Why is everyone cloning Deep Research?

This is Alessio, partner and CTO of Decibel Partners, and I'm joined by my co-host, swyx, founder of Small Eye.

Hey. Today we're very honored to have Aarush Selvan and Mukund Sridhar from the original Deep Research team in our studio. Welcome.

Thanks. Thanks for having us.

Thanks for making the trip up. I was fortunate enough to be one of the early beta testers of Deep Research when it came out, and I was very keen on it. Even at the end of last year, people were already saying it was one of the most exciting agents coming out of Google. We previously had Riza and Osama from the NotebookLM team, and I think this is part of an increasing trend where Gemini and Google are shipping interesting user-facing products that use AI. Congratulations on your success so far.

It's been great. Thanks so much for having us here. We're excited.

Thanks for making the trip up. I'm also excited for your talk that's happening next week. Obviously, we have to talk about what exactly it is, but I'll ask you about that toward the end. For now, we have the screen up, so maybe we can start at a high level. For people who don't yet know, what is Deep Research?

1. Deep Research Becomes Your Assistant

Deep Research is a feature where Gemini can act as your personal research assistant to help you learn about any topic more deeply. It's really helpful for queries where you want to go from 0 to 50 very quickly on something new.

The way it works is that it takes your query, browses the web for about 5 minutes, and then outputs a research report for you to review and ask follow-up questions about. This is one of the first times something has taken 5 or 6 minutes to perform research for you, so there are a few challenges that brings. You want to make sure you're spending that time and compute doing what the user wants.

There are also UX design considerations that we can talk about as we go through an example. Then there are challenges in browsing the web. The web is extremely fragmented, and being able to plan iteratively as you pass through that noisy information is a challenge by itself.

This is the first time Google is automating the way you search. You're supposed to be the experts at search, but now you're meta-searching—determining the search strategy.

We see it as 2 different use cases. There are things where you know exactly what you're looking for, and search is still probably one of the best places to go. I think Deep Research really shines when there are multiple facets to your question and you spend a weekend opening 50 or 60 tabs. Many times, I just give up, and we wanted to solve that problem and give people a great starting point for those kinds of journeys.

Do we want to start a query so that it runs in the meantime and then we can chat over it?

Here's one query that we love to test: super-niche, random things where there's no Wikipedia page already about the topic. That's where you'll see the most lift from a feature like this. I've come up with this query—it's actually Mukund's query, which he loves to test: “Help me understand how milk and meat regulations differ between the US and Europe.”

2. Research Plans Come First

What's nice is that the first step is where it puts together a research plan that you can review. This is its guide for how it's going to carry out the research.

This was a pretty decently well-specified query, but let's say you came to Gemini and said, “Tell me about batteries.” That query could mean so many different things. You might want to know about the latest innovations in battery technology, or you might want to know about a specific type of battery chemistry.

If we're going to spend 5 to 10 minutes researching something, we want to understand exactly what you're trying to accomplish and give you an opportunity to steer where the research goes. If you had an intern and asked them this question, the first thing they would do is ask you a bunch of follow-up questions: “Help me figure out exactly what you want me to do.”

We thought, why don't we have the model produce its first stab at the research query—how it would break the question down—and then invite the user to engage with how they want to steer it?

Many times, when you try to use a product like this, you don't know what questions to ask or what things to look for. We made the decision deliberately that instead of asking users follow-up questions directly, we would lay out what we would do and show the different facets.

Here, for example, it could be what additives are allowed and how that differs, or labeling restrictions on products in the US and the EU. The aim is to tell the user a little bit more about the topic and get steered at the same time. We also elicit follow-up questions.

It's kind of like editable chain-of-thought.

Exactly.

We were talking to you about your top tips for using Deep Research, and your number-one tip is to edit the plan. Just edit it, right?

You can actually edit it conversationally. We put in a button here just to draw users' attention to the fact that they can edit it. In early rounds of testing, we saw that no one was editing, so we thought that if we put a button here, maybe people would engage with it.

I just hit Start. I think we see that too. Most people hit Start. It's like the “I'm Feeling Lucky” button.

All right, I can just add a step here, and what you'll see is that it should refine the plan and show you a new proposal.

Here we go. It added step 7: “Find information on milk and meat labeling requirements in the US and EU.” Or you can just go ahead and hit Start.

I think it's still a nice transparency mechanism, even if users don't want to engage. You still understand why you're getting the report you're going to get, which is useful.

While it browses the web, Mukund, you should maybe explain how it browses. We show the websites it's reading in real time.

I'll preface this with the fact that I forgot to explain the roles. You're a PM?

Yes.

Okay.

Just for people who don't know, we maybe should have started with that. We know each other's work sometimes as well, but that's how it is. More or less, that's the boundary.

3. Research Browsing Goes Iterative

What's happening behind the scenes is that we give the model this research plan as a contract—something that has been accepted. If you look at the plan, there are things that are obviously parallelizable, so the model figures out which of the substeps it can start exploring in parallel.

It primarily uses 2 tools. It can perform searches, and it can go deeper within a particular web page of interest. Oftentimes, it will start exploring things in parallel, but that's not sufficient. Many times, it has to reason based on information it has found.

In this case, one of the searches could reveal that the EU Commission has banned certain additives. The model then wants to check whether the FDA does the same thing. This notion of being able to read outputs from the previous turn, ground on them, and decide what to do next was key. Otherwise, you have incomplete information and your report becomes a set of high-level bullet points.

