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It might be time for Apple-as-a-service as the company looks to plot a cunning course through the post-globalist freeze — and offering its products as a service could help it do just that.

Apple had been expected to introduce just such a service. In fact, Mark Gurman recently said those plans were quite advanced, but the company shelved the idea, presumably because of its potential impact on “normal” iPhone sales and consequent revenue. 

With the recent round of tariffs from US President Donald J. Trump, things have changed. Given a $3,000 iPhone has become a real possibility under the tax regime, Apple knows it’s going to see a decline in iPhone sales anyway. It knows that those iPhones it does sell will get used longer and it knows that the inevitable cost will put a lot of consumers off from buying these devices.

Apple has also bought itself some time to figure out a way forward, thanks to the planeloads of iPhones it imported into the US just before the tariff announcement was made. These should tide the company over into September, reports claim. That makes sense, because the time isn’t right for the iPhone-as-a-service plan quite yet. 

To put the plan in effect, Apple will need the support of its carrier partners who I expect were quite resistant to the idea before — they liked the margins they made on phones sold through their networks.

However, pragmatism changes things, and even they can see that some money from a lower margin is still better than no margin at all. And when analysts predict iPhone prices could hit $3,000, it’s crystal clear sales will decline.

Think about it

Those tariff taxes will impact almost everything that cannot be made, grown, or harvested in the US. They’re going to be felt, particularly by shoppers in more impoverished socio-economic groups (who also use iPhones). Most shoppers will be much too concerned about the cost of eggs to spring thousands for a phone, and while there will be an elite group of consumers for whom it’s business as usual, most people will endure a crisis of confidence. 

Like the 1920s, there may be a lot of dancing, but not much to dance about.  

The economic precipice the world appears to have been pushed over may be enough to make any iPhone-as-a-service plan look a lot more attractive. After all, it enables cash-strapped consumers to use the smartphone they desire (and perhaps also the watch, tablet, and Mac) for a predictable monthly fee, with AppleCare, iCloud+, and Apple services included, and doesn’t require they laden themselves with credit card debt. 

People are ready to accept it

It’s not as if we’re not ready for such a service. Even back in 2022, CIRP Partner and Co-Founder Josh Lowitz said: “Based on current consumer behavior, iPhone users are primed to adopt a subscription service that provides an iPhone bundled with useful apps. Almost half iPhone owners already finance their iPhone purchase, paying monthly for a new phone. And about one-third trade-in their old phone when they buy a new one. So, a significant portion of the user base is accustomed to never owning a phone, instead basically leasing it.”

Apple also gains. In this case, it benefits from potentially lower, but at least recurring, income upon which to balance its stock. And it benefits from the fact that at the end of the subscription period (or during it if the consumer cannot maintain payments), the devices will be returned for refurbishment, resale/let, and/or recycling.

This also opens up the highly lucrative second-user iPhone market, which is an income stream Apple hasn’t yet fully explored. The iPhone is the most widely sold smartphone on the second-user market and holds its value the longest; company management is said to have been eyeing whether they can extract more from those sales. 

Pros and joes

Taking things a speculative step forward, I can easily imagine the company might choose to keep the highest-end devices out of the subscription loop, making these available for sale only. Apple knows its most affluent customers may be more accepting of a higher price in exchange for a truly cutting-edge product. 

That balance of high-end retail sales and subscription-income, bolstered by all the other plans Apple is putting in place to survive the high-tax transformation of the US economy may help it build a good and viable business in times like these. A different business, but a business all the same

Will Apple do it? Will Apple choose to offer up its products on a rental basis? 

Given sales cadence is going to fall anyway and, logically, product costs will increase, the company might see the plan as a way to pass the poison pill of some of these price increases onto consumers in as easy-to-stomach a remedy as possible. It’s not ideal, of course, but right now company management will be focused on finding the least worse options to help support the future of its business. That’s the context in which such a plan makes sense.

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When Google first launched the NotebookLM beta in late 2023, the company billed it as an experimental note-taking app with a generative AI assistant built in. You can still use it to create notes, but the app has evolved into something much more novel and powerful. Google now calls it “the ultimate tool for understanding the information that matters most to you.”

Here’s our clearer summary of what NotebookLM has become: It’s a web-based workspace where you can upload your documents and other sources and then instruct Google’s Gemini AI chatbot to analyze and synthesize them. Gemini can summarize the source materials, answer questions about them, and create a range of document types based on them, such as study guides or executive briefings. It can even spin up an audio podcast with synthetic hosts discussing the materials.

When used in NotebookLM, Gemini pulls information from the sources you feed it rather than the internet, theoretically making it less prone to hallucinations than many other genAI tools. That said, Google clearly states that NotebookLM can make mistakes. Responses include citations from your sources, which makes it easy to check for accuracy.

NotebookLM comes in free and paid versions. The paid version, called NotebookLM Plus, comes with either a Google One AI Premium plan or a Google Workspace subscription (Business Standard or higher plan). The free and paid versions share many of the same features. The main differences are that with Plus you have fewer usage limitations, can customize the writing style and length of the content generated by Gemini AI, and have advanced options for collaborating on your workspace with others.

In this guide, we take you through how to set up and use the major features of NotebookLM.

Create your first notebook

In NotebookLM, a “notebook” contains one or more sources. You can create multiple notebooks, each representing a project you’re working on and containing sources that are specific to that project.

Caution: Google says that the data you upload is not used to train Gemini AI and will stay private, but the company also advises, “it’s best to avoid submitting any information you wouldn’t feel comfortable sharing.”

To get started, sign in to NotebookLM with your Google account, then click the Create button.

To start a notebook, click the Create button.

Howard Wen / Foundry

On the “Add sources” panel, you can drag-and-drop or upload files that are on your PC. Accepted file formats include PDF, TXT, and MP3. Toward the bottom of the “Add sources” panel, you can click to add other sources: Google Docs and Google Sheets files in your Google Drive, links to web pages or YouTube videos, and text that you’ve already copied to your PC clipboard.

You can upload a variety of file types to a notebook, or link to an outside source.

Howard Wen / Foundry

Once you’ve added a source, you’ll be taken to the workspace for your new notebook. This page is divided into three columns: Sources, Chat, and Studio.

The new notebook has three columns: one for sources, one for chatting with the AI, and one for notes and audio materials.

Howard Wen / Foundry

On the upper left, the title of the source (or the first several words in it) that you added will appear as the title for this notebook. You can change this by clicking the title and typing in a new one that you prefer.

The Sources column will list the source you added, and the Chat column will show a summary of that source. (We’ll get to the Studio column later.)

Add more sources and manage them

In the Sources column, click Add source to add another source. The sources that you add will appear in the Sources column.

When you add a source, it’s selected by default, which means Gemini will use it for analysis in the Chat and Studio columns. However, you may need to refresh your browser tab before Chat column updates to include additional sources in its summary.

Checked sources are included in Gemini’s analyses; unchecked ones are excluded.

Howard Wen / Foundry

To unselect (or select) a source: If you don’t want Gemini to include a source in a summary, click the checkmark to the right of the source to unselect it. Click the checkbox again to re-select the source.

To remove or rename a source: Move the pointer over the source and click the three-dot icon that appears to the left of it.

Get a single-source summary and kick off a related chat

If you want to delve deeper into one of your sources, click the source in the Sources column. A “Source guide” card will open at the top of the column, where Gemini will generate a summary that describes the contents of the source.

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Click a source in the Sources column to get a summary and a list of related topics.

Howard Wen / Foundry

To the right of the summary is a list of key topics. Clicking any of these sends a request to Gemini to elaborate on the topic, and its response will appear in the Chat column.

In this tutorial, though, we’re more concerned with synthesizing data from multiple sources. To dismiss the topic-related chat and return to the summary of selected sources, click the Refresh button at the top right of the Chat column and then click Continue.

