5 Business Uses for NotebookLM

Today on the AI Daily Brief, a big update for NotebookLM and before that, in the headlines, another AI personnel shuffle over at Google. The AI Daily Brief is a daily podcast and video about the most important news and discussions in AI. To join the conversation, follow the Discord link in our show. Notes. Well, the reshuffling of AI personnel continues with some big changes at Google. First, the Gemini team will be folded into the Google DeepMind division. This team is apparently only responsible for the UI and product design around Google LLMs once they have been trained. In a blog post, the company said bringing the teams together will improve feedback loops, enable fast deployment of our new models in the Gemini app, make our post training work, proceed more efficiently and build on our great product momentum. This move of course, represents a continuation of efforts to consolidate AI teams within the company. The team in charge of models, research and responsible AI were brought inside DeepMind and shortly afterwards DeepMind itself was merged with Google Brain, a rival research unit within the company. The net impact of this is that DeepMind CEO and recent Nobel Prize winner Demis Hassabis will have a vastly expanded team and enhance control over AI strategy across the company. There does seem, however, to be a clear mandate to not only have a research focus, but to also have a product focus in an effort to keep up with rivals. In an interview last month, Eli Collins, DeepMind's vice president of products, said so many of the leading research labs are actually product companies at this point and commented that DeepMind, quote, really has to pick up the pace. The second big change announced was that Senior VP of Search and Advertising, Prabhakar Raghavan has been promoted to the overall company's Chief technologist. In the same blog post, CEO Sundar Pichai Prabhakar has decided it's time to make a big leap in his own career. After 12 years leading teams across Google, he'll return to his computer science roots and take on the role of Chief technologist. In this role, he'll partner closely with me and Google Leads to provide technical direction and leadership and grow our culture of tech excellence. Now Sundar's note says that Prabhakar's leadership journey at Google has been remarkable, but another word that some might use is controversial. In 2019, when he was then the head of advertising, he led a push to change the way Google monetizes search. Then head of Search Ben Gomez sent a firm wide email where he warned that search was, quote, getting too close to the money and stated that he was, quote, concerned that growth is all that Google was thinking about Raghavan has also been at the center of recent antitrust complaints against Google. The DOJ is of course alleging that the company maintains a monopoly in Internet search and advertising through the use of anti competitive behavior. Given all that, it is to some extent unclear whether this promotion will give Raghavan increased power over the firm or is a very polite way of stashing him away in a corner office. Longtime Google exec Nick Fox has been appointed to replace Raghavan as head of search and advertising. Now back to the Bringing together the AI team. This to me just makes sense, and to the extent that anything was weird here, it's the fact that it took Google this long to bring all of its AI together. Now big companies get kind of sprawling, but we've seen this type of move from Microsoft as well. Obviously they brought in Mustafa Sulaiman, another former Googler and former DeepMind co founder, to be the CEO of AI overall at Microsoft, presumably to coordinate strategy there as well. Jordan Thibodeau tweets, more power is being consolidated under Demis. He has waited 10 years for this moment. Now it's his time to shine. The question is, will Google's bureaucracy get out of its way so DeepMind can shine? Next up, another AI entertainment industry tie up with Meta partnering with Blumhouse Productions to showcase their new generative video product moviegen. In a blog post Meta wrote at launch, we shared our plans to work closely with filmmakers and creators to integrate their feedback as we continue working on these models. Connor Hayes, the VP of Moviegen, added, while we're not planning to incorporate Moviegen models into any public products until next year, Meta feels it's important to have an open and early dialogue with the creative community about how it can be the most useful tool for creativity and ensure its responsible use. Meta has selected a small group of filmmakers to test and provide feedback on the product, including this gent, Casey Affleck. The announcement of the collaboration touched on the controversy around AI's use in filmmaking, with Meta positioning Moviegen as a powerful tool for creatives rather than as a replacement for filmmaking talent. Jason Blum, the CEO of Blumhouse, commented, artists are and forever will be the lifeblood of our industry. Innovation and tools that can help those artists better tell their stories is something we are always keen to explore and we welcome the chance for some of them to test this cutting edge technology and give their notes on its pros and cons while it's still in development. These are going to be powerful tools for directors and it's important to engage the creative industry in their development to make sure they're best suited for the job. Alongside the partnership, Meta also released open source versions of their generative video and audio benchmarks, which they quote, hope will help enable the AI research community to progress work on more capable audio and video generation models. The release included another collection of generated videos which are every bit as impressive as the first batch we saw earlier this month, and AI artist Diamond J summed up the sentiment on X posting but I want to use it lastly, today TSMC reported blowout quarterly earnings, renewing AI excitement on Wall street the monopoly manufacturer of advanced chips reported a 54% jump in net profit in the third quarter. The company has also boosted forecasts, now expecting a 30% rise in sales year on year. When asked if AI is in a bubble, CEO CC Way stressed that quote, AI demand is real and I believe it's just the beginning. On overall chip demand, he added, everything stabilized and started to improve. Elaborating on how real the demand is, Wei explained, why do I say it's real? Because we have our real experience. We have been using AI and machine learning in our fab and R and D operations. By using AI, we are able to create more value by driving greater productivity, efficiency, speed and quality. And think about it, 1% productivity gain that was almost equal to a billion won to tsmc. This is a tangible ROI benefit and I believe we cannot be the only company that has benefited from this AI application. So I believe a lot of companies right now are using AI for their own improving productivity, efficiency and everything. Now of course, over the summer there had been growing skepticism around AI on Wall street, perhaps exemplified by a Goldman Sachs report in June which questioned the industry's ability to generate a return. These concerns simmered down in the intervening months and began reversing last month. In September, Nvidia CEO Jensen Huang really brought a fine point to the issue two weeks ago when he said the demand for new Blackwell units was, quote, insane. However, TSMC's earnings report is the first time we've seen this insane demand show up in the financials. It seems to have shredded the last remaining doubt on Wall Street. TSMC stock was up almost 10%, hitting a new all time high. Indeed, we might be at the start of another leg in the AI boom, with Wedbush analysts writing that there is, quote, no end in sight to AI derived strength. Today's episode is brought to you by Fractional when we wanted to build an AI powered feature of Superintelligent, our AI tool finder, I went straight to Fractional. The Fractional team is a group of senior engineers in San Francisco working on some of the most exciting projects in applied AI. Working with them is basically like hiring an absolute top flight AI engineering team, but in a way that you can customize exactly for your particular needs. Like I said, that AI tool Finder feature that we built with them is already a key part of the Super Intelligent platform and we are working on something new as well. Fractional works with everyone from startups to the Fortune 500 and to request a free consultation you can go to Fractional AI. If you want help identifying and building AI projects for your business, then I highly recommend you hit pause, open a web browser and go to Fractional AI to request a free consultation. Today's episode is brought to you by Venice. The leading AI companies store your entire conversation history and attach it to your identity forever. That's every question you ask, every answer you receive, every image you generate, every thought you share with the machine, it's all being spied on. 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To recognize the end of summer and back to school slash back to work. We are running our best promotion ever when you sign up for Super Intelligent Using Code So back, your first month will be 100% free. The platform features over 600 fun, highly practical AI tutorials that get you using AI fast and with an eye to actually transforming how you get things done. We've just launched super for Teams, so if you have a group of people at your company that want to figure out how to use AI together, I highly suggest you check it out. But for those of you who are using superintelligent as an individual. Once again, if you sign up for Super Intelligent between now and the end of the month using code so back you will get your first month 100% free. Go to be super AI and check it out today. Welcome back to the AI Daily Brief. Today we get to do one of my favorite things, which is a product feature. Friday there are a couple of exciting updates to various products that are out there in the AI space that I think could have some pretty significant impacts and also happen to reveal what I think are some broader trends as well. Where we want to start is with what I believe is the buzziest AI product we've seen, at least for a business audience, since the launch of ChatGPT, and that is Google's Notebook LM. And to get a sense of how excited people are about this, rather than just taking my word for it with that sort of hyperbole and chatgpt comparison, I'll turn it over to OpenAI founder Andrej Karpathy who wrote it's possible that NotebookLM podcast episode generation is touching on a whole new territory of highly compelling LLM product formats. Feels reminiscent of ChatGPT. Maybe I'm overreacting now. NotebookLM has actually been around for a while. It's a product from Google that allows you to add a bunch of documents to a single space and convert them into a bunch of different formats. Like you could create FAQs, you could create summary briefing documents. But when things really heated up for NotebookLM was when they added the audio overview feature. They described them as lively deep dive discussions that summarize the key topics in your sources. Everyone else, however, just called them automated podcasts. Almost immediately people started experimenting with all the different ways they could use this, and these podcast type conversations that were produced were really good. They feature two hosts who are talking together who, as many have commented, even have some of the annoying tropes of podcaster interactions. While some people's first question was is this going to change podcasting? To me it felt like that question was really missing the forest for the trees. The idea that you can take any document or set of documents and turn it into a simple audio summary creates a totally new modality for knowledge consumption that I think is going to become de rigueur and just common across almost any domain in which knowledge consumption is valuable. Students are going to use this to start studying. Businesses are going to use this to prep their people on new topics that they have to deal with or competitors. In other words, it's not that we're going to get a bunch of podcasts that look like the podcast of today just created automatically. With Notebook lm, what you're going to get is a massive uptick in general in people consuming audio summaries of topics that are relevant for whatever they happen to be doing or interested in. However, there was one really big challenge in terms of actual applicability for businesses and for content creators like myself, and that was that up until now there was no way to guide the audio generation. All you could do was press a single button and you had to deal with whatever came out. Turns out, more customizability and more guide ability of audio overviews was their number one most requested feature, and the team at Google listened. Risa Martin, the product lead at Notebook lm, yesterday tweeted new Notebook LM updates rolling out today. Pass a note to the hosts. You can now click on Customize in Audio Overviews to give additional instructions such as focusing on a specific topic source or even