“It’s Hunger Games for tokens in our front offices.” — CDAO, Enterprise Bank
Intelligence is made—by factories that generate tokens. Tokens are the building blocks of AI. They transform raw data into foresight, decode the laws of physics, and connect the dots across space, science, and life. Every new token is a step into a world of extraordinary possibility- Nvidia: Jensen Huang’s GTC25 Keynote
Data arrives as tokens, models consume and generate tokens, and—crucially—profits flow to those who act on tokens faster than their competitors.
This begins well before execution, at the research layer—where firms compete to ingest, clean, label, and tokenize market and alternative data faster than their peers. From research to simulation to signal generation to trade execution, every phase is now part of a token-driven arms race.
This hunger reflects a real race to gain information advantage despite the high cost of large-scale AI. The fuel for these AI factories (compute and data) is expensive, and only clever optimization and speed can tip the balance.
In this article, we introduce token latency and token half-life as critical metrics for AI effectiveness in trading systems and explore how mastering them can redefine Sharpe ratios, alpha generation, and determine who wins the quant arms race.
The Race for Tokens in AI-Driven Trading
In enterprise AI and algorithmic trading, the competition for tokens has become a defining feature of the landscape—a "Hunger Games" for AI tokens. Here, tokens can mean anything from chunks of text and numbers that large language models process, to micro-batches of market data an algorithmic strategy ingests.
The analogy highlights both business leaders' eagerness to capitalize on AI and the reality that AI resources are finite. In trading, this translates to a fierce contest: who can gather more informative data points, process them into predictive signals, and execute trades faster?
Jensen Huang's vision that every company will run an "AI factory" producing streams of tokens is manifesting on trading floors as real-time analytics pipelines. These AI factories take in raw material (market data, news, order flow) and output tokens—predictions, risk estimates, optimal orders—that drive decisions.
Crucially, it's not just about volume, but freshness. If data is the new oil, then tokens are refined fuel — and like any fuel, they lose potency over time. This is where token latency and token half-life come in as core yardsticks for performance.
Modern AI techniques are voracious in their token consumption. Advanced reasoning methods may demand 100× more tokens for inference than simpler approaches. This exploding token requirement raises the stakes: every millisecond of processing can improve an AI's intelligence—or, if slow, become a bottleneck.
Let's define our two key metrics:
Token Latency: the time from data arrival to action – how long it takes your pipeline to turn new information into a decision.
Token Half-Life: the time it takes for the predictive utility of a token to decay by 50%. This measures how quickly an edge "goes stale."
From Quant Research to Real-Time Trading
Before a single trade is executed, the battle for alpha begins in quant research.
AI token flows are not only critical during execution, but also during the upstream stages of signal discovery, model training, backtesting, and simulation. Quant research teams consume large volumes of data—market data, alternative data, macro indicators—transforming them into tokenized representations for modeling and inference.
These research pipelines continuously experiment with strategy hypotheses, model architectures, and features. The resulting models are stress-tested, optimized, and ultimately deployed downstream into real-time trading systems, where they must operate at extremely low latency.
This handoff from research to trading is where the AI Time Factory becomes essential. It ensures that strategies developed in research are embedded in execution infrastructure with minimal friction. Each insight generated in research becomes a streaming token consumed in production, where it's either acted on or discarded in milliseconds.
Token latency and token half-life apply equally across this lifecycle. Latency in research affects time-to-alpha. Latency in trading affects alpha capture. Together, they define the firm's competitive rhythm.
Token Latency – From Data to Decision in Milliseconds
Token latency is a measure of speed through the AI pipeline. Imagine a market event just occurred – how quickly can your algorithms incorporate that into their internal state and output an action?
In high-frequency trading (HFT), this latency is measured in microseconds. Even in less frenzied strategies, latency matters: delays of seconds in reacting to new data can mean the difference between capturing an inefficiency or chasing a moved market.
Why does speed matter so much? Because market signals are perishable. If your AI recognizes a pattern, every microsecond of delay increases the chance someone else will act first. As one quant put it, "even microsecond delays can kill alpha in HFT."
Reducing token latency is therefore a prime objective in modern quant infrastructure. It involves optimizing every stage: data capture, preprocessing, feature generation, model inference, and order execution.
For example, processing and computing AI models as part of the data stream—within a high-performance, in-memory environment—eliminates needless network hops and context switching. By embedding AI inference directly into the real-time data pipeline, firms can trigger predictions the instant new data arrives, maximizing responsiveness and minimizing friction.
