- Perplexity is an AI-powered “answer engine” that provides direct answers to queries with source citations, combining search capabilities with large language models to reduce hallucinations and increase reliability
- Unlike traditional search engines, Perplexity aims to feed curiosity by offering related questions and encouraging users to dig deeper into topics, viewing itself as a discovery engine
- Perplexity utilizes advanced AI techniques like retrieval augmented generation and chain of thought reasoning to improve accuracy and performance in natural language processing tasks
- The future of AI may involve developing systems capable of higher-level reasoning and natural curiosity, potentially leading to breakthroughs in creating new knowledge and understanding complex truths
- While AI tools like Perplexity enhance human capabilities, they should be viewed as aids to critical thinking and creativity rather than replacements for uniquely human attributes
(Mercola)—In the video above, computer scientist and AI researcher Lex Fridman interviews Aravind Srinivas, CEO of Perplexity, an AI-powered “answer engine.” Unlike typical search engines, which require you to sort through pages of results to find the information you need, Perplexity provides a real-time answer to your query.
One of the pitfalls of current AI technologies like ChatGPT is the tendency to hallucinate or fabricate information on occasion. To minimize this risk, you can ask it to provide source links and verify the accuracy of the information given. However, Perplexity addresses this issue from the start, and while it can still hallucinate, it has a factual grounding.
“[Perplexity] aims to revolutionize how we humans get answers to questions on the internet. It combines search and large language models, LLMs, in a way that produces answers where every part of the answer has a citation to human-created sources on the web,” Fridman says. “This significantly reduces LLM hallucinations, and makes it much easier and more reliable to use for research, and general curiosity-driven late night rabbit hole explorations that I often engage in.”1
Part Search Engine, Part Question-Answering Platform
Fridman describes Perplexity as part search engine — a software system designed to search for information on the internet — and part LLM. LLM is a type of artificial intelligence system trained on vast amounts of text data to understand and generate human-like text. LLMs can perform various language-related tasks such as answering questions, generating content, translating languages and more.
Unlike standard search engines that provide links, Perplexity attempts to directly answer queries. Srinivas explains:2
“Perplexity is best described as an answer engine. You ask it a question, you get an answer. Except the difference is, all the answers are backed by sources. This is like how an academic writes a paper. Now, that referencing part, the sourcing part is where the search engine part comes in. You combine traditional search, extract results relevant to the query the user asked. You read those links, extract the relevant paragraphs, feed it into an LLM …
That LLM takes the relevant paragraphs, looks at the query, and comes up with a well-formatted answer with appropriate footnotes to every sentence it says, because it’s been instructed to do so, it’s been instructed with that one particular instruction, given a bunch of links and paragraphs, write a concise answer for the user, with the appropriate citation.
The magic is all of this working together in one single orchestrated product, and that’s what we built Perplexity for.”
Srinivas, who previously was an AI researcher at DeepMind, Google and OpenAI, says he views Perplexity as a discovery engine that feeds curiosity:3
“The journey doesn’t end once you get an answer. In my opinion, the journey begins after you get an answer. You see related questions at the bottom, suggested questions to ask. Why? Because maybe the answer was not good enough, or the answer was good enough, but you probably want to dig deeper and ask more. That’s why in the search bar, we say where knowledge begins, because there’s no end to knowledge. You can only expand and grow.”
Breakthroughs in AI
Please understand that while Perplexity is not perfect and still exhibits some bias, particularly regarding COVID-19 information, it significantly outperforms Google in almost every other search query. The AI-driven technology behind Perplexity provides more accurate, comprehensive, and nuanced results, making it a superior choice for general searches. Its advanced algorithms ensure that users receive the most relevant and insightful information, setting it apart from traditional search engines.
Srinivas describes several ways Perplexity embraces state-of-the-art advances in machine learning, along with general innovation. This includes retrieval augmented generation (RAG), an advanced technique in natural language processing (NLP) that combines the capabilities of LLMs with information retrieval systems to produce more accurate and contextually relevant responses.