We wanted to go beyond that blueprint and figure out what the key aspects were. This happens iteratively until the model thinks it has finished all its steps. Then we enter analysis mode. There can be inconsistencies across sources, so the model comes up with an outline for the report and starts generating a draft. It then tries to revise that by self-critiquing to finalize the report. That's broadly what's happening behind the scenes.

What's the initial ranking of the websites? When you first started it, there were 36. How do you decide where to start, since it sounds like the initial websites carry a lot of weight because they inform what comes next?

In the initial turns—again, this isn't something we enforce; it's mostly the model making these choices—the model typically explores all the different aspects in the research plan that was presented. We get a breadth-first view of the different topics to explore.

In terms of which ones to double-click on, it comes down to what the model learns every time it searches. It gets some idea of what a page contains, and depending on what it finds, there may be inconsistencies or partial information. Those are the pages it double-clicks on.

It can continuously search and browse iteratively until it feels like it's done.

I'm trying to think about how I would code this. Do you think we could do this with the Gemini API, or do you have some special access that we can't replicate? If I model this with tool calls for search, double-click, and whatever else, would that work?

I don't think we have special access per se. It's pretty much the same model. We of course have our own post-training work that we do, and y'all can also fine-tune from the base model and so on.

I don't know that we can do all this fine-tuning.

Well, if you use our Gemma open-source models, you could fine-tune.

Yeah, yeah.

Yeah, so I don't think there's special access per se, but a lot of the work for us is first defining that there needs to be a research plan and how you go about presenting that, and then doing a bunch of post-training to make sure it's able to do this consistently, well, and with high reliability.

Okay, so Gemini 1.5 Pro with Deep Research is a special edition of Gemini 1.5 Pro?

Yes, so it's not purely Gemini 1.5 Pro; it's post-trained.

This also explains why you can't just toggle on Gemini 2.0 Flash.

Right.

Yeah, but I assume you have the data, and you know it should be doable.

Yep. There's still this question of ranking.

Right. And, oh, it looks like you're already done?

Yeah, yeah, we're done. We can look at it. So, let's see. It's put together this report, and what it's done is sort of broken it down. It started with milk regulation, and then it looks like it goes into meat, probably further down, covering how the U.S. approaches the problem of how to regulate milk, comparing it with the EU, and then, like I said, going into meat production.

What's nice is that it also reasons over why there are differences. I think what's really cool here is that it's showing a difference in philosophy between how the U.S. and the EU regulate food. The EU would adopt a precautionary approach, so even if there's inconclusive scientific evidence about something, it's still going to prefer to ban it, whereas the U.S. takes a reactive approach, allowing things until they can be proven to be harmful.

What's nice is that you also get the second-order insights from what it's putting together. So, yeah, it's kind of nice. It takes a few minutes to read and understand everything, which makes for a quiet period during a podcast, I suppose.

Oh, well, this is fun.

But, yeah, this is kind of how it looks right now. From here, you can keep the usual chat-and-iterate flow. Compared to other platforms, it's kind of like Anthropic Artifacts or a ChatGPT Canvas, where you have the document on one side and the chat on the other, and you're working on it.

4. Reports Become Ongoing Work

This is something we thought a bit about. One of the things we feel is that your learning journey shouldn't just stop after the first report, and so what you probably want to do is, while reading, be able to ask follow-up questions without having to scroll back and forth.

There are broadly a few different kinds of follow-up questions. One type is that maybe there's a factoid you want that isn't in here, but it was probably already captured as part of the web browsing that it did. We actually keep everything in context; all the sites that it has read remain in context. So, if there's a piece of missing information, it can just fetch that.

Another kind is, “Okay, this is nice, but I actually want to kick off more Deep Research.” For example, “I also want to compare the EU and Asia in how they regulate milk and meat.” For that, you'd want the model to recognize that this is sufficiently different and that it needs to do more Deep Research to answer the question; it won't find that information in what it has already browsed.

The third is that maybe you just want to change the report. Maybe you want to condense it, remove sections, add sections, and iterate on the report that you got. We've broadly tried to teach the model to be able to do all 3, and this side-by-side format allows the user to do that more easily.

Yeah. So, as a PM, there's an Open in Docs button there, right? How do you think about what belongs there versus—kind of sounds like the condensing and things should be in Google Docs?

Yeah. Bard extensions is different; it's just an amazing editor. Sometimes you just want to directly edit things, and now Google Docs also has Gemini in the side panel. The more we can help this be part of your workflow throughout the rest of the Google ecosystem, the better, right?

One thing we've noticed is that people really like that button and really like exporting it. It's also a nice way to save it permanently, and when you export, all the citations carry over. In fact, I can just run it now, which is also really nice.

Gemini Extensions is a different feature. That's really about Gemini being able to fetch content from other Google services in order to inform the answer. That was actually the first feature that we both worked on on the team: building extensions in Gemini. Right now, we have a bunch of different Google apps, as well as, I think, Spotify and a couple of others. I don't know if we have any Samsung apps as well.

Who wants Spotify? I have this whole thing about how much I love Spotify. What's that in your Deep Research?

In Deep Research, I think less. The interesting thing is that we built extensions and weren't really sure how people were going to use them, and a ton of people are doing really creative things with them. A ton of people are also doing things they loved on the Google Assistant, and Spotify—playing music on the go—was a huge value.

Oh, it controls Spotify?

Yeah. Deep Research purely uses Search.

But this is Search. Otherwise, you can have Gemini go—you have YouTube, Maps, and Search. There's also Gemini 2.0 Flash Thinking Experimental with Apps, the newest—yeah, longest model name that has been launched. Gmail is an obvious one, and Calendar is an obvious one.

Exactly. You know, those are the ones I want.

Yeah, Spotify. Fair enough.

Then, obviously, feel free to dive in on your other work. You're not just doing Deep Research, right? You were just kind of focusing on Deep Research here. I actually asked for modifications after this first run, where I was like, “Oh, you stopped. I actually want you to keep going. What about these other things?” And then continue to modify it. It really felt like a little bit of a copilot-type experience, but more like an agent that would research. I thought it was pretty cool.

Yeah, I think one of the challenges is that currently we kind of let the model decide, based on your query, among the 3 categories. There is a boundary there: depending on how deep you want to go, you might just want a quick answer versus kicking off another Deep Research. Even from a UX perspective, I think the panel allows for this notion that not every follow-up is going to take you 5 minutes.

Right now, it doesn't do any follow-up search, does it?

It always does. It depends on your question. Since we have the liberty of really long-context models, we actually hold all the research material across turns. If it's able to find the answer in things it has already found, you're going to get a faster reply. Otherwise, it's just going to go back to planning.

A bit of a follow-up: since you talk about the product context, I had 2 questions. One, do you have an HTML-to-Markdown transform step, or do you just consume raw HTML? There's no way you consume raw HTML, right?

We have both versions. The models are getting much better at natively understanding these representations. The Markdown step definitely helps because, as you can imagine, there's a lot of noise with pure HTML.

JavaScript, Tailwind CSS, exactly.

When it makes sense, we don't artificially try to make it hard for the model, but sometimes it depends on the kind of access we get as well. For example, if there's an embedded snippet that's HTML, we want the model to be able to work on that too.

And no vision yet?

Currently, no vision yet.

The reason I ask all these things is because I've done the same, but I haven't done vision.

The tricky thing about vision is that I think the models are getting significantly better, especially if you look at the last 6 months, at natively being able to do VQA stuff and so on. But the challenge is the trade-off between having to actually render it and the added latency versus the value add you get.

You have a latency budget of minutes.

Yeah, yeah, yeah, it's true. In my opinion, the places you'll see a real difference are in a small part of the tail. In this kind of open-domain setting, if you just look at what people ask, there are definitely some use cases where it makes a lot of sense to do it, but I still feel it's not in the head cases. We do it when we get there, I guess.

The classic is that it's a JPEG with some important information, and you can't touch it.

Yeah.

And then the other technical follow-up was just: you have a 1 million–2 million-token context. Has it ever exceeded 2 million? What do you do there?

Yeah, so we had this challenge sometime last year when we started wiring up this multi-turn flow. We said, “Hey, let's see how long somebody on the team can take Deep Research.”

What's the most challenging question you can ask that takes the longest?

No, we keep asking follow-ups. For example, here you could say, “Hey, I also want to compare it with—”

Okay, so you're guaranteed to bust it.

Yeah, yeah, yeah. We also have retrieval mechanisms if required. We natively try to use the context as much as it's available, beyond which we have a RAG setup to figure out.

Okay. Is this all in-house tech?

Yes, yes.

5. Long Context Meets RAG

What are some of the differences between putting things in context versus RAG? When I was in Singapore, I went to the Google Cloud team, and they talked about Gemini plus grounding. Is Gemini plus search kind of like Gemini plus grounding? How should people think about the different shades of “I’m doing retrieval on data” versus “I’m using Deep Research” versus “I’m using grounding”? Sometimes the labels can be hard, too.

Let me try to answer the first part of the question. I’m not fully sure about the grounding offering, so I can at least talk about the first part.

I think you’re asking about the difference between when you would do RAG versus relying on the long context. I think we all get that. I was more curious, from a product perspective, when you decide to do RAG versus not. Do you get better performance just by putting everything in context?

The tricky thing with RAG is that it really works well because a lot of these systems are doing cosine distance, a dot-product kind of thing. That gets challenging when your query has multiple different attributes. The dot product doesn’t really work as well. At least for me, that’s my guiding principle for when to avoid RAG.

The second thing is that, with the initial generations of these models, even though they offered long context, you would see some kind of decline as the context kept growing. But as newer-generation models came out, they became really good at picking out fine-grained information, even if you kept filling in the context. So those are my guiding principles.

Just to add to that, a simple rule of thumb that we use is that if it’s the most recent set of research tasks, where the user is likely to ask lots of follow-up questions, that should be in context. But as stuff gets 10 turns ago, it’s fine if that stuff is in RAG, because it’s less likely that the user needs to do very complex comparisons between what’s currently being discussed and the stuff that they asked about 10 turns ago. That’s just a very simple rule of thumb that we follow.

From a user perspective, is it better to just start a new research instead of extending the context?

I think that’s a good question. If it’s a related topic, there’s a benefit to continuing with the thread, because the model, since it has this in memory, could figure out, “I found this niche thing about milk regulation in the U.S. Let me check if your follow-up country or place also has something like that.” You might not catch those things if you start a new thread.

It really depends on the use case. If there’s a natural progression and you feel like this is part of one cohesive project, you should just continue using it. If my follow-up turn is, “I’m just going to look for summer camps or something,” then I don’t think it should make a difference. But we haven’t really pushed that or tested that aspect of it. For us, most of our tests are more natural transitions.

How do you evaluate Deep Research?

6. Evaluation Needs Human Judgment

Oh boy. This is a hard one. I think the entropy of the output space is so high. People love auto-raters, but they bring their own set of challenges.

For us, we have some metrics that we can automatically generate. When we do post-training and have multiple models, we want to make sure that the distribution of certain statistics—such as how long the model spent planning and how many iterative steps it takes on a dev set—doesn’t change unexpectedly. If you see large changes in the distribution, that’s an early signal that something has changed. It could be for better or worse.

So every time you have a new version, you run it across a test suite of cases and see how long it takes?

Yeah, we have a dev set and automatic metrics that can detect behavior end to end. For example, how long is the research plan? Does a new model produce a much longer plan? Not just in terms of the number of characters, but in terms of the number of steps in the research plan. As we spoke about, the model iteratively plans based on previous searches, so we look at how many steps that goes on average over some dev set.

There are some things like this that you can automate. Beyond that, there are auto-raters, but we definitely do a lot of human evaluation. We’ve defined, with the product team, certain things that we care about, and we’ve been very opinionated about them: Is it comprehensive? Is it complete? Is it grounded? So it’s a mix of these two approaches.

Is this where the other challenge is that sometimes you just have to have your PM review examples?

Yeah, exactly.

Broadly, what we try to do for the evaluation question is think about all the ways in which a person might use a feature like this. We came up with what we call an ontology of use cases. We try to stay away from verticals like travel or shopping and instead focus on the underlying research behavior that a person is engaging in.

On one end, there are queries where you’re going very broad but shallow. Shopping queries are an example of that: “I want to find the perfect summer camp. My kids love soccer or tennis.” You want to find as many different options as possible, explore all the options available, and then synthesize a TL;DR about each one. Those are the kinds of journeys where you open many Chrome tabs but then need to take notes somewhere about what’s appealing.

On the other end of the spectrum, you have a specific topic that you want to go very deep on and really understand. There are all sorts of points in the middle, too. Maybe you have a few options that you want to compare, or you don’t want to go super deep on one topic but want to cover slightly more topics.

We developed this ontology of different research patterns. For each one, we came up with queries that would fall within it, and that became the evaluation set. We then run human evaluations on it to make sure we’re doing well across the board.

You mentioned three things. Is it literally three, or is it three out of 20 things? How long is the conversation?

I basically just told you the full set. No, I told you the extremes, and then we had several midpoints. So it goes from something super broad and shallow to something very specific and deep.

We weren’t actually sure which end of the spectrum users would really resonate with. On top of that, you have compounds of those. You can have things where you want to make a plan. A great example is, “I want to plan a wedding in Lisbon, and I need you to help with these 10 things.” That becomes a project with research enabled. It needs to research planners, venues, and catering.

There are compounds that emerge when you start combining these different underlying ontology types, and we also thought about that when we put together our evaluation set.

What’s the maximum conversation length that you allow or design for?

We don’t have any hard limits on how many turns you can do. One thing I will say is that most users don’t go very deep right now. It might just be that it takes a while to get comfortable, and then over time people start pushing it further and further. But right now, we don’t see a ton of users going very deep.

I think the way that you visually present it suggests that you stop when the document is created. You don’t really encourage ongoing chats. The UI doesn’t encourage ongoing chats, even though it was designed like a project.

I think there are definitely things we can do on the UX side to invite the user to say, “Hey, this is the starting point. Now let’s keep going together. Where else would you like to explore?” There are definitely some explorations we could do there.

In terms of how deep people go, I don’t know. We’ve seen people internally dogfood this and push it quite a long way. I think the other thing that will change with time is people uncovering different ways to use Deep Research.

For the wedding-planning example, that’s not one of the first things that comes to mind when we tell people about this product. As people explore and find that it can do these various different kinds of things, some of that can naturally lead to longer conversations. Even for us, when we dogfooded this, we saw people use it in ways we hadn’t really thought of before.

Yeah. That was because this was new for us, and we didn't know: Would users wait 5 minutes? What kinds of tasks would they try that take 5 minutes? Our primary goal was not to specialize in a particular vertical or target 1 type of user. We just wanted to put this in the hands of a busy-parent persona and various different user profiles, see what people tried to use it for, and learn more from that.

How does the ontology of your use case tie back to Google's main product use cases? You mentioned shopping as 1 ontology, right? There's also Google Shopping. This sounds like a much better way to do shopping than going on Google Shopping and looking at the wall of items. How do you collaborate internally to figure out where AI goes?

When I said shopping, I was trying to boil down what exactly the behavior is underneath. That's really around what I called options exploration: You want to see whether you're shopping for summer camps, a product, or scholarship opportunities. It's sort of the same action: You need to sift through a lot of information to curate a bunch of options for yourself. That's what we tried to distill, rather than thinking about it as a vertical.

Google Search is awesome if you want really fast answers. You've got high intent—you know exactly what you want—and you want super up-to-date information. I still use Google Shopping because it's multimodal, and you can see the best prices and stuff like that. I think creating a good shopping experience is hard, especially when you need to look at the thing. If I'm shopping for shoes, I don't want to use Deep Research because I want to look at how the shoes look.

But if I'm shopping for HVAC systems, great. I don't care how it looks, and I don't even know what it's supposed to look like. I'm fine using Deep Research because I really want to understand the specs, how exactly it works, the voltage rating, and stuff like that. I also need to look at contractors who know how to install each HVAC system. I'd say where we really shine when it comes to shopping is at the more complex end of the spectrum, where it matters less what it looks like. It's perhaps less on the consumer-y side of shopping.

One other thing I've observed about the metrics—or the communication of what value you provide—and this also goes into a latency budget, is that I think there's a perverse incentive for research agents to take longer and be perceived as better. People say, “You're searching 70 websites for me,” but 30 of them are irrelevant. I feel like we're in a honeymoon phase where you get to pass all this off. Being inefficient is actually good for you because people care about quantity and not quality. They're like, “This thing took an hour for me; it's doing so much work,” or, “It's slow.”

7. Latency Changes the Product

That was super counterintuitive for us. The 1st time I realized what you were saying was when I was talking to Jason Calacanis, and he was like, “Do you actually just make the answer in 10 seconds and then make me wait for the balance?” We hadn't expected people to value the work it was putting in because you're not actually worried about it. We were really worried about it.

We had actually built 2 versions of Deep Research. We had a hardcore mode that took 15 minutes, and what we actually shipped was something that took 5 minutes. I even went to Eng and said, “There has to be a hard stop, by the way. It can never take more than 10 minutes.”

Yep. Because I think at that point, users will just drop off.

Yep. But what's been surprising is that that's not the case at all; it's been going the other way. When we worked on Assistant, at least, and other Google products, the metric was always that if you improve latency, all the other metrics go up. Satisfaction goes up, retention goes up, all of that.

When we pitched this, it was like, “Hold on. In contrast to all Google orthodoxy, we're actually going to slow everything right down, and we're going to hope that users will still stay engaged.”

Not on purpose.

I think it comes down to the trade-off: What are you getting in return for the wait? From an engineering/modeling perspective, it's trading off inference compute and time to do 2 things: either explore more, to be more complete, or verify more on things that you probably know already. It's a spectrum, and we don't claim to have found the perfect spot. We had to start somewhere, and we're trying to see where there are probably some cases where you care about verifying more than others.

In an ideal world, based on the query and conversation history, you know what that is. I think it basically boils down to 3 things. From a user perspective, am I getting the right value-add? From an engineering/modeling perspective, are we using the compute to explore effectively, and also to verify and go in depth on things that are vague or uncertain in the initial steps?

The other point about the number of websites is that it also comes with a trade-off. Sometimes you want to explore more early on before you narrow down on the sources or topics you want to go deep on. If you look at how Deep Research works for most queries, initially it goes broad. It tries to explore all the different topics mentioned in the research plan. Then you see the choices of websites getting a little narrower around a particular topic or entity that it has come across, and so on. That's roughly how the number fluctuates. We don't do anything deliberate to either keep it low or try to increase it.

Would it be interesting to have an explicit toggle for the amount of verification versus the amount of search?

I think so. Users would always just hit that toggle. I worry that if you give them a Max Everything button, they're always going to hit it. So the question is: Why don't you just decide from the product point of view where the right balance is?

OpenAI has a preview of this—I think it's either in Anthropic or OpenAI—and they have a preview of a model-routing feature where you can choose intelligence, cheapness, and speed. They're all 0-to-1 values, so you just choose 1 for everything. Obviously, they're going to do some normalization, but users are always going to want 1, right?

We've discussed this a bit. If I wear my pure-user hat, I don't want to set anything. I come with a query; you figure it out. Sometimes, based on the query, there will be different requirements. If I'm asking, “How do rising rates from the Fed affect household income for the middle class, and how has that traditionally happened?” you want to be very accurate and precise about the historical trends.

Whereas there's a little more leeway when you're saying, “I'm trying to find businesses near me to celebrate my birthday,” or something like that. In an ideal world, we figure out that trade-off based on the conversation history and the topic. I don't think we're there yet as a research community, and it's an interesting challenge by itself.

This reminds me a little bit of the NotebookLM approach. We also asked Riza about this, and she was like, “People just want to click a button and see magic.” People just want to hit Start every time, right? Most people don't even want to enter the system.

My feedback, if you want feedback, is that I'm still kind of a champion for Devin. Devin will show you the plan while it's working on the plan, and you can say, “Hey, the plan is wrong,” and chat with it while it's still working. It will live-update the plan and then pick off the next item on the plan. Both also have this; that's the most default experience.

I think you should never lock the chat. You should always be able to chat with the plan and update the plan, and the plan scheduler—or whatever orchestration system you have under the hood—should just pick off the next job on the list. That's my 2 cents.

Especially if we spend more time researching. If you watch that query we just did, it was done within a few minutes. By the time it left the research phase, your opportunity to chime in and steer was less. But imagine a world where these things take 1 hour and you're doing something really complicated. Then your intern would totally come check in with you: “Here's what I found. Here's some hiccups I'm running into in the plan. Give me some steer on how to change that or how to change direction.”

You would do that with them. I could see that, especially as these tasks get longer. We actually want the user to come engage.

Way more, to create a good output. I guess Devin had to do this because some of these jobs take hours.

Right. So, yeah. Yeah, totally. Magic. And it's perverse incentives where they charge by the hour, so they make more money the slower they are. Interesting. Have we thought about that before? I'm calling this out because everyone is like, “Oh my God, it takes hours. It does hours of work autonomously for me,” and they're like, “Okay, it's good.” But this is a honeymoon phase. At some point we're going to say, “Okay, but, you know, it's very slow.”

Anything else? Obviously, within Google, you have a lot of other initiatives. I'm sure you sit close to the NotebookLM team. Any learnings coming from shipping AI products in general?

They're really awesome people. They're really nice and friendly, just as people. I'm sure you met them and realized this with Riza and stuff. They've actually been really, really cool collaborators and people to bounce ideas off.

I think one thing I found really inspiring is that they just picked a problem. Hindsight is 20/20, but in advance they said, “Hey, we just want to build the perfect IDE for you to do work, be able to upload documents, ask questions about them, and just make that really, really good.” I think we were definitely inspired by their ability and their vision to say, “Let's pick a simple problem, really go after it, do it really, really well, be opinionated about how it should work, and just hope that users also resonate with that.” That's definitely something that we tried to learn from.

Separately, they've also been really good at extracting the most out of Gemini 1.5 Pro, and they were really friendly about sharing their ideas about how to do that.

I think you learn a bit when you're trying to do the last mile of these products, and the pitfalls of any given model and so on. So, yeah, we definitely have a healthy relationship and share notes, and we're doing the same for other products.

You'll never merge, right? It's just different teams?

They are different teams. They're in Labs as an organization, and the mission of that is to really explore different bets and explore what's possible.

Even though I think there's a paid plan for NotebookLM now.

Yeah. It's the same plan as us, actually.

It's more than just Labs. That's what I'm saying.

It's more than just Labs because, ideally, you want things to graduate and stick around. But hopefully one thing we've done is not created different SKUs, but just been like, “Hey, if you pay for AI Premium, it's cool.”

Yeah, whatever, you get everything. Good thing.

What about learning from others? Obviously, OpenAI has Deep Research, literally the same name. I'm sure there's a lot of contention. Is there anything you've learned from other people trying to build similar tools? Do you have opinions on what people are getting wrong and what they should do differently? From the outside, a lot of these products look the same: ask for research, get back research. But obviously, when you're building them, you understand the nuances a lot more.

When we built Deep Research, there were a few different bets that we took around how it should work. What's nice is that some of those are actually things where we feel like we took the right approach.

We felt like agents should be transparent about telling you up front, especially if they're going to take some time, what they're going to do. That's really where the research plan we showed in a card came from.

We really wanted to be very publisher-forward in this product. While it was browsing, we wanted to show you all the websites it was reading in real time and make it super easy for you to double-click into those while it was browsing. The third thing is putting it into a side-by-side artifact so that, ideally, it was easy for you to read and ask questions at the same time.

What's nice is that, as other products come around, you see some of these ideas also appearing in other iterations of this product. I definitely see this as a space where everyone in the industry is learning from each other. Good ideas get reproduced and built upon. We'll definitely keep iterating and following our users to see how we can make the future better.

And on the model side, OpenAI has the o3 model, which isn't available through the API—the full one. Have you tried it already with the Gemini 2 model? Is it a big jump, or is a lot of the work in the post-training?

I would say, stay tuned. It currently is running on 1.5. The new-generation models, especially these thinking models, unlock a few things. One is obviously better capability in analytical thinking, like math, coding, and these types of things. But there's also this notion that, as they produce thoughts and think before taking actions, they inherently have the ability to critique the partial steps that they take and so on.

So, yeah, we're definitely exploring multiple different options to provide better value for our users as we iterate.

Yeah. I feel like there's a little bit of a conflation of inference-time compute here. One, you can do inference-time compute within the model—the thinking model. Two, you can do inference-time compute by searching and doing more iterations.

I wonder if that gets in the way. Presumably, you've tested thinking plus Deep Research. Does the thinking actually do a little bit of verification and maybe save you some time, or does it try to draw too much from its internal knowledge and therefore search less? Do they step on each other?

Yeah, no, I think that's a really nice callout. This also goes back to the use case. There are certain things that I can tell you from model memory—for example, last year the Fed did a certain number of rate cuts and so on. But unless I source it, it's going to be hallucinated.

Even if I got it right, as a user I'd be very wary of that number unless I'm able to source the .gov website for it. That's another challenge: there are things that you might not optimally spend time verifying, even though the model is saying, “This is a very common fact. The model already knows it, and it's able to reason over it.”

Balancing that out—trying to leverage the model's memory while also grounding it in some kind of source—is the challenging part. I think, as you rightly called out, with the thinking models this is even more pronounced because the models know more and are able to draw more second-order insights just by reasoning over things.

Technically, they don't know more. They just use their internal knowledge more, right?

Yes, but also, for example, with things like math, they've been post-trained to do better math. I think they probably do a way better job in math than the previous ones, in that sense.

Yeah. Obviously, reasoning is a topic of huge interest, and people want to know what the engineering best practices are. We think we know how to prompt them better, but engineering with them is also very, very unknown. Again, you guys are going to be the first to figure it out.

