Databricks — Teaching a Search Agent When to Stop

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Every time you ask an enterprise AI a complex
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question, it basically spins up a microscopic
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budget crisis. Oh, absolutely. It's an instant
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crisis. Right. Like, you hit enter, the cursor
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blinks, and, well, behind the scenes, that system
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is aggressively burning through thousands of
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compute cycles every single second you wait.
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It desperately wants to give you the perfect,
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you know, deeply researched answer, but it's
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on a ticking clock. Exactly. And for you, sitting
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there watching that little glowing animation,
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the wait can feel like being put on hold with
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elevator music. You're left wondering, is this
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thing actually synthesizing deep, profound thoughts
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right now? Or did it just get stuck reading the
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same three documents in an infinite loop? Yeah,
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it's a massive point of friction. I mean, from
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a user experience standpoint, the lack of visibility
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into that wait time is incredibly anxiety inducing.
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Definitely. But from an engineering standpoint.
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It's really a symptom of a much larger architectural
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bottleneck that the whole industry has just been
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slamming its head against. Which is exactly why
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we are dedicating today's deep dive for you,
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our listener, to a system that is actively trying
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to kill that friction. We are unpacking a September
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2026 blog post from Databricks detailing their
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newly announced Adaptive Instructed Retriever.
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It's a fascinating piece of engineering. It really
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is. Our mission today is to figure out the specific
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business problem this solves, how they engineered
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the underlying mechanics to fix it, and, well,
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what it actually means for the broader landscape
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of AI search. Because they are just brute forcing
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the problem by throwing more processing power
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at it, right? They are fundamentally rewiring
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how the AI evaluates its own time, forcing it
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to decide when its job is basically done. Okay,
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let's unpack this. To really appreciate the architecture
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Databricks built, we have to ground ourselves
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in the business context first. We have to look
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at the fast versus accurate dilemma that is essentially
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choking enterprise data agents right now. Yeah,
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that dilemma is really the defining challenge
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of enterprise AI deployments today. How so? Well,
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if you were deploying an agent for a Fortune
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500 company, you needed to be lightning fast.
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Otherwise, employees just get frustrated, abandon
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it, and go right back to their legacy search
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tools. Right. Nobody wants to wait 20 seconds
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for an answer. Exactly. But you also needed to
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be highly accurate because people are making
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million -dollar business decisions based on the
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output. And... Historically, the underlying physics
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of large language models dictate that you really
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only get to pick one, fast or accurate. So developers
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are essentially trapped between two flawed architectures,
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the first being what the source material calls
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single -step retrieval? Yes. So single -step
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is your speed option. The AI takes your prompt,
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generates a single search query, fires it across
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the vector database, grabs whatever semantic
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matches come back, and then immediately drafts
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an answer based on that one hull. But the obvious
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vulnerability there is that it completely face
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plants on complex logic, right? Like the source
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points out that single -step retrieval fails
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miserably on multi -hop questions. It does. It
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fails because multi -hop questions require sequential
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reasoning. You can't just retrieve a fact in
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a vacuum. You have to retrieve a fact, evaluate
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it, realize there is, say, a missing piece of
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context, and then generate an entirely new secondary
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search based on that new realization. So it's
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like if I'm looking for my lost car keys. A single
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-step search is just me glancing at the kitchen
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counter. I look once, I don't see them, and I
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just declare the keys cease to exist. Exactly.
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Game over. Keys are gone. Right. But a multi
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-hop search is me checking the counter, not finding
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them, but then remembering I wore a jacket yesterday.
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I go to the closet, search the jacket, find a
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receipt from a diner, and then I realize, oh,
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I need to call the diner. That is a perfect analogy.
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But the thing is, in an enterprise database,
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we aren't just checking one jacket. We're checking
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a lot of jackets. Far from one. You might be
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checking 5 million jackets and querying 10 ,000
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diners. The scale of the data is massive. Which
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brings us to the alternative architecture, sequential
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multi -step search. So this is where the AI is
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allowed to do exactly what we just described
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with the keys. Right. It searches. reads the
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context, generates a new query, searches again,
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over and over. But every single hop requires
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the LLM to process thousands of tokens and generate
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new outputs. It is painfully slow. And the compute
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costs, well, they scale linearly with every single
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step. So if a user asks a complex financial compliance
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question and it takes the agent seven hops to
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piece the narrative together, that user is staring
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at a blinking cursor for 30 seconds. Easily 30
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seconds. And for a vendor like Databricks who
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has to actually pay for the compute powering
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those hops, they are just blowing up their latency
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and cost budgets simultaneously. It's completely
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unsustainable. Enterprise search requires that
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complex multi -hop reasoning, but the market
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simply won't tolerate the massive wait times.
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Databricks knew they couldn't just optimize the
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hardware. They had to fundamentally alter the
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agent's behavior. Which brings us to the actual
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technical details. The core of this blog post
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is about teaching an AI when to pull the plug.
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But how does that mechanically work? Because
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normally you train an AI model to maximize accuracy.
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You tell it to find the absolute best answer,
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period. Right. And they shifted the paradigm
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by using online reinforcement learning to teach
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the agent a dynamic stopping rule. Specifically,
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they used an objective function called CISPO.
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Wait, I want to pause on CISPO because that is
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a really heavy string of terminology. It is a
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bit of a mouthful, yeah. I know we're assuming
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a baseline understanding of reinforcement learning
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here, giving the model rewards for good behavior
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and penalties for bad. But what is the actual
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mechanism of a CISPO? Let's break down the mechanics.
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It stands for Clipped Importance Sampling Policy
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Optimization. In standard reinforcement learning,
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you might update the model's policy wildly if
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it stumbles on a massive reward. Like it gets
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too excited and overcorrects. Exactly. But in
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complex language models, wild updates cause catastrophic
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forgetting. The model basically destabilizes.
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The clipped part of SciPO means they mathematically
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constrain how much the model's behavior can change
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in a single training step. It forces stable,
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incremental learning. Okay, so it prevents the
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AI from radically altering its search strategy
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overnight just because it got lucky on one query.
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Precisely. Now, look at how they applied it on
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the Databricks AI runtime. They didn't train
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this agent from scratch. They took an existing
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model, Instruct Retriever 1, which was already
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highly optimized for that fast, parallel, single
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-step search. So it already had the baseline
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capability to scan the kitchen counter efficiently.
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Yes. To force it to learn complex reasoning,
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they introduced a data set of synthetic multi
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-hop questions, queries specifically engineered
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so that they could not be answered in one step.
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They forced it to look in the jacket pockets.
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They forced it. But here is the critical innovation,
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the reward function within Syspo. They didn't
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just reward the model for finding the right answer.
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They designed a formula that constantly weighs
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the trajectory quality against the computational
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cost. But wait, if you introduce a heavy penalty
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for time and compute, doesn't the AI just learn
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to get lazy? Like, doesn't it just say, well,
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searching further hurts my score, so I'll just
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stop after step one and give a hallucinated garbage
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answer? That is the exact risk of poorly tuned
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reinforcement learning. And it's why the math
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here is so delicate. The AI is constantly projecting
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the expected value of the next step. How does
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it calculate that? It's constantly calculating,
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if I spend another 800 milliseconds and 4 ,000
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tokens to run one more search hop, the probability
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of increasing my accuracy is X. But the baked
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-in penalty for that time is Y. Here's where
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it gets really interesting. They basically installed
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a real -time ROI calculator inside the agent's
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brain. That's exactly what it is. It's like a
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professional researcher who has a strict five
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-minute deadline. If they find an answer that
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is 95 % confident at minute three, they stop.
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They don't burn the last two minutes hunting
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for a 96 % confident answer because the marginal
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gain just isn't worth the ticking clock. That's
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a great way to visualize it. The model learns
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to recognize diminishing returns. And as an architectural
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fail -safe, Databricks hard -coded an absolute
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upper bound on sequential steps. So there's a
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hard limit no matter what. Right. Even if the
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expected value calculation gets confused and
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the agent really wants to keep digging, the system
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cuts it off. The latency is mathematically capped.
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It's an enforced budget. And according to the
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researchers, this CISPO training process results
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in something they call a Pareto frontier of checkpoints.
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What does that actually look like for a developer
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deploying this? Think of a Pareto frontier like
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tuning the aerodynamics on a Formula One race
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car. You can... Tune the wings for maximum downforce
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to take corners perfectly, but, well, you sacrifice
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top speed on the straightaways. Right, you can't
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have both. Exactly. Or you can flatten the wings
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for maximum top speed, but you'll slide out on
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the corners. The Pareto Frontier represents the
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absolute physical limit of that car. It's a curve
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where you cannot improve speed without losing
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downforce and vice versa. So in this case, the
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two competing metrics are retrieval quality and
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latency. Yes. Because Databricks trained this
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model across various cost penalty weights, they
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didn't just output one final model. They output
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a menu of checkpoints along that mathematical
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limit. So what does this all mean for the person
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actually building the app? Like, if I'm an engineer,
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I can look at this Pareto menu and make a business
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decision? Yep. You have total control. If I'm
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building an internal HR chatbot where speed is
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critical for adoption, I pick the checkpoint
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that aggressively prioritizes low latency, accepting
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a tiny hit to deep contextual reasoning. But
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if I'm building a legal discovery tool, I slide
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the dial entirely the other way. You've got it.
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The developer gets to dictate the terms of the
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tradeoff, but they do so knowing that whichever
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checkpoint they select, it is hyper -optimized.
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They are guaranteed to be operating on that absolute
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frontier of efficiency. It's a very elegant theoretical
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framework. But... Let's look at the impact, because
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theory only matters if it actually translates
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to real -world speed. What are the actual results
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Databricks achieved with this adaptive instructed
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retriever? The headline metric they published
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is an average end -to -end response time of just
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5 .8 seconds. Wow. 5 .8 seconds. For complex
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multi -hop reasoning over enterprise data? It's
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incredibly fast. And they benchmark that 5 .8
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second mark against the heavyweight Frontier
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models of the industry. They claim this architecture
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is more than two times faster than Claude's Sonnet
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5, DeepSeek V4 Flash, and GPT -5 .6 Luna. Beating
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GPT -5 .6 Luna by a 2x multiplier on speed is
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a massive flex. But I assume the immediate counter
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-argument is that they must be sacrificing accuracy
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to get that speed. Well, that's where the Pareto
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optimization comes in. Databricks claims they
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match all of those leading third -party models
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on standard retrieval benchmarks. They are delivering
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parity on reasoning quality, but doing it in
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less than half the time. So we drop latency from,
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what, 12 or 14 seconds down to 5 .8. Beyond just
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saving a few seconds of annoyance, how does that
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actually change the transferable lessons for
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the industry? It fundamentally reshapes the user
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interface layer. In human -computer interaction,
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there are established cognitive thresholds. 10
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to 15 second wait time breaks the illusion of
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a continuous thought process. Right. The user's
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mind wanders, they switch browser tabs, and the
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flow state is just destroyed. Exactly. But when
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you drop that response time under six seconds,
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it registers cognitively as a slight natural
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pause, like someone taking a breath before answering
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a hard question. It keeps the user locked into
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the workflow. So Databricks is proving that developers
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don't have to accept a broken UX as a mandatory
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tax for running advanced AI. This CISPO -based
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stopping rule is a replicable blueprint. Exactly.
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It shows the wider industry that you can rein
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in these models without castrating their reasoning
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abilities. Which brings us to the question of
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novelty. A 2x speed increase over GPT -5 .6 Luna
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sounds revolutionary, but we have to separate
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genuine scientific breakthroughs from really
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good software engineering. How new is this approach,
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really? Looking at the underlying methodology,
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the novelty score for this paper sits at a solid
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3 out of 5. Okay, a 3 out of 5. Why not higher?
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What part of this is just established practice?
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Well, agentic retrieval itself is not new. Having
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a language model autonomously search, read, evaluate,
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and search again, we have had multi -hop agents
00:12:55.690 --> 00:12:57.830
doing that for a couple of years now. So the
00:12:57.830 --> 00:12:59.610
concept of the AI digging through the jacket
00:12:59.610 --> 00:13:01.669
pockets for the diner receipt isn't the breakthrough?
00:13:01.990 --> 00:13:04.929
No, it isn't. The true advance is the highly
00:13:04.929 --> 00:13:06.970
targeted application of reinforcement learning,
00:13:07.129 --> 00:13:09.590
specifically that clipped objective function
00:13:09.590 --> 00:13:11.950
to actively learn a cost -ware stopping rule,
00:13:12.110 --> 00:13:14.990
and then packaging that into a practical Pareto
00:13:14.990 --> 00:13:17.389
curve for developers. So to synthesize this,
00:13:17.490 --> 00:13:19.710
Databricks didn't invent the internal combustion
00:13:19.710 --> 00:13:22.570
engine of AI research. They basically invented
00:13:22.570 --> 00:13:25.070
the highly optimized automatic transmission.
00:13:25.629 --> 00:13:28.169
Oh, that's good. Right. Instead of the engine
00:13:28.169 --> 00:13:31.009
redlining in first gear while the AI mindlessly
00:13:31.009 --> 00:13:34.210
runs query after query, the transmission senses
00:13:34.210 --> 00:13:37.309
the load, shifts gears, and stops burning computing
00:13:37.309 --> 00:13:39.730
fuel the exact millisecond it realizes it has
00:13:39.730 --> 00:13:42.210
enough momentum to coast to the answer. That
00:13:42.210 --> 00:13:45.230
is a phenomenal analogy. Yes, they engineered
00:13:45.230 --> 00:13:47.549
an automatic transmission for agentic surge.
00:13:47.710 --> 00:13:50.970
And that specific control mechanism is highly
00:13:50.970 --> 00:13:53.429
novel. To really contextualize how important
00:13:53.429 --> 00:13:55.769
that transmission is, we need to look at what
00:13:55.769 --> 00:13:57.669
other tech giants are doing to solve the exact
00:13:57.669 --> 00:14:00.529
same latency problem. Because the entire industry
00:14:00.529 --> 00:14:03.649
knows that multi -hop surge is too slow. They
00:14:03.649 --> 00:14:06.289
do. And there are wildly different philosophies
00:14:06.289 --> 00:14:09.129
on how to fix it. If we look at similar recent
00:14:09.129 --> 00:14:11.570
work, there's a major paper from Google that
00:14:11.570 --> 00:14:13.730
dropped right around the same time, late September
00:14:13.730 --> 00:14:17.509
2026, called Retrieve for Train. Retrieve for
00:14:17.509 --> 00:14:20.309
Train. Right. And Google is taking an almost
00:14:20.309 --> 00:14:22.409
opposite approach to bypassing the inference
00:14:22.409 --> 00:14:25.269
bottleneck. Opposite in what way? Google is investing
00:14:25.269 --> 00:14:27.690
heavily in what they call fan -out AI search.
00:14:28.049 --> 00:14:31.070
Remember how sequential multi -step search forces
00:14:31.070 --> 00:14:33.769
you to wait for one query to finish before starting
00:14:33.769 --> 00:14:35.750
the next? Yeah, the linear bottleneck. Right.
00:14:36.250 --> 00:14:38.570
Fan -out search attempts to avoid the wait by
00:14:38.570 --> 00:14:42.269
casting a massive parallel net all at once. Instead
00:14:42.269 --> 00:14:44.309
of checking the counter, then the jacket, then
00:14:44.309 --> 00:14:46.990
the diner in sequence, the AI simultaneously
00:14:46.990 --> 00:14:50.190
fires off 50 variations of the search query across
00:14:50.190 --> 00:14:53.590
the entire database in one massive burst. So
00:14:53.590 --> 00:14:56.389
it's trading sequential time for sheer breadth.
00:14:56.830 --> 00:14:59.289
But doesn't that just flood the system with irrelevant
00:14:59.289 --> 00:15:03.129
data? It does. It hits API rate limits. And more
00:15:03.129 --> 00:15:06.029
importantly, it absolutely floods the LLM's context
00:15:06.029 --> 00:15:08.870
window. Google's approach is about maximizing
00:15:08.870 --> 00:15:11.350
the probability that the right document is somewhere
00:15:11.350 --> 00:15:14.289
in that massive pile. It's like finding a needle
00:15:14.289 --> 00:15:16.990
in a haystack by just grabbing the entire haystack.
00:15:17.190 --> 00:15:20.639
Exactly. Whereas Databricks' approach is hyper
00:15:20.639 --> 00:15:22.679
-focused on the precise timing of the search,
00:15:22.879 --> 00:15:26.299
keeping the search sequential and focused, but
00:15:26.299 --> 00:15:28.539
using reinforcement learning to optimize the
00:15:28.539 --> 00:15:32.600
exact moment to pull the plug. brute force parallelization
00:15:32.600 --> 00:15:37.179
versus extreme sequential efficiency. Yes. And
00:15:37.179 --> 00:15:39.799
this dynamic ties directly into another critical
00:15:39.799 --> 00:15:42.600
piece of research mentioned in our sources, a
00:15:42.600 --> 00:15:46.120
paper titled The Recall Ceiling of LLM Recommendation
00:15:46.120 --> 00:15:49.179
Re -Ranking. The recall ceiling? Yeah. Walk me
00:15:49.179 --> 00:15:51.360
through the mechanics of that. Sure. So recall
00:15:51.360 --> 00:15:53.879
is the industry term for the raw percentage of
00:15:53.879 --> 00:15:56.399
relevant documents the search system manages
00:15:56.399 --> 00:15:58.740
to pull from the database before the language
00:15:58.740 --> 00:16:01.899
model even looks at them. Okay. That paper mathematically
00:16:01.899 --> 00:16:05.440
proves a hard ceiling. Your final generated answer
00:16:05.440 --> 00:16:07.679
is entirely bounded by the quality of the raw
00:16:07.679 --> 00:16:10.840
materials retrieved. If the search agent doesn't
00:16:10.840 --> 00:16:12.720
pull the right document into the context window,
00:16:12.980 --> 00:16:16.159
no amount of advanced reasoning by GPT -6 or
00:16:16.159 --> 00:16:19.320
Claude 5 can magically hallucinate the correct
00:16:19.320 --> 00:16:21.980
factual answer. Right. If you hire a world -class,
00:16:22.000 --> 00:16:23.899
award -winning architect to build a skyscraper,
00:16:23.919 --> 00:16:26.399
but the supply chain only delivers rotten wood
00:16:26.399 --> 00:16:28.840
and rusty nails, the building is going to collapse.
00:16:29.080 --> 00:16:31.720
The brilliance of the architect or... the LLM
00:16:31.720 --> 00:16:34.000
in this case, is capped by the raw materials.
00:16:34.340 --> 00:16:36.419
Which is exactly why the Databricks achievement
00:16:36.419 --> 00:16:39.919
is so significant. FanOut Search risks hitting
00:16:39.919 --> 00:16:43.240
a context limit with irrelevant noise. Databricks
00:16:43.240 --> 00:16:45.200
is claiming their sequential adaptive instructor
00:16:45.200 --> 00:16:47.840
retriever maintains that high recall ceiling.
00:16:48.159 --> 00:16:50.960
It pulls the high -grade steel and the good concrete,
00:16:51.120 --> 00:16:54.059
but it cuts the retrieval time in half by knowing
00:16:54.059 --> 00:16:57.659
exactly when to stop ordering materials. It is
00:16:57.659 --> 00:16:59.779
a brilliant piece of structural engineering on
00:16:59.779 --> 00:17:02.620
paper, but as we do with all vendor -released
00:17:02.620 --> 00:17:04.680
benchmarks, we need to read the fine print. And
00:17:04.680 --> 00:17:07.440
I want to clearly flag for you, our listener,
00:17:07.619 --> 00:17:09.619
that we are now shifting gears. We are moving
00:17:09.619 --> 00:17:12.099
away from the factual claims presented in the
00:17:12.099 --> 00:17:15.180
Databricks blog post and moving into expert commentary,
00:17:15.400 --> 00:17:18.700
analysis, and opinion. And this is where a healthy
00:17:18.700 --> 00:17:21.519
dose of skepticism is required. The critical
00:17:21.519 --> 00:17:24.400
caveat underlying this entire discussion is that
00:17:24.400 --> 00:17:28.059
the 2x speed claim? is based entirely on internal
00:17:28.059 --> 00:17:31.400
benchmarks run by Databricks themselves. Right.
00:17:31.519 --> 00:17:33.559
It's the classic problem of the vendor grading
00:17:33.559 --> 00:17:36.799
their own homework. Always a red flag. Furthermore,
00:17:37.059 --> 00:17:39.480
the source summary explicitly notes that there
00:17:39.480 --> 00:17:41.380
are glaring missing details in the methodology.
00:17:41.980 --> 00:17:45.019
Like what? Databricks doesn't specify exactly
00:17:45.019 --> 00:17:47.660
which retrieval benchmarks were used to validate
00:17:47.660 --> 00:17:51.059
the quality match against Claude and GPT 5 .6.
00:17:51.920 --> 00:17:54.519
Even more concerning, they don't explicitly detail
00:17:54.519 --> 00:17:57.140
how latency was measured across those disparate
00:17:57.140 --> 00:18:00.319
systems. Oh, wow. Measuring API round -trip time
00:18:00.319 --> 00:18:02.680
for a third -party model versus internal runtime
00:18:02.680 --> 00:18:05.619
on your own proprietary hardware is often in
00:18:05.619 --> 00:18:08.859
apples -to -oranges comparison. Wait, so if they
00:18:08.859 --> 00:18:10.700
aren't disclosing the exact testing conditions
00:18:10.700 --> 00:18:12.900
and we can't independently verify their math
00:18:12.900 --> 00:18:15.700
right now, should developers just dismiss the
00:18:15.700 --> 00:18:18.940
2x claim entirely? Like, is this just a highly
00:18:18.940 --> 00:18:21.720
optimized press release? Dismissing it would
00:18:21.720 --> 00:18:25.119
be a mistake, actually. While the exact 2x multiplier
00:18:25.119 --> 00:18:27.759
should absolutely be treated as a directional
00:18:27.759 --> 00:18:30.339
marketing claim rather than absolute scientific
00:18:30.339 --> 00:18:33.299
gospel, the underlying architectural methodology
00:18:33.299 --> 00:18:37.480
is rock solid. Directional, meaning it is definitively
00:18:37.480 --> 00:18:39.720
a faster architecture, even if independent testing
00:18:39.720 --> 00:18:43.039
eventually proves it's only 1 .6x or 1 .8x faster
00:18:43.039 --> 00:18:46.829
in the wild? Exactly. Using Sysbo to train a
00:18:46.829 --> 00:18:48.970
cost -aware stopping rule solves a fundamental,
00:18:49.269 --> 00:18:52.289
mathematically provable bottleneck in agentic
00:18:52.289 --> 00:18:55.089
reasoning. The Pareto menu they've offered developers
00:18:55.089 --> 00:18:58.750
is a highly practical, usable tool. It is a very
00:18:58.750 --> 00:19:01.329
real, very clear win for anyone deploying enterprise
00:19:01.329 --> 00:19:04.369
AI, regardless of whether the marketing sticker
00:19:04.369 --> 00:19:07.150
on the window is slightly inflated. The engine
00:19:07.150 --> 00:19:09.390
design works, even if we want to run it on a
00:19:09.390 --> 00:19:11.609
dyno ourselves before we believe the top speed.
00:19:12.079 --> 00:19:14.220
That's the most accurate way to view this release.
00:19:14.640 --> 00:19:16.519
All right, let's pull all of these threads together.
00:19:16.720 --> 00:19:18.720
What is the ultimate takeaway for you today?
00:19:19.619 --> 00:19:22.380
Databricks has successfully tackled the agonizing
00:19:22.380 --> 00:19:24.599
wait times of enterprise AI by engineering the
00:19:24.599 --> 00:19:27.099
adaptive instructed retriever. Yep, they nailed
00:19:27.099 --> 00:19:29.700
the core issue. By leveraging reinforcement learning,
00:19:29.839 --> 00:19:31.759
specifically that constrained CISPO objective,
00:19:32.079 --> 00:19:34.299
they didn't just throw more silicon at the problem.
00:19:34.420 --> 00:19:37.599
They taught the AI the lost art of knowing when
00:19:37.599 --> 00:19:40.420
to stop searching. By constantly balancing the
00:19:40.420 --> 00:19:42.619
mathematical value of a slightly better answer
00:19:42.619 --> 00:19:45.259
against the severe computational costs of finding
00:19:45.259 --> 00:19:48.299
it, they've achieved a snappy 5 .8 second average
00:19:48.299 --> 00:19:50.720
response time, allowing them to rival massive
00:19:50.720 --> 00:19:54.339
models like GPT -5 .6 Luna on complex multi -hop
00:19:54.339 --> 00:19:56.789
reasoning. They've effectively proven that the
00:19:56.789 --> 00:19:58.869
industry doesn't have to choose between deep
00:19:58.869 --> 00:20:01.990
reasoning and usable latency. You can enforce
00:20:01.990 --> 00:20:04.950
a strict budget on time and compute without shattering
00:20:04.950 --> 00:20:07.990
the recall ceiling. It is a fascinating leap
00:20:07.990 --> 00:20:10.710
in system design. But before we wrap up, I want
00:20:10.710 --> 00:20:12.470
to leave you with something to ponder that extends
00:20:12.470 --> 00:20:14.910
a bit beyond vector databases and server costs.
00:20:16.000 --> 00:20:18.119
Databricks essentially trained in artificial
00:20:18.119 --> 00:20:21.220
intelligence to recognize the precise mathematical
00:20:21.220 --> 00:20:24.740
moment when good enough is fundamentally better
00:20:24.740 --> 00:20:28.319
than perfect but slow. If we can successfully
00:20:28.319 --> 00:20:31.359
codify that boundary for a machine, well, where
00:20:31.359 --> 00:20:33.599
else in our own lives, in our daily workflows,
00:20:33.759 --> 00:20:36.299
or in the software we build, could we apply a
00:20:36.299 --> 00:20:39.319
mathematical formula for knowing exactly when
00:20:39.319 --> 00:20:43.099
to stop? That is a profound question. We spend
00:20:43.099 --> 00:20:46.079
so much time optimizing for perfection. We often
00:20:46.079 --> 00:20:48.640
forget that the pursuit of perfection is usually
00:20:48.640 --> 00:20:52.599
the enemy of the good and the fast. Thank you
00:20:52.599 --> 00:20:55.000
so much for joining us on this deep dive. Keep
00:20:55.000 --> 00:20:57.700
questioning the wait times, keep interrogating
00:20:57.700 --> 00:20:59.740
the data around you, and we will catch you next
00:20:59.740 --> 00:21:00.000
time.