This approach is particularly useful for tasks that require precise and up-to-date information, such as question answering, summarization and dialogue systems. In short, RAG involves the search aspect of the query, but Perplexity goes beyond this. Srinivas says:4
“The principle in Perplexity is you’re not supposed to say anything that you don’t retrieve, which is even more powerful than RAG because RAG just says, ‘OK, use this additional context and write an answer.’ But we say, ‘Don’t use anything more than that too.’ That way we ensure a factual grounding. And if you don’t have enough information from documents you retrieve, just say, ‘We don’t have enough search resource to give you a good answer.’”
They’re also using chain of thought reasoning, which takes NLP tasks up a notch in terms of performance. Chain of thought reasoning in AI refers to the ability of a language model to generate logical, step-by-step explanations or sequences of thoughts that lead to a conclusion or answer. This approach enhances the model’s performance on complex reasoning tasks by encouraging it to articulate the intermediate steps in its reasoning process. Srinivas explains:5
“Chain of thought is this very simple idea where, instead of just training on prompt and completion, what if you could force the model to go through a reasoning step where it comes up with an explanation, and then arrives at an answer?
Almost like the intermediate steps before arriving at the final answer. And by forcing models to go through that reasoning pathway, you’re ensuring that they don’t overfit on extraneous patterns, and can answer new questions they’ve not seen before.”
The Beginning of Real Reasoning Breakthroughs
Whether or not AI is fundamentally capable of higher-level reasoning, akin to human cognitive processes, remains to be seen. Reaching that point, however, relies in part on applying more inference compute, which in AI refers to the computational resources and processes involved in running an AI model to make predictions or decisions based on new data.
This stage is distinct from the training phase, which involves building and optimizing the model. Broken down, inference is the process by which an AI model applies learned patterns to new data to generate predictions, classifications or other outputs. For example, using AI to classify images or predict stock prices.
The compute aspect, meanwhile, refers to the computational power required to perform inference. It involves hardware, software frameworks and algorithms optimized for efficient computation. Srinivas says:6
“Can you have a conversation with an AI where it feels like you talked to Einstein or Feynman? Where you ask them a hard question, they’re like, I don’t know. And then after a week they did a lot of research … and come back and just blow your mind.
I think if we can achieve that amount of inference compute, where it leads to a dramatically better answer as you apply more inference compute, I think that will be the beginning of real reasoning breakthroughs … It’s possible. We haven’t cracked it, but nothing says we cannot ever crack it.”
Curiosity Is a Key Part of What Separates Humans From AI
Part of cracking this code involves teaching AI how to mimic natural human curiosity. “What makes humans special though, is our curiosity,” Srinivas explains. “Even if AIs cracked this, it’s us still asking them to go explore something. And one thing that I feel like AIs haven’t cracked yet is being naturally curious and coming up with interesting questions to understand the world and going and digging deeper about them.”7
Beyond this, there’s a lot of controversy and fear around artificial general intelligence (AGI), which refers to a type of AI that possesses the ability to understand, learn and apply knowledge across a wide range of tasks at a level comparable to human intelligence.
Srinivas says he doesn’t think we need to worry about “AIs going rogue and taking over the world,” but there is an issue of who controls the compute on which AGI runs. “It’s less about access to a model’s weights. It’s more access to compute that is putting the world in more concentration of power and few individuals. Because not everyone’s going to be able to afford this much amount of compute to answer the hardest questions.”
A sign of higher intelligence in AI, Srinivas says, is becoming capable of creating new knowledge and providing truth to questions we don’t know the answers to — and helping us understand why it’s the truth.
“Can you build an AI that’s like Galileo or Copernicus where it questions our current understanding and comes up with a new position, which will be contrarian and misunderstood, but might end up being true? … And the answer should be so mind-blowing that you never even expected it.”8
What’s the Future of Search and AI?
We’re already seeing AI tools like Perplexity, which are exponentially superior to existing search engines. In the future, however, Srinivas says the goal isn’t about building a better search tool but building a platform for knowledge:9
“If you zoom out, before even the internet, it’s always been about transmission of knowledge. That’s a bigger thing than search … So, we imagine a future where the entry point for a question doesn’t need to just be from the search bar. The entry point for a question can be you listening or reading a page, listening to a page being read out to you, and you got curious about one element of it and you just asked a follow-up question to it.