adjusting the audience it's optimized for. And this is the exact feature that people had been requesting. So now instead of just giving it a mess of documents and hoping it produces something good, I could for example request that it hone in on one specific document and give it more credence than the others, even if it took the others into consideration as well. I could guide it to focus on a particular topic within a larger set of conversations. I could let it know that it's for executives who need a very high level summary versus experts who need a lot of details. In short, this is the feature update that makes this much more performant for business. And it turns out that's a good thing because businesses are already flocking to Notebook LM. In that same post, Riser writes that over 80,000 organizations are already using NotebookLM. One of their other announcements then was NotebookLM business, which is an upcoming version that'll be offered via Google Workspace that has an enhanced set of features for business, university and organizational use. So what are some of the ways that you could use this? A couple that I shared on LinkedIn. The first is for customer service insights. This idea I cribbed from Ramp, who has been doing this in a much more harder or manual way. But basically every day Ramp takes the logs of all of their customer service calls and creates an auto generated podcast that summarizes the most important themes in five minutes. This gives everyone in the company the chance to get direct feedback from customers every day without wading through a mass of logs. I think NotebookLM makes that sort of thing viable for basically any company that's interacting with customers. You could simply feed NotebookLM the transcripts from the day or the week or whatever the right time interval is from customer service calls or other customer service interactions, and have NotebookLM create a podcast about the most important insights. Given how much direct feedback this is from customers, I think this could be extraordinarily valuable and democratize access to direct customer feedback across an entire organization. Another idea, sort of in the same vein, would be to go a step farther than the sort of meeting transcription and summary that we've started to see with services like otter and fireflies, etc. At this point, pretty much every meeting that you're in, I'm sure whether it's Zoom or Google Meet, has a slew of AI note takers sitting there capturing all the things that are discussed. Now a lot of those tools do a great job of summarizing the key takeaways, giving you insight into who was speaking the most and what the biggest themes were. But imagine that you're working on some big mission critical project and over the course of a given week there are 10 or 15 different conversations that happen about that project. Now imagine giving Notebook LM all of those transcripts, indeed, maybe even just the summaries from all of those transcripts, and having it create an even higher level summary podcast that gives executives or anyone else in the company the chance to, in just five to 10 minutes, really understand the progress that was made on the project that week. What are the big challenges that remain? It feels like this could be a total game changer when it comes to internal alignment. A third idea that I shared was for a conversational sales pitch. Now this is something that you could also use ChatGPT advanced voice mode for. But the basic idea here is that most companies have a lot of written collateral about their products or services, but there's a big difference between what you send people to read and the way that you describe things conversationally. A good salesperson or any person who's in a position to pitch something, figures out how to translate from that sort of written detail type medium into that more conversational medium. Well, NotebookLM can just do that for you as well. Feed it all of those written materials and see how these auto generated hosts interact around the product or services benefits in a conversational way. Now this is just the tip of the iceberg. Once you start thinking about this type of audio summarization, you can't really throw a stone without hitting a new use case idea when it comes to R and D. One of the Things that I think we're already seeing a ton of usage around NotebookLM is people taking very dense academic papers and getting the gist of them at least getting the starting point in this sort of more conversational format. And then of course there are all of the just straight up marketing use cases for this where people could actually be creating their own company podcasts in a much easier way. Anyways, as you can probably tell, I am hugely bullish on this particular tool. I think it's going to beget a ton of really exciting use cases and it really is one of the more exciting things that we've seen in some time now. Another really exciting tool is Perplexity, and that company just keeps pushing forward. Yesterday they announced Internal Knowledge search and an update to Spaces. So internal knowledge search is a huge idea. Perplexity is of course a research tool that searches the web, but now with their Pro and Enterprise Pro accounts you can search not only the web, but internal knowledge databases as well. So all of those past materials, internal research, call notes, et cetera, all become something that you can search through with Perplexity, and which Perplexity can offer some of this sort of summarization as well. Now Spaces are basically the collaboration hubs for how teams are actually using Perplexity. With a particular space, you can organize research, you can organize files, you can provide custom access to specific people. And they additionally announced that they'd be starting to add third party data integrations to starting with Crunchbase and factset again, allowing all of this to come together even more. Now, if you're saying to yourself, hmm, it'd be pretty cool to have a Notebook LM style podcast creation embedded in that space as well. Well, it seems like that might be on the way as well, given that CEO Aravan Srinivas said, who wants podcasts on our Spaces, I would be shocked if we didn't see this sort of feature basically imminently. I think these two experiences are going to be hugely transformational for companies. I'm incredibly excited to start getting use cases for Notebook LM and for Perplexity Spaces up on Super Intelligent. If you want to learn more about that and how you can get more use case inspiration, Check out BeSuper AI for now though, that is going to do it for today's AI Daily Brief. Until next time, peace.