It is a bit of a heavy morning here in Jerusalem, isn't it Herman? You can almost feel the hum of the city changing when things escalate like they have lately.
Herman Poppleberry here, and yeah, Corn, you are right. There is a specific kind of tension in the air. We are sitting here looking at the latest reports on the conflict with Iran, specifically what people are calling True Promise four, and it really highlights why the topic our housemate Daniel sent over is so relevant right now.
It really does. Daniel was asking about the evolution of AI search. Basically, he is looking at how we have moved from these static models with training cutoffs to systems that can actually reach out and touch the internet. And he is asking if there is still a future for dedicated tools like Tavily or Perplexity now that giants like Google and OpenAI are baking search directly into the model backend.
It is a great question because it touches on the fundamental way we interact with information. For the longest time, the biggest weakness of a large language model was that it was essentially a frozen brain. It knew everything up until, say, twenty twenty-three or twenty twenty-four, but it was completely blind to what happened five minutes ago.
Right, and we have discussed this before in different contexts. I think it was back in episode seven hundred fifty-two when we talked about the rise of the answer engine. We were looking at how we were moving away from that old pigeon English style of searching where you just throw keywords at a box and hope for the best.
And the first solution to that frozen brain problem was what we call RAG, or retrieval augmented generation. That is where you basically give the model a pair of glasses and a library card. Instead of just relying on its internal weights, it goes out, finds some relevant documents, and then summarizes them for you. Tools like Tavily built their whole business on being the best at finding those documents for the AI to read, focusing on high-quality developer-centric data.
But now, as Daniel pointed out, the big players are consolidating. If I am using Gemini or the latest SearchGPT features, I do not need a third party tool to go find the news for me. Google just uses its own massive search index. So, Herman, does that mean the specialized search tools are dead? Is the market gap closing for good?
I think it is helpful to look at this through the lens of vertical versus horizontal integration. Google is the king of horizontal integration. They have the model, they have the index, they have the browser. It is incredibly convenient. But there is a reason we still see companies like Perplexity thriving. It is about the intent of the search and the quality of the synthesis.
You mean the difference between a general query and a deep research dive?
Precisely. Google’s built-in search for Gemini is designed to be a friction reducer. It wants to give you a quick answer so you do not leave the chat interface. But a tool like Perplexity or a specialized API like Tavily is often optimized for different things, like source transparency, academic rigor, or even just bypassing the SEO spam that sometimes plagues general search results. They use what we call semantic re-ranking to ensure the most relevant, non-sponsored content actually reaches the model.
That is an interesting point about SEO spam. If Google’s AI search is drawing from the same index as their main search engine, it is potentially susceptible to the same gaming of the system that we see in traditional search results. A dedicated AI search tool might have different ways of ranking what constitutes a good source for a language model versus a human clicking links.
And let us not forget the developer angle. We talked about this in episode eight hundred eight when we dove into the AI deprecation trap. Developers hate being locked into a single ecosystem where the rules change every week. If you are building an agentic workflow—where an AI agent has to make decisions based on live data—you might want a stable, dedicated search API like Tavily because you can tune it specifically for your needs without worrying that Google is going to change the underlying search algorithm in a way that breaks your app's logic.
That makes sense. It is the classic specialized tool versus the Swiss Army knife. But I want to push on the second part of Daniel’s prompt, because this is where it gets really visceral for us here in Israel. He asked about real-time information. Specifically, can these tools actually tell us what happened five minutes ago? Or is there an inherent delay in how they retrieve data?
This is the holy grail of AI engineering right now. And the answer is... it depends on where the data is coming from. If you are waiting for a traditional search engine to crawl a news site, index the page, and then make it available to the AI, you are looking at a delay. It might be minutes, it might be hours.
Which is useless if you are trying to find out where a rocket just landed or what the latest military briefing said during an active escalation like True Promise four.
That is why Grok, for all the controversy surrounding it, has a massive structural advantage. It is plugged directly into the X real-time data stream. When someone posts a video of an event in Tehran or Tel Aviv, Grok sees that almost instantly. It is not waiting for a journalist to write an article and a crawler to find it. It is consuming the raw signal of the world.