The goal is a seamless pipeline where data flows through transformations with minimal friction. By cutting out intermediate steps, one can shrink the latency from 100 milliseconds to 1 millisecond – a massive edge.
Lower token latency directly translates into reduced opportunity cost. If your system can react faster, it seizes opportunities that slower rivals miss. For a portfolio manager, this means fewer missed trades and less slippage. For a market maker, it means being the first to update quotes when conditions change.
Finally, token latency isn't just about trading execution; it also affects time-to-alpha in strategy development. Think about how quickly you can go from an idea to a live trading signal. Streamlining data science workflows reduces this research latency. The firm with an AI "time factory" can iterate on ideas faster, turning them into alpha before others do.
Token Half-Life – The Decay of Predictive Power
If token latency is about race time, token half-life is about shelf life. It quantifies how quickly the predictive value of a token deteriorates. This concept is analogous to radioactive decay: a freshly generated prediction has maximum potency at time zero, but as time passes, that potency decays as the market absorbs the information.
In quant trading, practitioners refer to alpha decay – the idea that a trading signal's edge fades over time. "Welcome to the world of alpha decay," one quant quips, where an edge you thought you had is already gone by the time you trade it.
Research confirms what traders know anecdotally: signals lose predictive power rapidly. A strategy that backtested well last quarter might be arbitraged away by this quarter. A news sentiment signal might have a useful life measured in minutes before prices reflect it. Alpha isn't static; it's a melting ice cube.
Several forces drive this decay:
Competition (many traders chasing the same signal)
Data dilution (new data arrives, making older data less relevant)
We can formalize the decay: imagine a signal's predictive power decays exponentially over time. If P(0)=1.0 (100% initial power), and the half-life is T₁/₂, then by time T₁/₂ the power is 0.5. By time 2T₁/₂, it's 0.25, and so on. The shorter the half-life, the more quickly you need to act before the signal is mostly noise.
In practical terms, token half-life can be measured by delaying signals and seeing how performance drops. If a one-minute delay cuts profitability in half, you know your signal's half-life is extremely short – a mandate to minimize latency.
Short half-life signals demand rapid execution and higher turnover. Longer half-life signals are more forgiving on speed, but even they degrade if too many others catch on.
Token half-life defines the urgency of action: a trading system must convert tokens to trades faster than the signal decays. This is intimately tied to token latency: if your token latency is longer than the token's half-life, you're trading mostly noise. Ideally, token latency << token half-life to capture the majority of the available alpha.
Redefining Sharpe and Alpha in the Time Domain
These two concepts – token latency and token half-life – provide a new lens to view traditional performance metrics like Sharpe ratio and alpha generation.
By accelerating token throughput – i.e., the number of tokens processed and decisions made per unit time – a firm can increase the number of independent bets it places. Each small, fast trade might have modest profit, but collectively, hundreds of such micro-alpha trades per day can compound to an impressive Sharpe ratio.
High throughput combined with low latency and a decent success rate means more bites at the apple. Instead of 5 big trades a day, an AI system might execute 500 tiny trades, each exploiting a fleeting imbalance. If those trades are weakly correlated, the law of large numbers smooths out the equity curve – resulting in a higher Sharpe.
Sharpe ratio also benefits from lower latency by reducing the variance of outcomes. A faster system hits the mark more precisely, leading to returns closer to the model's expectation. This improves the consistency of returns, boosting Sharpe.
Alpha generation itself is redefined when we focus on time. Even the best model, if applied too slowly, yields zero alpha.
Incorporating token half-life, we think of alpha in units of alpha-per-time. A fast pipeline effectively lengthens the useful horizon of a signal – you exploit it at full strength, rather than at half strength. This means you extract more total return from each signal before it dissipates.
A high-throughput, low-latency system also utilizes capital more efficiently by rotating it through many short-lived opportunities. Instead of holding a position for days waiting for a thesis to play out, a fast-acting AI can deploy capital to dozens of quick opportunities in that same period.
To summarize, optimizing token latency and half-life leads to:
Higher Sharpe Ratios: via more numerous and timely trades
More Alpha Captured: squeezing each signal before it evaporates
Improved Capital Efficiency: continuously redeploying capital in fresh opportunities
Shorter Time-to-Alpha: new strategies can be put to work faster
The AI Time Factory: A Pipeline for Speed and Persistence
How do we build a system that optimizes these token metrics? Enter the concept of an AI Time Factory: a scalable pipeline designed to minimize token latency and maximize token half-life.
