navigate this AI inflection. Success will come to those who strategically align technology, talent, and governance to build resilient, scalable AI capabilities without incurring crippling tech debt.
Recommendations for CIOs and CTOs (and Questions CEOs Should Ask)
Decide what to build: While AI will likely be added to every application as the UX layer switches to chat and agentic interactions become better understood, organizations still need to decide whether to build an AI-first or AI-enhanced product. Answering “What” is a prerequisite for a successful AI strategy.
Stand up one clear “front door” for AI: Think of an MCP gateway like the main reception desk at your headquarters—every AI system, whether built in-house or bought, must check in here. If the MCP standard changes, you will renovate one desk instead of the whole building.
Hide the brand of each AI model behind your own switchboard: Connect business software to an internal API you control—not straight to any single vendor—so you can swap models without costly rewrites when prices rise or better options appear.
Turn rules and guardrails into editable playbooks: Store “who can see what” and “what an AI is allowed to do” in version-controlled files; updating policy becomes as easy as editing a document.
Keep all your AI facts in one, well-lit warehouse: Let teams experiment with different tools, but insist that their data and search indexes live in a single, governed repository to avoid scattered “mystery closets.”
Give teams pre-packed kits for building AI features: Provide ready-made templates, cloud scripts, and security settings so engineers start fast and, by default, follow best practice—much like issuing standardized laptops.
Measure AI health in plain business terms: Dashboards should show both tech metrics (speed, cost) and AI-specific ones (accuracy, trustworthiness) so executives can spot issues, like a chatbot “hallucinating”—early.
Separate the sandbox from the factory floor: Encourage rapid experiments in a low-risk test zone, but promote only the winners, after they meet cost, security, and reliability checks, into production, where they touch customers.
Track lifetime operating costs, not just launch budgets: GPU bills and per-token fees can snowball; tie every AI initiative to a simple unit metric (e.g., cost per customer served) to ensure scaling stays economical.
Choose options that let you move house later: Favor open-source models and containerized deployments so you’re not locked into one cloud provider; assume you’ll want to relocate—or add a second site—within three years.
Inflection points are times of opportunity and peril. For smart CEOs, CTOs, and CIOs, the inflection will allow them to build distance from competitors, overhaul business practices, try out new business models, and radically reduce operational overhead. Those who see the inflection point but whiff on execution can pay for their mistake for years. Thinking through how to ride the inflection and executing well on a strategy that allows you to benefit from the upside but shields you from the downside will be the critical difference between winners and losers in the age of AI.
Andrew Tahvildary is on the leadership team at www.techquity.ai. He is the primary author of this post. Andrew is a CTO who has led 7 tech startups to successful exits, exceeding $2 billion in total transaction value.
Alex Salkever is a partner at Techquity.ai. He is the author of four award-winning books and has worked at multiple startups and scale-out technology companies over his career.
Anthony Bay is CEO and co-founder at Techquity. www.techquity.ai He has previously held senior roles at Amazon, Apple, and Microsoft as well as early-stage companies, served on multiple private and public boards, and launched the world’s first social network before the Internet even existed. He has led product groups, M&A, CEO, and board governance for everything from early-stage startups to large corporations.
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