Tom Zheng is the co-founder and CTO of Clado, a company focused on reliable, cost-efficient people search infrastructure for recruiting.
When executives hear “scale AI,” the instinct is to think about compute: more GPUs, more servers, bigger clusters. But when it comes to large language model (LLM) powered products, raw horsepower isn’t what unlocks efficiency or customer value.
The real unlock comes from concurrency: running multiple LLM calls in parallel, distributing work across agents and stitching results together. Concurrency is how AI systems get from “smart demos” to enterprise-grade reliability.
The Problem With Sequential AI
Traditional application design often runs tasks step-by-step: fetch the data, run the analysis, then summarize results. For AI workloads, that approach quickly breaks down.
Take a customer support chatbot. A single customer query might require:
• Searching knowledge bases
• Checking account details
• Drafting a response
• Running compliance checks.
If those steps run sequentially, the user waits. And every extra second erodes trust. Worse, sequential execution ties cost directly to wall-clock time. More requests mean longer queues and slower performance.
Taking a look at technology before AI, companies like Google don’t fetch one web page at a time; they query thousands of index shards concurrently. That’s why results return in fractions of a second. AI systems need the same mindset.
Why Concurrency Wins In The Market
1. Faster Time To Answer
By launching multiple LLM calls at once, systems reduce total latency. Imagine asking an AI to compare 20 competitors. Sequentially, it might take a minute. Concurrently, the calls run in parallel and return all of them in seconds. Over time, this adds up to halving or quartering the total time taken to run tasks.
This is how we at Clado scale people search: We fan out requests across multiple search pipelines and validate them concurrently, so a recruiter gets results in real time rather than waiting for one candidate at a time.
2. Better Accuracy Through Cross-Checking
Concurrency allows “ensemble” reasoning. Different LLM calls can tackle the same problem from different angles—one summarizing, one fact-checking, another retrieving references. The system then reconciles them.
It’s like getting a panel of experts to weigh in at once instead of polling them one after the other. The result is both faster and more trustworthy.
3. Cost Efficiency Through Distribution
Counterintuitively, concurrency often saves money. Parallelizing lets systems cut wasted tokens: caching overlapping results, stopping early when confidence thresholds are reached and avoiding duplicate long chains.
For example, instead of running a 30-step reasoning chain sequentially (with failure at step 27 wasting everything), concurrent execution can branch, return early and reuse partial outputs—so you spend less per successful answer.
4. Reliability At Enterprise Scale
Concurrency provides fault tolerance. If one provider stalls or times out, others can continue in parallel. The user still gets an answer. This is how high-availability systems in finance and telecoms have always worked—and it’s becoming table stakes for AI infrastructure, too. In the pre-AI era, concurrency infrastructure was crucial to the success of companies like Netflix, which was not the first but served up extremely reliable systems. When you hit play on Netflix, data is fetched in parallel from multiple CDNs (locations). That concurrency is what ensures smooth playback even when one server falters.
Lessons For Technology Leaders
Here are the practices that consistently separate concurrency-first companies from the rest:
Tokens are money. Treat every model token as if it were a dollar. Caching and reusing partial results avoid waste. Early stopping rules prevent runaway requests. Example: Instead of re-summarizing the same annual report 50 times, cache the summary and pull it instantly.
Separate work types. Light requests shouldn’t compete with heavy ones. Apply stricter rate limits to resource-intensive queries.
Observe relentlessly. Log not just latency and throughput, but cost per successful action and failure rates by workload type. LLM observability is crucial to understanding where to optimize within systems and why.
Design for change. Fixing one bottleneck exposes the next. That’s the point. A concurrency-first design doesn’t just survive bottlenecks; it evolves past them.
The Executive Checklist
If you’re leading an AI initiative, here are five questions to ask your team this week:
1. What is our cost per successful action, and how does it scale with concurrent users?
2. Are expensive operations rate-limited differently from everyday ones?
3. How do we cache intermediate results to avoid recomputing the same answers?
The companies that win in AI won’t be the ones with the biggest GPU clusters. They’ll be the ones who orchestrate work the smartest.
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