Real-time AI Is Inevitable

Every AI system that replaces human interaction must operate at human latencies. This makes real-time AI inevitable, not optional.

Compared to the last decade of progress, the most recent year has seen a drastic uptick in low-latency AI products. And even though the market is finally picking up on the value of low-latency, streaming responses, the big picture is still missing from the conversation: ultra-low latency, real-time AI will be critical for building products that humans and machines interface with regularly.

These systems are a category of their own, with design and implementation constraints that are quantitatively different from other low-latency but interactive systems. The market is converging on this reality, and the shift will change everything about how AI products are built.

For decades, batch (or “offline”) processing was the standard. You’d submit a job and wait minutes, hours, or even days for it to complete. You had no other option but to deliberately context-switch and find something else to do while you waited. If something failed, you’d have to try again and hope you didn’t waste another day. Batch processing is cost-efficient because you can optimally schedule work and keep compute costs rock-bottom. Continuous log processing, aggregation, and AI-enabled web scraping are good examples where minutes or hours of delay are fine.

But not all work can be batched. Sometimes you want an answer right away. Searching the web, checking traffic, looking at weather forecasts, or chatting with a text bot for support are interactive systems. They’re turn-based: you do something, then wait for your turn again. Users can tolerate waiting a few seconds without losing mental context, but after about 10 seconds attention starts to drift. Around 30 seconds, you risk losing engagement entirely.

And then there’s real-time. Human conversation doesn’t tolerate several seconds of silence between turns. If a voice agent or IVR system pauses for 10 seconds before responding, the interaction feels broken, and customers hang up. Real-time processing typically operates in the 10ms–1000ms range. At ~100ms, responses feel instantaneous; between 500–600ms, they start to feel magical; in voice AI, ~300ms is a reasonable cutoff.

There’s plenty of evidence that real-time latencies need to be sub-second.

• Web performance: Akamai found that 100ms delays hurt conversion rates by 7%, and two-second delays cause 103% increases in bounce rates.
• Google responsiveness: 200ms is their cutoff for a site feeling responsive.
• Voice AI: Waiting more than 500-1000ms for TTS to start breaks the illusion of natural conversation.
• Autonomous systems: Whether road vehicles, warehouse robotics, or surgical assistants, if they can’t respond to external stimuli in ~200ms, the consequences can be catastrophic.
• Gaming: Next-generation games must generate NPC content in real time; if characters stare blankly for seconds, players disengage.
• Creative tools: If every small tweak takes five seconds to render, creators abandon the product.

And voice isn’t the only application. Autonomous vehicles require near-instant perception and reaction to avoid accidents. AI surgical assistants must identify anomalies in under a second or risk patient harm. Content creation tools need to collaborate fluidly with creators, and five seconds per action is a non-starter.

The demand for streaming systems capable of real-time responses is accelerating faster than for batch systems. Whichever way you cut it, real-time AI is the AI of the future.

If AI’s utility is to increase human productivity by replacing or enhancing human effort, it must operate within human response times. Much of our daily interaction is, in fact, real-time. If it weren’t, we’d be in a constant state of interruption, incapable of maintaining the focus needed for meaningful work. Research on this has been clear for decades, from Robert Miller’s seminal work Response time in man-computer conversational transactions to Jakob Nielsen's 1993 book Usability Engineering.

Real-time AI has three non-negotiable constraints:

• Latency - The defining constraint. Latencies above 1 second are tantamount to a service outage. In many cases, even 500ms is too slow to feel natural. To meet user expectations, you have to optimize every stage of the pipeline (data ingest-> pre-processing->inference->post-processing->output) for absolute minimum delay.
• Efficiency – Reaction speed isn’t enough. A platform must deliver enterprise-grade uptime, support, security, and pricing that works at scale. Efficiency is what keeps performance and costs balanced as deployments grow to serve large, demanding workloads. In real-time AI, efficiency is harder to achieve because you can’t rely on large batch sizes for cost savings. Every inference must be completed quickly, even if that means running less-than-optimal batch sizes.
• Feature Coverage - Real-time AI must work across multiple verticals, languages, and outputs. If you can’t expand feature coverage without starting over, you’ll lose to platforms that can. This means designing models and systems so they can adapt to new domains and integrate with downstream applications without architectural rewrites.

There’s some flexibility: an application might trade slightly higher latency for lower cost, or delay certain feature expansions until after launch. But if a roadmap doesn’t prioritize latency, efficiency, and coverage in that order, it risks falling behind. In the long run, all three must be in place to maintain a competitive edge.

Just because a system is “low-latency” doesn’t mean it’s real-time AI.

You can’t take off-the-shelf, general-purpose models and inference engines and expect them to hit true real-time performance. The architecture, data pipeline, and model design must be built for streaming from day one. Many companies attempt to lower latency by shrinking models, sacrificing accuracy. Others depend on cloud auto-scaling, until they hit GPU availability limits in a region. Even then, margins can tank by 4x or more. Without ongoing engagement with real-world use cases, access to diverse production data, and a foundation designed for latency, efficiency, and coverage from the start, what you have is a demo, not a platform.

The reality is that true real-time AI requires:

• Models built for streaming inputs and streaming outputs, not just fast inference.
• Inference engines capable of context-switching across thousands of simultaneous requests without dropping latency.
• Stateful architectures that incorporate new context on the fly, rather than processing each chunk of data in isolation.
• Compute efficiency far beyond general-purpose frameworks, because you can’t rely on batch-size optimizations.
• Protocols that are streaming, bidirectional, and forward-flowing, tolerating packet loss without sacrificing responsiveness.

Nobody has built LLMs that stream data in and out simultaneously. Nobody has multimodal models that stream both ways. Nobody has models that can re-contextualize mid-stream.

These capabilities require solving for bidirectional streaming, hierarchical context management, and ultra-efficient inference scheduling, all at once. This is an engineering challenge few companies are positioned to meet.

At Deepgram, we have spent years building for this moment: a scalable platform, world-class real-time models, and a self-owned data strategy. Our architecture supports sub-300ms STT and sub-200ms TTS latencies, with the efficiency to sustain enterprise-scale deployments. We have designed our models, inference engine, and hardware strategy together so each reinforces the others, delivering unmatched performance in latency, efficiency, and feature coverage.

Real-time AI is not just another use case; it is an entire category with more complex and demanding requirements than batch or interactive AI. To succeed, your architecture must be real-time by design, from protocols and pipelines to state management and inference orchestration, and built to deliver the same unmatched latency, efficiency, and feature coverage at scale.

The window to define this category is short-lived. The shift is inevitable. The only question is who will build the platforms that lead it.