That’s why I’m saying it’s very important to understand your mission is not about changing the search. Your mission is about making people smarter and delivering knowledge. And the way to do that can start from anywhere. It can start from you reading a page. It can start from you listening to an article … It’s just a journey. There’s no end to it.”
Keep in mind that Perplexity and other AI tools are not a replacement for your own critical thinking; rather, they serve as an aid to enhance your creativity. It’s vital to keep this in mind and remember that AI is an adjunct to, not a substitute for, your intellectual and creative capacities.
While precautions need to be taken, including not sharing personal or confidential information, this is not about replacing human action but enhancing it, allowing individuals to focus on aspects of their work that require uniquely human attributes like empathy, strategic thinking, creativity and curiosity. Srinivas explains:10
“So, I think curiosity makes humans special and we want to cater to that. That’s the mission of the company, and we harness the power of AI and all these frontier models to serve that. And I believe in a world where even if we have even more capable cutting-edge AIs, human curiosity is not going anywhere and it’s going to make humans even more special.
With all the additional power, they’re going to feel even more empowered, even more curious, even more knowledgeable in truth-seeking and it’s going to lead to the beginning of infinity.”
- 1 LexFridman.com, Transcript for Aravind Srinivas: Perplexity CEO on Future of AI, Search & the Internet, 0:28
- 2 LexFridman.com, Transcript for Aravind Srinivas: Perplexity CEO on Future of AI, Search & the Internet, 2:05
- 3 LexFridman.com, Transcript for Aravind Srinivas: Perplexity CEO on Future of AI, Search & the Internet, 7:14
- 4 LexFridman.com, Transcript for Aravind Srinivas: Perplexity CEO on Future of AI, Search & the Internet, 1:56:44
- 5 LexFridman.com, Transcript for Aravind Srinivas: Perplexity CEO on Future of AI, Search & the Internet, 1:16:04
- 6, 7 LexFridman.com, Transcript for Aravind Srinivas: Perplexity CEO on Future of AI, Search & the Internet, 1:23:53
- 8 LexFridman.com, Transcript for Aravind Srinivas: Perplexity CEO on Future of AI, Search & the Internet, 1:34
- 9 LexFridman.com, Transcript for Aravind Srinivas: Perplexity CEO on Future of AI, Search & the Internet, 2:34
- 10 LexFridman.com, Transcript for Aravind Srinivas: Perplexity CEO on Future of AI, Search & the Internet, 2:50
Two Storms, One Harvest
Every food crisis in living memory has been a one-shock event. The 2008 price spike was a commodity bubble. The 2020 shortages were a logistics failure. The 2022 grain scare was a war on one exporter’s ports. Each time, the system bent, adjusted, and recovered, and each time the experts assured us afterward that global markets are simply too big and too diversified to fail.
What nobody in Washington seems eager to discuss is that 2026 is shaping up to be something the modern food system has never actually faced. Two independent shocks, one climatic and one geopolitical, are converging on the same harvest cycle at the same time. Not sequentially. Simultaneously.
Start with the weather. The Pacific Ocean is currently building toward what forecasters now openly call a record event. NOAA’s Climate Prediction Center puts the odds of at least a strong El Niño near 88 percent, with roughly two in three odds it reaches “very strong” status, the tier reserved for perhaps three or four events in the entire satellite era. Every major global model now projects a median peak in Super El Niño territory, and most of them project it exceeding the 2015-16 event, which until now held the modern record. Sea surface anomalies were already brushing the super threshold in mid-July, months before these events normally peak. The atmosphere has already shifted into El Niño mode, and the event is forecast to crest in late fall and early winter.
This is not about “climate change.” It’s about the standard cycles of weather, and the cycle we’re currently in is one that has likely devastated societies in the past. We’re better prepared as a society today, but not all Americans are equally prepared.
Serious households have started doing the quiet math on their own. Grocery bills tell part of the story, and the forecast maps tell the rest, which is why long-term food storage has moved from fringe hobby to mainstream line item in the family budget, with established suppliers like Heaven’s Harvest seeing demand from people who five years ago would have rolled their eyes at the idea. That instinct is not paranoia. It is pattern recognition, and the pattern is worth walking through carefully.