Yeah, definitely interesting times. And there's no pressure, Mukund. If you have tips, let us know.

While we're on the technical elements and technical bets, I'm interested in other parts of the Deep Research tech stack that might be worth calling out. What hard problems did you solve, more generally?

8. Agent Architecture Remains Hard

I think the iterative-planning one—to do it in a generalizable way—was the thing I was most wary about. You don't want to go down the route of teaching the model how to plan iteratively per domain or per type of problem.

Going back to the ontology, if you had to teach the model, for every single type of ontology, how to come up with these traces of planning, that would have been nightmarish. Trying to do that in a super data-efficient way by leveraging a lot of the model's memory was important.

There's also this very tricky balance when you work on the product side of any of these models: knowing how to post-train it just enough without losing things that it knows from pre-training. Basically, not overfitting in the most trivial sense, I guess. The techniques there, the data augmentations there, and multiple experiments to tune this trade-off—that's one of the challenges.

On the orchestration side, this is basically you're spinning up a job. I'm an orchestration nerd. How do you do that? Is it an internal tool?

Yeah, so we built this asynchronous platform for Deep Research, which is basically—most of our interactions before this were synchronous in nature. All chat things are synchronous, right? Now you can leave the chat and come back.

Exactly. And close your computer.

And now it's on Android and rolling out on iOS.

You know, I saw that you said that. I told you we switch roles sometimes.

Okay, you're reminding him, right?

Yeah, we ramped on all Android phones, and iOS is this week. What’s neat, though, is that you can close your computer, get a notification on your phone, and so on.

So it’s some kind of asynchronous engine that you made?

Yes, yes. The other part is this notion of asynchronicity and the user being able to leave. If you build 5- or 6-minute jobs, they’re bound to have failures, and you don’t want to lose your progress and so on. It’s this notion of keeping state, knowing what to retry, and trying to keep the journey going.

Is there a public name for this, or is it just some internal thing?

No, I don’t think there’s a public name for this.

We can name it now. This is our opportunity.

Yeah, we can name it now. The classic names that I used to work with in this area—which is why I’m asking—are workflows. There’s Durable Functions, like back when you were at Meta before, I think. Apache Airflow and Temporal were both at Amazon, by the way. AWS Step Functions would be one of those, where you define a graph of execution.

Step Functions are more static and would not be as able to accommodate Deep Research-style backends.

What’s neat, though, is that we built this to be quite flexible. You can imagine that once you start doing hour- or multi-day jobs, you have to model what the agent wants to do.

Yeah, you have to model what the agent wants to do.

Exactly. In short, it’s stable for hundreds of LLM calls. It’s boring, but this is the thing that makes it run autonomously.

Right. Yeah. Anyway, I’m excited about it. Just to close out the OpenAI thing, I would say OpenAI easily beat you on marketing, and I think it’s because you don’t launch on benchmarks. Should you care about benchmarks? Should you care about Humanity’s Last Exam or MMLU, or whatever?

I think benchmarks are great. The thing we wanted to avoid is the day Kobe Bryant entered the league, who was the president’s nephew, and weird benchmark friends. These are just weird things that nobody talks that way. Why would we oversolve for some sort of benchmark that doesn’t necessarily represent the product experience we want to build?

Nevertheless, benchmarks are great for the industry. They rally a community and help us understand where we’re at.

No, I think you kind of hit the point. For us, our primary goal is solving the Deep Research user value for the use case. The benchmarks, at least the ones that we’re seeing, don’t directly translate to the product. There are definitely some technical challenges that you can benchmark against, but if I do great on HLE, that doesn’t really mean I’m a great deep researcher.

We want to avoid going into that rabbit hole a bit, but we also feel that benchmarks are great, especially in the whole generative AI space, with models coming every other day and everybody claiming to be SOTA. It’s tricky.

The other big challenge with benchmarks, especially when it comes to models these days, is output-space entropy. Everything is text in and text out, so the notion of verifying whether you got the right answer is difficult. Different labs do it in different ways, but we all compare numbers. There’s a lot of art and figuring out how you verify this or how you run it on a level playing field.

I think there’s definitely value in doing benchmarks. At the same time, from a selfish PM perspective, benchmarks are a really great way to motivate researchers.

Yeah, make the number go up.

Exactly. Or just prove you’re the best. It’s a really good way of rallying the researchers within your company. I used to work on the MLPerf benchmarks, and you’d put a bunch of engineers in a room and, in a few days, they’d make amazing performance improvements on our TPU stack and things like that. Having a competitive nature and pressure really motivates people.

There’s one benchmark that is impossible to benchmark, but I just want to leave you with it: Deep Research. Most people are chasing this idea of discovering new ideas. Deep Research right now will summarize the web in a way that’s much more readable, but what will it take to discover new things from the things that you searched?

First, I think the thinking-style models definitely help here because they’re significantly better at reasoning natively and being able to draw these second-order insights. That’s the premise: if you can’t do that, you can’t think of doing what you mentioned.

The other thing is that it also depends on the domain. Sometimes you can prompt a model for new hypotheses, but depending on the domain, you might not be able to verify that hypothesis. In coding and math, there are reasonably good tools that the model already knows how to interact with, so you can run a test, verify the hypothesis, and so on.

