Written by Nelson Koning
on 

Quality vs speed in the AI fast lane: when to floor it, and when to ride the brakes

We’re all feeling it: AI has turned the workday into a highway. Drafts appear in seconds. Code scaffolds itself. Slides assemble like Lego bricks. It’s thrilling (and a little terrifying) because speed is finally cheap. Quality isn’t.

The new reality isn’t just “move fast and break things.” It’s “move fast, then decide what’s safe to break, what mustn’t break, and when to slow down.” Two recent moments captured this tension: Google’s stumble with AI Overviews in Search, and a Canadian tribunal holding Air Canada responsible for its own chatbot’s bad advice. Both are reminders that the quality–speed dial isn’t theoretical anymore; it’s public, legal, and brand-defining. And with the rise of “reasoning” models that can take extra time to think, teams now have an explicit knob to trade latency for accuracy.

Let’s unpack the lessons and map a practical playbook for building AI features that are fast when they can be—and careful when they must be.

When speed itself becomes the headline

The lesson: when you accelerate a core experience (search, checkout, safety), your margin for error shrinks to near-zero. You might gain days of velocity—and owe months of trust repairs.

When speed meets law

In February 2024, a Canadian tribunal ordered Air Canada to compensate a traveler who was misled by the airline’s own website chatbot about bereavement fares. Air Canada argued the bot was a “separate legal entity.” The tribunal called that “remarkable,” ruling the airline was responsible for all information on its site, bot or not. Quality failures turned into real liability, interest, and fees. (theguardian.com)

As companies drop AI into customer touchpoints, this case is now the go-to cautionary tale: if your bot answers, your brand stands behind it. “We’ll fix it later” may be an acceptable stance for an internal prototype; for consumer-facing policy or pricing, it’s an invitation to court. (arstechnica.com)

AI just gave us a new dial: fast vs. slow thinking

The newest wave of models doesn’t just get faster. It offers modes that think longer. OpenAI’s o1 family, introduced on September 12, 2024, famously improved on reasoning-heavy tasks by allocating more “test-time compute”—in plain English, more time for the model to think before speaking. OpenAI’s own write-up is explicit: performance improves not only with more training but also with “more time spent thinking.” Translation: you can buy quality with latency (and cost). (openai.com)

In 2025, that knob started showing up in product UX. OpenAI’s o3‑mini lets you choose reasoning depth—low, medium, or high—so you can pay for deeper deliberation when the question is hard or high-risk, and sprint when it’s simple. This isn’t just model geekery; it’s an operational tool for product teams to tune quality vs speed in real time. (axios.com)

Researchers are codifying the idea, too. A June 11, 2025 paper argues we should treat “reasoning” like a resource—budgeted, scheduled, and measured—so systems “think deep when necessary and act fast when possible.” It’s the practical framing teams need to design quality into latency budgets instead of bolting it on. (arxiv.org)

There’s a flip side: as we add “slow-thinking” safety checks, attackers probe them. A February 2025 study showed that exposing intermediate “chain-of-thought” safety reasoning can be hijacked (H‑CoT), collapsing refusal rates on dangerous prompts from about 98% to under 2% in some tests. Extra time isn’t automatically safer; it’s another surface you must harden. (arxiv.org)

A simple playbook: two-speed AI by design

Think like a racing team. You don’t use the same tires for rain and sunshine. Build a two-speed system from day one:

  1. Fast lane (default)
  1. Slow lane (deliberation)

In other words: ship speed broadly, spend thought selectively.

A tiny pattern you can paste into your app

Here’s a minimal sketch for routing and gating. You can implement this with any provider; the idea is what matters.

def answer(query, user_id):
    risk = classify_risk(query)           # policy/safety taxonomy → {low, medium, high}
    difficulty = predict_difficulty(query) # heuristics: ambiguity, novelty, numeric reasoning, cite need

    # default to fast lane
    lane = "fast"
    if risk == "high" or difficulty == "hard":
        lane = "slow"

    if lane == "fast":
        resp = fast_model(query, guardrails=True)  # retrieval on, safety checks on
        if not passes_quality(resp):
            return escalate(query, reason="low_quality_from_fast")
        return resp

    # slow lane: spend more thinking
    resp = slow_model(
        query,
        reasoning="high",
        citations=True,
        guardrails=True,
        self_check=True,           # e.g., second pass critique or cross-model agree
    )
    if requires_human_review(resp, risk):
        create_review_task(user_id, query, resp)
    return resp

Under the hood, budget the “slow_model” with explicit knobs:

Quality gates that catch what speed misses

Use layered, cheap checks to avoid expensive failures.

The Google Overviews episode is a live reminder that post-launch scoping and policy upgrades (“limit satire sources,” “reduce trigger surface”) are not nice-to-haves—they’re part of the release plan. Ship with the assumption you’ll tighten the aperture as real traffic reveals edge cases. (theguardian.com)

Metrics that balance quality and speed

Define SLAs and SLOs for both lanes. Track them visibly.

These tie directly to today’s “reasoning models.” If your own experiments mirror OpenAI’s finding—that more test-time thinking usually improves outcomes—instrument it. Know how much extra thought is worth, where it saturates, and when it backfires. (openai.com)

Organizational guardrails: quality is a team sport

A musician’s take: tempo is a choice, not a virtue

As a guitarist, I love speed, but the crowd remembers tone and timing. AI is similar. Velocity feels like progress because something is happening. Quality is progress because something is right.

The good news: modern tooling makes the trade-off explicit. You can route low-risk, low-ambiguity tasks through a fast lane that delights users, while sending ambiguous or consequential work to a slower lane that reasons, cites, and sometimes asks a human. Vendors are even baking these controls into the interface: pick your reasoning depth, embrace latency where it buys trust, and trim it where it doesn’t. (axios.com)

If you need a litmus test, try this:

If the answer to either is no, switch lanes, spend more thought, and raise the quality bar.

The AI highway isn’t slowing down. But you don’t have to pick a single speed. Treat reasoning like a budget. Design for two lanes. Make tempo a product choice. That’s how you keep shipping fast—and keep your reputation intact. (arxiv.org)

References to recent events and research: