Talk by Thais Castello Branco (Taste Labs) on improving AI performance in subjective work such as writing and design. She frames domains along a spectrum from cleanly verifiable tasks to pure preference, arguing that valuable work lies in between and can be made tractable through decomposition—breaking designs into elements graded against an original. She discusses "collapse to the mean," where models drift toward average outputs, and how structured expert preference data, human QA tied to specific choices, and rich (not noisy) preference signals can be used to train taste in subjective domains. # Channel Presentation Observations — Taste Labs ## Speaker & Channel Identity - **Speaker:** Thais Castello Branco, self-identified founder of Taste Labs. > "I'm Tais. I'm the founder of Taste Labs. For those of you who don't know us, we came out of Stealth a few weeks ago." - **Explicit mission claim:** The company's stated purpose is eliminating low-quality AI-generated content ("AI slop"): > "our whole mission is basically how do we end AI slop?" ## Claimed Scope of Work The speaker describes Taste Labs as a **data and infrastructure layer** for subjective domains, operating across two areas: - **Frontier lab partnerships:** > "we work with the top frontier labs on how do we evaluate benchmark their models understand where they're breaking" - Including decisions on which problems suit **RL environments** vs. **post-training data**. - **Agent/application-layer companies:** > "we also go and work with a lot of agent and application layer companies on what are things that we actually don't believe should be solved at the foundation model layer" ## Claimed Resources / Capabilities - **Expert network claim:** A community of over a thousand domain experts: > "we work, for example, with a community of designers, over like a thousand experts that are experts in different types of medium, different styles" - **Research activity claim:** > "we run a lot of research on our side" ## Intended Audience - A **technical AI/ML conference audience** — assumed familiarity with RL environments, LLM-as-a-judge, reward hacking, and post-training: > "as probably all of you know, AI has gotten quite good at things like coding and math" - Includes practitioners at companies using coding agents: > "if you're at a company and you've used coding agents, you've probably shipped an internal dashboard" - Attendees of a multi-day event with tracks: > "For those that are here tomorrow I'll also be giving a chat on the design track" ## Observable Presentation Style - **Founder-led technical talk** with a framework-driven structure: two core theses (**"capability follows measurability"** and **"collapse to the mean"**), a brand-decomposition worked example, and a stated takeaway: > "if you have one takeaway take this away it's like how do we break down this problem into something that you understand what's actually the best method to solve it" - **Positioning stance:** Emphasis on **quality over quantity** in training data, and that **human judgment outperforms LLM-as-judge** in subjective domains: > "we believe human judgment is still at a much higher level than any LLM as a judge" ## Claims vs. Demonstrated Patterns | Creator claim | Evidence type | |---|---| | Mission to "end AI slop" | Explicit self-description | | Works with top frontier labs & agent companies | Stated, not demonstrated in material | | 1,000+ expert designer community | Stated, no supporting detail shown | | Routing framework (RL vs. data vs. human judgment) | Demonstrated via worked brand-adherence example | No private motivations inferred; all observations trace to the transcript above. # Delivery Observation — Thais Castello Branco, "Ending AI Slop" ## Tone & Register - **Conversational, founder-pitch register**: blends a startup mission framing with technical ML vocabulary ("RL environments," "post-training data," "LLM as a judge"). - Opens with a warm, direct greeting: *"Hello everyone. It's great to meet you all. I'm Tais. I'm the founder of Taste Labs."* - Uses **hedged, thinking-aloud phrasing** throughout: *"I would say,"* *"I think,"* *"let's say,"* — softening claims rather than asserting them flatly. ## Recurring Phrases & Verbal Habits - **"How do we…" as a framing device** — extremely frequent: - *"how do we end AI slop?"* - *"how do you decompose it? How do you verify it?"* - *"how do we break these patterns?"* - *"how do we turn things that feel fuzzy into things that become more verifiable"* - **"Fuzzy" vs. "verifiable/codifiable"** as a repeated conceptual pairing: - *"turn something that feels fuzzy… into something that is more verifiable"* - *"turn a task that feels fuzzy into one where there's a clear ground truth"* - **"Right?"** used as a constant audience-check filler (appears dozens of times, e.g., *"it's contextual first of all. Right?"*). - Repeated word-stutter restarts, suggesting live delivery: *"Uh, and our whole mission,"* *"uh I I I like their website,"* *"subductive domains"* (apparent slip for "subjective domains"). ## Teaching Habits & Structure - **Signposting takeaways explicitly**: - *"So there's two characteristics that I want to touch on today…"* - *"if you have one takeaway… take this away"* - *"So that is my my message of of the day."* - **Concrete example after abstract claim**: introduces brand adherence (*"I want to give you a very specific example… So let's talk about brand"*) immediately after the abstract framing of subjective quality. - **Rhetorical questions to invite audience self-check**: *"I don't know if any of you have bought data for example for these domains…"*; *"I bet that some of you might like one style better than another."* - **Contrast as an explanatory tool**: code/math vs. design/writing; average answer vs. optimal answer; agreement on alignment vs. disagreement on style. ## Vocabulary - Distinctive coined/technical terms: **"AI slop,"** **"collapse to the mean,"** **"mode collapse,"** **"preference vector,"** **"routing problem,"** **"ground truth,"** **"brand adurance/adherence."** - Colloquialisms mixed with technical terms: *"a secret weapon at your disposal,"* *"closer to objective,"* *"whatever you want to call it."* ## Audience Awareness - Assumes AI-literate audience but explains basics anyway: *"as probably all of you know, AI has gotten quite good at things like coding and math."* - Cross-promotes future content: *"For those that are here tomorrow I'll also be giving a chat on the design track."* ## Uncertainty Notes - Fillers ("um," "uh," "like") are pervasive; hard to distinguish stable habits from transcript artifacts without audio. - Only one talk observed; frequency claims (e.g., "how do we…" dominance) are based on this transcript alone. # Ending AI Slop — Thais Castello Branco, Taste Labs ## Speaker - **Thais Castello Branco**, founder of Taste Labs (company emerged from stealth a few weeks before the talk). All claims below are hers. ## Core Thesis - Taste Labs' mission: **"how do we end AI slop?"** — which she argues requires first **decomposing and understanding subjective domains**. - AI is good at coding/math but "still super behind" on design, creative writing, personality, emotional intelligence. - Taste Labs positions itself as a **data and infrastructure layer** spanning foundation models to agent applications. ## How They Work - **With frontier labs**: evaluation, benchmarking, identifying where models break, and deciding which problems fit **RL environments** vs. **post-training data**. - **With agent/application-layer companies**: some problems shouldn't be solved at the foundation layer — better via **context** or **understanding user intent**. - Bet: a world with **billions of non-expert creators**, making user intent/context understanding as important as model improvement. - She works with a community of **1,000+ expert designers** across media and styles. ## Why Subjective Domains Are Hard ### 1. Capability follows measurability - Code being verifiable is "a fact about code, not a property about models" — models are good at coding because code decomposes, verifies, executes. - Design/writing lack clear decomposition and verification; "very hard to define what great even means." - Subjective quality is **contextual** (a slide great for a startup may be wrong for a finance firm) and **time-varying** (unlike math/code). - Most workflows (sales, marketing, writing) have multiple valid answers. ### 2. Collapse to the mean - Models predict the most likely outcome and assume it's optimal — true for math, false for design/writing. - > "A lot of greatness and creativity happens actually at the ends of the distribution." - **Slop = collapse to the mean plus repetition**; the fix is breaking patterns intentionally. - Naively collected preference data also collapses to the mean; the world is **multi-preference**, requiring matching and "preference vectors" attached to data. ## Example: Brand Adherence - "Make something great" is fuzzy; "make something on-brand" is decomposable — brands already encode colors, typography, motion, textures. - LLM-as-judge for brand adherence is "quite hard" (reward hacking, hallucination patterns). - **Task design is the hardest part**: turn fuzzy tasks into clear ground truth usable as an RL environment. - Example task: agent creates a net-new page adhering to a brand; grading should be against the decomposed ground truth, not just similarity to the original (novel valid uses of components should pass). ## Routing Framework (her stated key takeaway) - Design exists on a **spectrum**: objective-ish (alignment, typography, vision) → subjective (style, fit, creativity). - Closer to programmatically verifiable → better for **RL**. - More contextual / preference-dependent / time-dependent → requires **human judgment**; she believes human judgment is "at a much higher level than any LLM as a judge." - Framed as "pulling things toward verification" via codification. ## Data Quality Practices - **Controllable levers**: strict expert selection, forced true distributions, problem decomposition. - **QA signals correlating with high-quality data**: - **Specificity** of expert language/reasoning. - Tying expert commentary to exact code components (reduces noise; models struggle to connect code to visuals). - **Human QA nuance**: expert disagreement on objective things (e.g., alignment) signals bad data; disagreement on style/aesthetics is **good data** — evidence of genuine preference plurality. - She notes they often lack a feedback loop from labs on what caused what, hence focus on what they can measure themselves. ## Closing Message - **Quality over quantity** in subjective-domain data: expensive, high-taste, deeply understood data "yields far better results than a bunch of noisy data." ## Qualifications / Tensions Noted - She explicitly frames brand adherence and other capabilities as **spectrums, not binaries**. - Acknowledges uncertainty in attributing model improvements to data choices when working with labs. - References a separate talk the next day on the agent-side work (not covered here). # Speaker Attribution & Provenance Record ## Video **Title:** Ending AI Slop — Thais Castello Branco, Taste Labs **Video ID:** `01a15ca8-2cdb-7706-82f6-62fb738fcc0b` ## Speakers ### Thais Castello Branco — Creator / Sole Speaker - Self-identifies at opening: *"I'm Tais. I'm the founder of Taste Labs."* - Speaks for the entire transcript; **no guests, narrators, or other speakers** appear. - No audience questions or commentary captured (only `[music]` at close and audience-presence implied by "you all"). ## Roles of Referenced Parties (Non-Speaking) - **Frontier labs** — Taste Labs collaborates with them on model evaluation, benchmarking, and training methods (speaker's claim). - **Agent and application-layer companies** — clients/collaborators (speaker's claim). - **Community of designers** — "over like a thousand experts" used for data collection (speaker's claim). - **Company designers (generic)** — referenced as creators of brand definitions used in training examples. ## Referenced Sources & Examples - **Reduct (brand example)** — speaker cites using the "reduct" brand as a design decomposition example: *"I'm using the reduct brand as an example here because I like their website."* Third-party brand; no affiliation stated. ## Third-Party Claims - All substantive claims (AI performance gaps, LLM-as-judge reward hacking, mode collapse, data quality findings) are the **speaker's own assertions**; no cited external studies or sources. ## Promotional Segments - **Flagged:** Opening segment promotes Taste Labs' mission and services ("we came out of Stealth a few weeks ago... data and infrastructure layer... work with the top frontier labs"). - **Flagged:** Closing pitch promotes upcoming talk: *"For those that are here tomorrow I'll also be giving a chat on the design track."* ## Availability & Limitations - **Comments availability:** Not specified in supplied source material. - **Curated metadata:** None supplied beyond title and transcript; timestamps absent from transcript. Hello everyone. It's great to meet you all. I'm Tais. I'm the founder of Taste Labs. For those of you who don't know us, we came out of Stealth a few weeks ago. Uh, and our whole mission is basically how do we end AI slop? And we believe that to really solve this problem, we have to first decompose and understand subjective domains. Right? I think as probably all of you know, AI has gotten quite good at things like coding and math. Uh but it's still super behind on things like design, creative writing, personality, emotional intelligence. And to understand these domains, I think we have to take a little bit of a different approach than we do with um with objective ones. So our idea is like how do we become this data and infrastructure layer to really uh understand these problems and to become the solution for them across the stack. So from the foundation model layer all the way to how do we build solutions for agents as well. So we work primarily in two ways. We work with the top frontier labs on how do we evaluate benchmark their models understand where they're breaking uh understand how we can fix them and how do we determine also which problem is better fixed through each method. So what things should be turned into RL environments, which things should be turned into post- training data problems. Um, but then we also go and work with a lot of agent and application layer companies on what are things that we actually don't believe should be solved at the foundation model layer and that might be better solved through methods like context uh or understanding user intent. Right? We're basically betting on a world where suddenly you're going to have billions of people creating uh that are not necessarily experts. So this understanding of user intent and contest and context is equally as important as how do we get these models to improve. For today I'm going to focus on the model training part. For those that are here tomorrow I'll also be giving a chat on the design track where I'll cover more on what we're doing on the agent side of the house. But there we go. Okay. So most of the world is subjective as I was mentioning. Uh a lot of the world is subjective right? If we talk about these domains of writing even workflows within companies right of sales marketing uh a lot of the times there's this multitude of answers there's not one clear right answer and it's very hard to define what great even means I think at the end of the day like those are why these domains are so difficult um and we oftentimes I would say forget to mention like