But that signal is incredibly noisy, right? I mean, we have talked about the signal-to-noise ratio in AI memory back in episode eight hundred ten. If the AI is just reading raw social media posts to give you real-time info, how does it know what is true?
That is the trade-off. You get speed, but you lose some degree of verified accuracy. Google’s approach with Gemini is more conservative. They want to wait until there is a reputable source they can point to. So you might get the news ten minutes later, but it is less likely to be a hallucination based on a fake post. However, by March twenty twenty-six, we are seeing "high-velocity indexing" where certain trusted news feeds are indexed in under sixty seconds.
So when Daniel asks if there is an inherent delay, the answer is yes, but the length of that delay is shrinking. I noticed when I was playing around with the latest Gemini updates that it was able to reference events from the Iran conflict that had happened less than fifteen minutes prior. That tells me they are prioritizing certain high-velocity indexes for breaking news.
Right. And you have to think about the infrastructure involved. In the past, search was about building a massive, static map of the internet. Now, it is about having these high-refresh-rate sensors on specific parts of the web. It is like the difference between a satellite photo and a live security camera.
I wonder if we are going to see a shift where search isn't just one thing. Maybe the AI will say, hmm, for this academic question, I will use the deep web index, but for this question about the war, I am going to jump into the live social stream.
That is actually where I think the market for those external tools stays alive. A company like Tavily can specialize in that high-speed, low-latency retrieval. They can build the specialized infrastructure to scrape news sites every sixty seconds, which might be overkill for a general search engine like Google but is essential for someone building a financial trading bot or a news monitoring tool.
It is like what we discussed in episode eight hundred sixty-nine about the death of the generalist. As we hit the data wall, the value shifts to the quality and the freshness of the data you can ingest. If you are just using the same general search as everyone else, your AI isn't going to have an edge.
And let us look at the geopolitical side of this for a second, because we are pro-American and pro-Israel, and we see how information is used as a weapon. In a conflict like the one between Israel and Iran, the speed of information can be the difference between a successful defense and a catastrophe. If an AI can provide real-time synthesis of threats or even just clear up the fog of war by cross-referencing multiple live streams, that is a massive strategic asset.
But it also means the people who control those search backends have a lot of power. If Google or X or whoever is providing the search layer decides to filter certain types of information, the AI is only going to see what it is allowed to see. That is why I think having a competitive market for external search tools is actually a good thing for freedom of information. You do not want a single gatekeeper for what the AI thinks is the current state of the world.
That is a great point, Corn. Consolidation usually leads to more censorship or at least more aggressive guardrails. If everything is through one or two big model providers, they are going to be very sensitive to political pressure or optics. A smaller, dedicated search provider might be more willing to just give you the raw data without trying to curate the narrative as much.
So, looking ahead, do you think we will ever reach a point where the training cutoff is a thing of the past entirely? Where models are learning in real-time as they interact with the world?
We are seeing the early stages of that with things like online learning and dynamic weights, but it is incredibly expensive and difficult to do without the model forgetting its old knowledge—what we call catastrophic forgetting. We talked about this in episode eight hundred forty-six regarding long-standing AI memory. For now, the hybrid approach—a frozen brain with a very fast set of eyes—is the most efficient way to do it.
It is funny, I was thinking about how we used to complain that AI didn't know who the president was. Now, we are complaining if it doesn't know what happened ten minutes ago. Our expectations are scaling even faster than the technology.
That is the nature of the beast. But I think for Daniel’s question about the market gap, the real takeaway is that while consolidation will happen for the average consumer, the professional and developer market will always crave those specialized, high-performance search layers. You want the best possible eyes for your AI, and sometimes the default ones provided by the big companies just aren't sharp enough or fast enough.
Especially when the stakes are as high as they are right now. I mean, when we are looking at the potential for major regional shifts in the Middle East, having an AI that can accurately and quickly parse through the noise is huge. I have been using these tools to try and get a sense of the international reaction to the latest strikes, and the difference between a model with search and one without is literally the difference between a useful tool and a paperweight.
It really is. And I think we should talk about the practical side of this for the listeners. If you are building something today, how do you choose between using a built-in search like Gemini’s or an external one?
