"We overestimate what will happen in one year, but underestimate what will happen in ten years." - Alexandr Wang, Scale AI
The Speed of Progress
Five years ago, GPT‑2 writing a clear paragraph felt like science fiction. Today large models read contracts, watch videos, and speak back to us in real time. Each release lifts a limit we thought was fixed. If you shape your product keeping in mind what’s possible today, every time a new model is released, you’ll be refactoring all your core business logic. Wang’s point is simple: momentum matters more than snapshots. Models keep getting stronger, cheaper, and more flexible at a pace few other technologies can match. Treat today’s constraints as short‑term scaffolding, not permanent walls.
Agents Already at Work
Autonomous and multi‑agent systems are no longer prototypes. A scheduling assistant can read calendars, draft confirmations, and send them without human clicks. Banks use agent pipelines that scan transactions, raise fraud tickets, and kick off secondary checks before an analyst signs in. In a hospital an after‑hours agent reviews charts, flags risky drug combinations, and hands a summary to the morning staff. Factory agents watch hundreds of sensors, negotiate machine health among themselves, and open maintenance jobs before a belt snaps. These successes rely on models that can see images, listen to speech, parse long documents, plan step-by-step, and call external tools inside one feedback loop. If your architecture still treats an LLM as a chat box, you are leaving capabilities on the table.
Build for Tomorrow
Design for an environment where context windows grow, costs fall, and new modalities appear. Wrap model calls in a thin client, so you can switch providers with a single setting. Store full prompts and responses, because today’s logs become tomorrow’s fine‑tune data. Accept whole PDFs, hour‑long videos, or any large files your domain requires, even if you need to chunk them for now. Give agents scoped permissions, clear audit trails, and rate limits, making it easy to move from suggestion‑only to full automation when you are ready.
None of this works without visibility. A model upgrade can change latency, cost, or answer style. Good observability can ensure nothing breaks as you keep iterating. Log every prompt and response, track token spends, watch error rates, and surface any drift in behavior. With solid telemetry, whether you use tools built for AI workloads, like Instana (which, by the way, just had its 300th release!), or another observability stack, you can plug in a new model, verify that it works correctly, and roll forward confidently.
Which future capability are you already planning for?
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