why uh we treat for example the fact that code is verifiable and measurable as something that is a property about models and models are great at at coding um because we've made them great at coding but realistically it's actually a fact about code. Code is something that decomposes, it verifies, it executes and so it makes it a lot easier for us to be able to train on these domains for something like design or writing like how do you decompose it? How do you verify it? How do you judge if it's actually good? So that's why they become so difficult. Um, so there's two characteristics that I want to touch on today on why fundamentally subjective domains are harder. One is that capability follows measurability. So if we can solve the measurability problem or at least part of it, then we can solve a big portion of these domains. Uh the second which I'll touch on later is basically this like collapse to the mean and why the mean is not necessarily optimal in subjective domains. Okay. So to really start solving this problem, we have to turn something that feels fuzzy like if I ask you what is great design into something that is more verifiable. So there's a few questions here, right? Because if I ask you this of what is great design um you could ask yourself, okay, um do you mean great for which type of person, for which type of taste, for which situation? The same slide could be amazing. for example, if you are a startup and completely inappropriate if you are a finance firm. So it's contextual first of all. Second of all, it has this property that it changes over time which is different from other domains. What is considered good today is different than five years ago and different than five years from now. In code that's not necessarily true or in math, right? That's something that is way more consistent over time. So our ability to again decompose it and understand how is this good for a specific audience, how is this good today, how is this good in context um is some of the things that we've been thinking about in terms of how to how to break this down. But I want to give you a very specific example because of course this can mean many things. So let's talk about brand. Um if you're at a company and you've used coding agents, you've probably shipped an internal dashboard. You've probably shipped an internal like landing page. And oftentimes you might wonder, okay, how do I determine if this is slop, if this is actually good? And you have kind of this secret weapon at your disposal, which is really all the work that probably designers at your companies, for example, put into defining your brand. A brand to define takes a lot of effort, takes a lot of care. You're defining all these components about it, when it's good, why you're choosing certain combinations of colors, of typography, of spacing, of texture. Um, but if I just ask you to be like, okay, create something great, that's very hard. But suddenly if I'm like okay make something that is on brand that is a much easier problem to define and a brand is something that can become decomposable. Uh so for example if we I'm using the reduct brand as an example here because I I I like their website. Um let's say that we decompose this brand into the colors, the typography, the motion, the animation, the textures. Suddenly you have these very codified things that you can verify against. Verifying in general if something's on brand and you can try this uh by prompting an LLM as a judge to do it is quite hard. But once you start picking apart the exact elements that represent what great is, then it suddenly becomes the shape of something that is codifiable and verifiable. So if you want to turn this into a shape of an RL environment, for example, right, how would you train a model for a capability like brand adurance? Uh LLM LLM as a judge might not necessarily always be the best method. We know that there's a lot of reward hacking. We know that there's uh interesting hallucination patterns there too. And so we oftentimes try to create methods of basically how do we turn a task that feels fuzzy into one where there's a clear ground truth so that it can become the shape of an environment. So in this case the task design itself is really kind of the hardest part of the problem of how do you turn something that appears very fuzzy into something that actually can be arled. Um and so in this case that decomposition that I mentioned becomes the ground truth. So let's say that you start by tasking an agent to create a new page that is going to adhere to the reductal brand but be completely net new and different. Um you would want that output to not only be graded versus the original but to be graded on this ground truth, right? Because it could come up with completely new ways of using these components that are still valid but are different from the original. So you don't necessarily want to just see if it's replicating the original. So this is one example of like how to turn this into a problem of environment shaped um so that we can make it more verifiable. But in a way all of these things are I would say like spectrums right you have uh in a problem like design you have these elements of things like vision uh