In fields that race forward on exponential curves, the classic build-versus-buy dilemma becomes a wager on both time and learning. A team may pour heart and code into a feature only to watch the landscape shift the moment it ships. Yet speed alone is not a compass; one must weigh strategic control, talent development, and customer delight against capital, risk, and the rising tide of external offerings.

Build when the capability will anchor your long-term vision or protect you from gatekeepers who might later tighten the rules or vanish entirely. Ownership grants fine-grained tuning of the user experience, preserves your data flywheel, and trains your engineers in the very techniques that will shape the next iteration. Even if the module is thrown away in twelve months, the institutional knowledge remains, and the brand enjoys a stretch of differentiation during a critical window.

Buy when a trusted vendor already offers the function at industrial grade, and when the extra expertise you would gain by recreating it offers little strategic lift. The subscription may cost less than the payroll required to keep pace with an area that is neither core to your purpose nor likely to become so. Good external APIs also shift liability and compliance burdens—such as data handling, uptime guarantees, and evolving regulatory requirements—away from your roadmap, freeing the team to sharpen what truly sets you apart.

Do nothing — for now when incremental benefit to users is slim and the technology curve is clearly about to steepen. In such moments, restraint is a virtue: let others test the early hardware, expose the hidden costs, and validate demand. Watching from the sidelines can be an active choice, positioning you to leap once the standards settle and the tools converge.

The Law of Accelerated Returns suggests that technological progress follows an exponential trajectory. Advances in one area (such as computational power or data storage) accelerate progress in other areas, leading to faster and faster growth over time. This creates a compounding effect: as technology improves, so do the tools used to create the next generation of technologies. If large language models (LLMs) follow this pattern, we can expect exponential gains in their performance and usability, driven by both algorithmic improvements and next-gen hardware. In this setting, timing becomes not just strategic, but existential.

To illustrate how these principles play out in practice—especially within the context of rapid, exponential change—below are concrete examples from the generative-AI frontier, each mapped to one of the three paths:

1. Retrieval-augmented search layer to curb hallucinations (Build). When factual precision is vital—say, for a legal-tech product—you will want full control over data sources, indexing, and re-ranking. Even if vector DB techniques mature or LLM accuracy improves in the near future, building now enables immediate trust and control—two advantages that justify short-term investment, even if the solution is replaced within a year; meanwhile your users trust every answer.

2. Prompt-workflow orchestration service that chains calls to multiple LLMs (Buy). Several robust SaaS players already bundle guardrails, caching, and analytics. Unless orchestration is your signature magic, integrate their API and focus elsewhere. This also includes emerging standards like Model Context Protocol (MCP), which aims to modularize and coordinate interactions between different LLMs and tools. While promising, adopting MCP today is best implemented via well-established platforms or vendor ecosystems that already support MCP, rather than building your own integration layer while the protocol and best practices remain in flux.

3. Brand-specific image-style generator for marketing collateral (Build). A bespoke diffusion fine-tune locks in your visual identity and ensures consistency even as upstream models evolve.

4. Real-time voice-cloning for an audiobook feature (Buy). Vendors with large-scale speaker datasets can hit production quality today; rolling your own would add little strategic heft.

5. Synthetic data engine for training domain-specific compliance models (Build). The scenarios and edge cases you need are unique; crafting them in-house both safeguards private data and seeds future models.

6. On-device multimodal agent for AR glasses (Do nothing—for now). Chipsets and toolchains are advancing quarterly. Waiting a cycle may significantly reduce power consumption and unlock models far beyond what you could cram into today’s hardware budget.

7. Integration of Advanced AI Assistants in Consumer Devices (Do nothing—for now). Companies like Apple and Amazon have announced significant AI enhancements for their voice assistants, Siri and Alexa. However, these features have faced delays and are not yet widely available. Given the current state of development and the challenges in delivering robust AI capabilities at scale, it may be prudent to hold off on integrating such assistants into consumer devices until the technology matures and becomes more reliable.

8. Deployment of AI-native operating systems (Do nothing—for now). While related to AI assistants and on-device agents, this example offers a distinct angle: a foundational shift in the way applications and AI agents interact with hardware and the user. These operating systems could redefine interaction models entirely, but most are still experimental or suffer from ecosystem fragmentation. Waiting allows developers to avoid vendor lock-in and benefit from greater interoperability as standards emerge. Several startups and tech giants are exploring operating systems designed around generative AI and autonomous agents. While these are intriguing, most are still early in development or lack backward compatibility with established ecosystems. Waiting allows the dust to settle around interface standards, developer tools, and app ecosystems.

By matching each decision to its strategic weight—and by viewing those decisions through the lens of the Law of Accelerated Returns, which reminds us that progress compounds rapidly in high-velocity domains— by accepting that impermanence can be a feature, not a flaw—you can move with the market instead of against it, all while turning uncertainty into a disciplined advantage rooted in the logic of exponential technological growth.