Think of it as an assembly line where raw data enters, and profitable actions exit, with minimal delay. Its motto: "turn real-time data into probabilistic actions faster than the signal can decay."
An AI Time Factory involves a continuous streaming architecture, where data is processed in motion rather than in batches. Instead of waiting for intervals, the factory handles event-by-event updates.
This could be built on a high-performance event-driven framework coupled with in-memory AI models. Every tick is immediately transformed into features, fed into models, and triggers outputs – all within microseconds to milliseconds.
Key characteristics of a Time Factory architecture include:
Token-Optimized Processing
The pipeline treats tokens as first-class citizens. Everything from market data to news is quickly tokenized for AI consumption. It uses efficient data structures and keeps data close to compute to avoid slowdowns.
Low Entropy Flow
A Time Factory ensures data flow through the pipeline is predictable and orderly. It avoids congested network hops or inconsistent queuing by keeping most processing within a unified system.
In contrast, a patchwork system might have highly variable latencies – high entropy – the enemy of a reliable low-latency pipeline.
Real-Time Feedback Loop
A true AI Time Factory learns from outcomes in real time. When a trade executes, the result is immediately fed back into the data stream. The AI can then update for subsequent decisions. The system adapts – if signals start decaying faster, it can adjust weighting or switch to new signals.
This creates a closed-loop learning system that continuously refines itself. The more it operates, the smarter and faster it becomes – a positive feedback cycle.
Winning the Quant Hunger Games: A Call to Arms
In the ultra-competitive world of AI-driven trading, those who master time will master the markets. Token latency and token half-life may sound like technical metrics, but they encapsulate a strategic truth: the faster you can extract information from data and act, and the longer your insights remain valid, the more profit you stand to make.
Quant teams should treat latency budgets and signal half-lives as rigorously as they treat risk budgets. Just as we wouldn't ignore a rising Value-at-Risk, we shouldn't ignore a creeping latency that eats into our alpha, or a shortening half-life that signals an aging strategy.
This is a call to arms for quant leaders and front-office technologists: build your own AI Time Factory. Optimize your pipeline ruthlessly – cut out needless delays, invest in high-performance compute, bring models and data closer together, and streamline the flow from observation to action.
Use math to your advantage: quantify how much alpha you lose per millisecond of delay, quantify how quickly your signals decay, and let those numbers drive urgency within your organization. Rally your team around minimizing token latency at every step and extending token half-life through continuous innovation and learning.
The future of quant infrastructure will be won by those who move closer to the speed of light. It will be won by those who can synthesize real-time data into probabilistic decisions in a blink, while the signal is still fresh. It will be won by architectures that ensure each token finds its mark before its value halves or vanishes.
In short, the winners will be the firms who minimize token latency and maximize token half-life in a virtuous, self-reinforcing cycle of improvement. They will enjoy outsized Sharpe ratios, elusive alpha, and superior capital efficiency, leaving slow-moving competitors in the dust.
As you plan your next-generation trading platforms, remember that in the age of AI, speed isn't just for high-frequency traders; it's the lifeblood of every intelligent agent. Build for speed. Build for persistence of insight.
The Token Hunger Games have begun – may the odds (and the alphas) be ever in your favor.
References
Huang, J. (2025). "Everything's token" in AI. NVIDIA GTC Keynote
Barr, A. (2025). Every company will become an AI factory, generating tokens. Business Insider
Ward-Foxton, S. (2025). Huang Talks Tokens, Reveals Roadmap at GTC 2025. EE Times
The framing of token latency and token half-life as core metrics makes a lot of sense. In practice, any trading signal is competing against both time and noise. If your pipeline reacts slower than the market absorbs the information, the edge is already gone.
Also, the concept of an “AI Time Factory” gets at something real,most firms still have fragmented systems with batch delays and redundant processing. Bringing models closer to the data stream feels like the necessary next step, not just in trading but across any AI system that works on live signals.
The framing of token latency and token half-life as core metrics makes a lot of sense. In practice, any trading signal is competing against both time and noise. If your pipeline reacts slower than the market absorbs the information, the edge is already gone.
Also, the concept of an “AI Time Factory” gets at something real,most firms still have fragmented systems with batch delays and redundant processing. Bringing models closer to the data stream feels like the necessary next step, not just in trading but across any AI system that works on live signals.