Even if you think about it from a purely agent perspective, you could say, “Hey, I have this hypothesis in this area. Go figure it out and come back to me.” But let’s say you’re a chemist. What are you going to do there? We don’t have synthetic environments yet where the model is able to verify these hypotheses by playing in a playground and having a very accurate verifier or reward signal.

Computer use is another one. In both the open-source systems and elsewhere, there are nice playgrounds coming up. If you’re talking about truly being able to come up with new ideas, my personal opinion is that the model doesn’t just have to do the second-order thinking we’re seeing now with these new models. It also has to be able to play and test that out in an environment where you can verify it and give it feedback so that it can continue iterating.

Yeah, so basically code sandboxes for now.

Yeah, in those kinds of cases, it’s a little bit easier to envision this end to end, but not for all domains.

Physics engines. If you think about agents more broadly, there are a lot of things that go into them. What do you think are the most valuable pieces that people should be spending time on? Things that come to mind that I’m seeing a lot of early-stage companies do are memory—we already touched on emails and tool calls—and the auth piece. Should this agent be able to access this? If yes, how do you verify that? What are things that you want more people to work on that would be helpful to you?

I can take a stab at this from the lens of Deep Research. Some of the things we’re really interested in as we push this agent are, first, personalization, which is similar to memory. If I’m giving you a research report, the way I would give it to you if you’re a 15-year-old in high school should be totally different from the way I give it to you if you’re a PhD or postdoc.

You can prompt it, right?

You can prompt it, right? But the second thing is that it should ideally know where you’re at and everything you know up to that point. It should further customize the report and have an understanding of where you are in your learning journey.

Modality will also be really interesting. Right now, we’re text in, text out. We should go multimodal in, but also multimodal out. I would love it if my reports were not just text, but charts, maps, and images. Make it super interactive and multimodal, and optimize for the type of consumption.

The way in which I might put together an academic paper should be totally different from the way I’m trying to do a learning program for a kid, just in the way it’s structured. Ideally, you want to do things with generative UI and things like that to really customize reports. Those are definitely things I’m personally interested in when it comes to a research agent.

The other part that’s super important is that we will reach the limits of the open web. A lot of the things people care about are in their own documents, their own corpora, or within subscriptions that they personally really care about, especially as you go more niche into specific industries. Ideally, you want ways for people to complement their Deep Research experience with that content in order to further customize their answers.

There are 2 answers to this. One is that, in terms of our approach—or for me, rather—trying to figure out the core mission for building an agent, I feel like it’s still early days for us to try to platformize it or build these 5 horizontal pieces that you can plug and play to build your own agent.

My personal opinion is that we’re not there yet. In order to build a super-engaging agent, if I were to start thinking of a new idea, I would start from the idea and try to do that one thing really well.

Yes, at some point, there will be a time when these common pieces can be pulled out and platformized. There’s a lot of work across companies and in the open-source community to provide these tools to build agents very easily. I think those are super useful for starting to build agents, but at some point, once those tools enable you to build the basic layers, I would try to focus on really curating one experience before going too broad.

Yeah, we have Bret Taylor from Cieran. He’s said that Sierra mostly built everything in-house, which is very sad for VCs—the next great framework and tooling and all that. But the space is moving so fast. The problem I described might be obsolete 6 months from now, and I don’t know; we’ll fix it with one more LLMOps platform.

Okay, so just a final point on plugging your talk. People will be hearing this before your talk. What are you going to talk about? What are you looking forward to in New York?

I would love to actually learn from you guys. What would you like us to talk about? Now that we’ve had this conversation with you, what do you think people would find most interesting?

I think a little bit of implementation and a little bit of vision—kind of 50/50—and I think both of you can sort of fill those roles very well. Everyone looks at you as a very polished Google product, and I think Google always does polish very well. But everyone will want Deep Research for their industry.

Now, he’s invested in Deep Research for finance, and they focus on their thing. There will be Deep Researchers for everything, right? You have created a category here that OpenAI has cloned.

So, let’s talk about the hard problems in this brand of agent, which is probably the first real product-market-fit agent. I would say more so than the computer-use ones. This is the one where people are like, “Yeah, it easily pays for $200 worth of stuff a month, probably $2,000 once you get it really good.”

So, let’s talk about how to do this right from the people who did it, and then where this is going.

Yeah, it’s very simple. Happy to talk about that.

For me as well, I’m always curious to see you interact with the other speakers because then there will be other sorts of agent problems. I’m very interested in personalization and very interested in memory. I think those are related problems: planning, orchestration, all those things.

Auth and security—something that we haven’t talked about. A lot of the web is behind auth walls. How do I delegate my credentials to you so that you can go and search the things that I have access to? I don’t think it’s that hard. It’s just that people have to get their protocols together, and that’s what conferences like that are hopefully meant to achieve.

Yeah, no, I’m super excited. For us, we often live and breathe within Google, and we’re just a really big place, but it’s really nice to take a step back and meet people who are approaching this problem at other companies or in totally different industries. Inevitably, at least where we work, we’re in a very consumer-focused space.

I see. Right. I’m more B2B.

It’s also really great to understand what’s going on within the B2B space and within different verticals.

Yeah, the first thing they want to do is Deep Research for my own docs, right? My company docs.

Yeah, so obviously you’re going to get asked for that.

Yeah, there’ll be more to discuss. I’m really looking forward to your talk, and thanks for joining us.

Yeah, cool. Thanks for having us.

Thanks so much, guys.