Well, if I am looking for simplicity and I am already in the Google ecosystem, the built-in search is a no-brainer. It is fast, it is integrated, and for eighty percent of use cases, it is more than enough. But if I am building something where the accuracy of a specific niche is vital—say, medical research or legal updates—I would still be looking at those specialized providers.
I agree. And I would add that if you need that sub-five-minute latency, you have to look at who has the best real-time pipes. Right now, that is a very small number of players. Google is getting better, but they are still playing catch-up to the sheer speed of social media data in some areas.
It is also about how the information is presented. One thing I love about the dedicated tools is the way they handle citations. When I am looking at news about the war, I want to see exactly which outlet said what. Sometimes the built-in search summaries can get a little too smooth, you know? They blend everything together into one narrative, and you lose the nuance of different reporting.
That is a classic Herman Poppleberry gripe right there! You want to see the receipts. But you are right, the transparency of the search process is just as important as the results. If an AI tells me something happened in Iran, I want to know if that came from a verified news agency or a random account with ten followers.
And that is a technical challenge. It is not just about finding the data; it is about the metadata. Who said it? When? How many other people are confirming it? That kind of cross-referencing is what makes a search tool truly powerful for an AI.
You know, it reminds me of the agentic interview concept we discussed in episode eight hundred ten. The AI isn't just a passive receiver of information anymore. It is becoming an active investigator. It sees a piece of information, realizes it needs more context, and then goes out and searches for that specific missing piece. That kind of iterative searching is much easier to do when you have a high-performance, dedicated search API.
So, we are moving from AI search being a simple lookup to it being a full-on investigation. That is a massive shift. It means the search tool isn't just a library; it is a set of tools for a detective.
And that detective needs to be fast. If the investigation takes ten minutes, the world has already moved on. That is the inherent challenge Daniel mentioned. The latency isn't just about the network speed; it is about the speed of thought for the AI.
I think we should also touch on the cost factor. Using these external tools isn't free. If you are a developer, you are paying for every search. When Google bakes it in, they are often absorbing that cost or bundling it into the model price. That is a huge pressure on the specialized tools to prove they are providing enough extra value to justify the price tag.
It is the same pressure every specialized software company faces when a big platform moves into their space. You have to be five times better to survive. And in the world of AI search, being five times better means being faster, more accurate, and more transparent.
I suspect we will see some of these specialized companies get acquired. It makes so much sense for a mid-tier model provider to buy a company like Tavily just to close the gap with Google.
Oh, absolutely. Consolidation is the name of the game in twenty twenty-six. We are past the explosion of new startups and into the phase where the winners are being picked and the infrastructure is being locked down.
It is a bit of a shame in some ways. That early wild west period was exciting. But for the end user, having these capabilities built in is a massive win. I remember how frustrated I used to get when I had to copy-paste news articles into a chat box just to get a summary.
We are spoiled now, Corn. We really are. We expect the AI to know everything, everywhere, all at once. And for the most part, it is starting to deliver on that.
Even if it still gets the occasional date wrong or misses a detail in the chaos of a breaking news cycle. But hey, that is why we are here to talk through it, right? To provide that human layer of analysis on top of the machine’s search results.
That is the goal. And speaking of human layers, I think it is important for our listeners to realize that even with the best AI search in the world, you still need to exercise critical thinking. Especially when the news is coming from a conflict zone. The AI can find the information, but it can't always tell you the motive behind it.
That is the truth. The search tool gives you the what, but the why is still something we have to figure out for ourselves.
Well said. I think we have given Daniel a lot to chew on here. The future of AI search is definitely more integrated, but there is still a vital role for those specialized high-speed tools, especially as we push the limits of real-time information.
And if you are listening and you are finding this kind of deep dive helpful, we would really appreciate it if you could leave a review on your podcast app or on Spotify. It genuinely helps other people find the show and join the conversation.
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This has been My Weird Prompts. You can find our full archive and the contact form over at myweirdprompts.com. We are also on Spotify and wherever you get your podcasts.
Thanks for joining us today in Jerusalem. Stay safe out there, and keep those prompts coming.
Until next time, I am Corn.
And I am Herman Poppleberry. We will talk to you soon.