Editor’s Note: Heaven’s Harvest IS a sponsor, but the warnings of this article are real and would be written even if we didn’t have a survival food sponsor. With that said, those who take advantage of what they offer can use promo code “Patriot” for 15% off.
The Fertilizer Clock Is Already Running
While the Pacific warms, the second shock has been unfolding in the Strait of Hormuz. The conflict with Iran turned the world’s most important energy chokepoint into a contested waterway, and the consequences reach far beyond the gas pump. Roughly a third of global fertilizer trade moves through Hormuz, and the disruption sent urea prices up 86 percent year over year by March, with a 53 percent jump in a single month.
The World Bank projects energy prices rising about 24 percent in 2026 and fertilizer about 31 percent. By its own accounting, fertilizer prices ran 35 percent higher in the first five months of this year than the same period last year.
Here is the mechanism the nightly news will not explain. Fertilizer is not a grocery item. It is a time-delayed input. The nitrogen a farmer in Iowa or Punjab could not afford to apply this spring does not show up as a problem this spring. It shows up as a thinner harvest six to twelve months later.
The World Bank’s own food security brief concedes that the effects of reduced applications earlier this season “are likely to become visible only later in harvest outcomes.” Translate that from institutional language into plain English and it means this. The damage is already done, it is already in the ground, and we are simply waiting for it to arrive on the shelf.
Now check the calendar. Six to twelve months from the spring planting season lands us squarely in late 2026 and early 2027. Which is precisely when the strongest El Niño in the instrumental record is forecast to peak, bringing its signature droughts to Southeast Asia, Australia, southern Africa, northern Brazil, and South Asia, the very regions that grow the world’s rice, sugar, and oilseeds.
The World Bank warns openly that a strong El Niño “could disrupt multiple crop belts simultaneously” on top of the conflict-driven input costs. Their baseline projection assumes the Middle East disruptions ease by autumn. What in the last two years of Middle East history suggests that assumption is safe?
The System Has No Slack Left
The comfortable answer is that global markets always adjust. But adjustment requires slack, and the slack is gone. Global cereal production is expected to decline from last year’s records even before El Niño does its work. The UN World Food Programme, hardly a den of right-wing preppers, is calling this the most significant disruption to its supply chains since Covid and the invasion of Ukraine, and its supply chain director put the stakes bluntly.
Today’s supply chain challenges are tomorrow’s hunger crisis.
There is also a political dimension that markets cannot price. When food gets scarce, governments do not behave like economists. They behave like politicians. Export bans, hoarding mandates, and panic buying at the national level turned the modest rice shortfall of 2008 into a global crisis, and analysts are already warning that import-dependent nations are the first dominoes.
The 2015-16 Super El Niño, a far weaker event than what is now forecast, threw tens of millions into food stress across Africa and Asia. This one is projected to be stronger, and it arrives with fertilizer already rationed by price and shipping lanes already contested by missiles.
What Joseph Knew
Scripture does not treat preparation for lean years as faithlessness. It treats it as wisdom delivered in advance to those willing to act on it.
Behold, there come seven years of great plenty throughout all the land of Egypt: And there shall arise after them seven years of famine; and all the plenty shall be forgotten in the land of Egypt.
Joseph did not respond to that warning with a hashtag or a committee. He stored grain during the years of abundance, and when the famine came, Egypt stood while its neighbors begged. The lesson is not that famine is certain. It is that the time to prepare is precisely when preparation still looks optional.
Nobody who filled a pantry in a year of plenty has ever regretted it, and nobody standing in an empty aisle has ever been glad he waited for certainty.
None of this calls for panic, and panic is the enemy of sound judgment anyway. It calls for the same unglamorous prudence our grandparents considered ordinary. Keep some cash margin, know your local growers, and put real food in deep storage while it is cheap and available, because the entire arc of this story is that cheap and available is a closing window.
Families looking for a straightforward place to start can visit Heaven’s Harvest and use promo code Patriot for 15 percent off long-term storable food. The forecasts may yet soften, the strait may yet reopen, and we should pray they do. But hope is a fine thing to hold and a foolish thing to eat.