alignment typography that are closer to objective once you start moving up that scale onto things like style fit creativity how do you judge and measure something like creativity right that's much harder and so you kind of need to think of this as like a routing problem of how do you understand this like vast fuzzy problem break it down into smaller components and what is the best solution for each of these components. So why does something feel like slop? For example, when we're talking about something like creativity, right? I think this is the second reason why um subductive domains are so much harder to solve because if we're talking about the properties of models, they're basically predicting what's the most likely outcome to show up next. And they assume that that outcome is the ideal outcome. And for something like math encoding, that is true, right? You want the answer that your model gives you to be the average answer. If you're asking what 2 plus 2 is, uh, which also happens to be the right answer and the optimal answer. But for something like writing or design, you don't necessarily want the average answer, right? The average meaning the most likely does not necessarily coincide with like the optimal. Uh, a lot of like what I describe it is a lot of greatness and creativity happens actually at the ends of the distribution. It's not the most likely outcome. It's when you actually actively break from rules and actively break from patterns that you can create things that are subjective and and great. Um, and so the reason why this feels like slop and that we have this feeling that we're surrounded by by slop is exactly because of this collapse to the mean and this repetition. And so we have to find ways of okay, how do we break these patterns? How do we break from the mean uh but in a way that's also intentional. So then you kind of shift the problem onto things that are not so easily maybe verifiable. Uh but that are more questions of human preference and judgment and that might be better solved by data for example than by environments. And so again this kind of like mode collapse is is really the thing that we're trying to solve. And how do we force that distribution back? Uh, oftentimes, by the way, we we work, for example, with a community of designers, um, over like a thousand experts that are experts in different types of medium, different styles, and we purposely want to force that distribution when we're breaking down the problem. Exactly. So, we don't end up in this mode collapse, but there's of course a lot of other pieces of that puzzle. Uh, but I think this is an interesting framework is like the closer you are to something that it becomes verifiable, especially programmatically, the better for something like RL, right? And I think the challenge is how do we turn things that feel fuzzy into things that become more verifiable by establishing this ground truth and designing tasks in a way that allow for that. But then the more that it does shift to things that are contextual or that depend on that time element that we talked about or this like distinction in preference, the more this shifts towards something that requires human judgment. So uh I I I like this analogy of basically kind of pulling things toward verification. So brand adherance on its own would be something that's very hard and that is probably better judged by a human than for example by an LLM as a judge or something deterministic. But by codifying it by understanding which pieces matter and how do I turn that into something that is observable and measurable we can kind of pull it into this realm of verification. uh so this routing logic is I would say if you have one takeaway uh take this away it's like how do we break down this problem into something that you understand what's actually the best method to solve it and so when we are talking about these things that are more subjective uh we do require human judgment I think this is something that we um we believe human judgment is still at a much higher level than any LLM as a judge and this like human taste is really how do we encapsulate this in a way that uh can be turned into high quality data so that we can train these models better. And I think one of the tricky things here is often times in the past you had kind of this uh collection of preference data that would collapse again to the mean because you would collect it from a bunch of different people without necessarily understanding who they are, what they like, why they like it, when they like it. And if you don't break up that problem accordingly, you then end up again with preferences that kind of don't agree with each other. Because that naturally happens in the world, right? I bet that some of you might like one style better than another. And that doesn't mean either of those things are wrong or it doesn't mean that the best answer is the average of what two people might like. It means that we need to fundamentally understand that the world is multi-preference and how do we do that matching accordingly. So this almost like understanding of how do we create like a preference vector let's say for someone and attach that to even something like preference data can help us to train in a way that allows for that more pluralism of preferences intentionally instead of that data being turned into something that's noisy. when we talk about data quality as well for these domains, I think it becomes very uh I don't know if any of you have bought data for example for these domains or or have tried to curate data yourselves. Um but there's it's very hard to define okay like now I'm realming to the side of data. What is actually good? What is actually going to be helpful to my model? Um and there's obviously a few things that are harder to define but a few that I think become more controllable and how do we actually understand patterns of quality so that we can measure them. So one I would say is that problem decomposition. How do we force like true distributions of what you see in the world? How do you force true like expert selection across these u different buckets? And these are things that you can totally control, right? You can see who those experts are. You can do a selection pattern that is very strict um and you can decompose that problem and this is something that is completely in your control and that totally helps with the um results let's say of the experiment being being good. Um the second is I would say that like flow of like how do you determine then the right problem routeed to the right solution. Um we also I would say do a lot of what we call essentially like QA on this data and I think there's two ways to do that is understanding what are properties about that data point that correlate with being it being rich and high signal. So for example, specificity when you're trying to ask an expert to define is this good, is this bad or put reasoning behind it or create a whole observation system around uh how they would judge an asset. The specificity of their language and of how precise they're being able to be with how they're doing that description is what will determine that data quality. Or for example, if you can tie their commentary with actually which piece in the code does this relate to. Like let's say you have an expert that's judging a landing page. Uh they might be able to just like write a paragraph describing this. But we know that models have a tricky time kind of actually connecting the piece of the code to the visual. And so if you can find for example a method to tie that exact code component to the commentary of the expert, suddenly you have data that is way less noisy and way more clear. So these are some things that we can do both in terms of the flow of how you connect the data uh of how you collect the data but also in these like QA checks of how do you determine characteristics about it that will uh correlate highly let's say with with valuable data and same with human QA. I think by the way human QA is tricky here because there's two sides to it. There is a side of for example let's say you're collecting uh a bunch of preference data about slide design. Um you could have human QA be like okay is this again high quality data is the following the specs which most people would agree on but suddenly if you ask for expert consensus and you try to have another expert see if they agree with the initial designers like votes you might start seeing some disagreement there and you I think the key is understanding is that disagreement something that's actually a flaw in the data meaning are they for example disagreeing on something that they should be agreeing on such as alignment like alignment is something that's pretty objective So it would be kind of odd to see experts disagreeing on that front. But suddenly if they're disagreeing on things like sty style or um aesthetics that is not necessarily bad data that's actually good data. It shows you that there is a distinction for what people like. And so uh this almost like analysis of how do you run human QA in a way that both screens for kind of the fundamentals but then when you use consensus I think in an intentional way is another big big piece of this. Um and then obviously kind of seeing actually how this data is is interacting with models. Uh we run a lot of research on our side. Obviously when you're interacting with labs as we do um it there's a lot more involved and we oftentimes don't get that feedback loop of exactly what affected this cause which is why we're so adamant on focusing on these things we can control in terms of data quality because these are things that uh we can completely measure on on our side. Um so I think one other uh piece of advice or message of the day is I think especially when it comes to subjective domains I would advocate for a quality over quantity approach. I think creating high quality data is expensive. It's difficult. It takes a lot of understanding and depth around a specific domain. and having that be incredibly high quality done by people that also are incredibly high taste or whatever you want to call it in that domain uh yield far better results than getting a bunch of noisy data or a bunch of messy data uh that was not necessarily intentional or didn't have all those things we talked about of like the problem breakdown. So that is my my message of of the day. Thank you. \>\> \[music\]