Chat with Gemini AI about your sources

The true power of NotebookLM rests in Gemini’s ability to respond to natural-language queries about your sources. You can ask it questions or give it instructions for analyzing the data.  

In the entry box at the bottom of the Chats column, type in your query and click the arrow to the right. The best way to word your requests is in the form of a question.

Or you can use one of the suggested queries that appear below the entry box. You can use the right and left arrows to cycle through them. Click a suggestion and it’ll be posted as a request to Gemini in the chat window.

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Type a request in the entry box or click one of the suggested queries below.

Howard Wen / Foundry

When you enter a request, Gemini may take several seconds to process it and post a response. It analyzes only the sources listed in the Sources column that have a checkmark by them.

When the response is posted in the Chat column, there will be numbers denoted throughout it. These are citations, each representing text from one of your sources Gemini used to create the response. When you click one of these numbers, a panel opens to show you the original text from the source so you can check whether the response is accurate.

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Gemini’s generated response includes citation numbers and links.

Howard Wen / Foundry

Additionally, there are three actions that you can take, which are listed below Gemini’s response:

• Save to note: Click this button to turn the response into a note that will appear under the “Notes” section in the Studio column.
• Copy: Click this icon to copy the response to your PC clipboard.
• Thumbs Up/Down: Click either icon to rate how good you think the response was. This helps to train the AI to give you better results for future requests.

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You can save the response as a note, copy and paste it elsewhere, and/or rate the quality of the response.

Howard Wen / Foundry

To erase the current chat and start over, click the Refresh button at the top right of the Chat column.

Customize Gemini’s responses (in NotebookLM Plus)

The Plus version lets you adjust and customize the conversational style and length of the responses generated by Gemini. Click the icon of slider controls at the top-right corner of the Chat column. On the “Configure chat” panel that opens, select from the following:

• Analyst: Gemini’s results will have a more business-oriented tone.
• Guide: Gemini’s wording will be geared to those you are collaborating or sharing your notebook with who may not be familiar with its contents.
• Custom: Opens an entry box in which you can write a prompt instructing Gemini how you want it to word its responses.
• Longer: Generates more in-depth and lengthy responses.
• Shorter: Generates concise, summarized content.

Create and manage notes

You can create notes in your notebook and optionally use them as additional sources for Gemini to analyze.

To create a new note: In the Studio column under the Notes heading, click Add note. A card for a new note will open at the top of the Studio column.

Click New Note and type in a new title for this note. Then type your text into the body of the note. You can also paste in text and/or images saved on your PC clipboard, format the text (as headings, bold, italic, or bulleted or numbered lists), and insert web links.

Creating a new note.

Howard Wen / Foundry

To save a note: When you’re finished, click the double-arrow icon at the upper right of the note. Your new note will appear in the Notes section of the Studio column. Click it to reopen it so you can read or edit it.

To save a note as a source: With the note open, click the Convert to source button at the bottom of the Studio column. The note will be added to the sources list in the Sources column.

To delete a note: Move the pointer over its title in the Notes list. Click the three-dot icon that appears to the left of it and click Delete note.

To delete all notes at once, or convert all your notes to a source: To the right of the Notes section heading, click the three-dot icon and select Convert all notes to source or Delete all notes.

Selecting Convert all your notes to source combines all your notes together into one source. (Otherwise, you can convert an individual note to a source at any time by opening the note and selecting Convert to source.)

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You can combine all your notes into a single source, or delete them all.

Howard Wen / Foundry

Create targeted documents from your notes

In addition to responding to queries about your sources, NotebookLM can extract content from the sources you select to create specific types of documents such as study guides, briefing documents, FAQs, or timelines.

To create a document: In the Studio column, under the Notes section, click one of the following:

• Study guide: Your selected sources are turned into a guide that’s structured for the reader to learn about them, featuring quiz questions, essay format questions, and a glossary of key terms.
• Briefing doc: This is a summary of key information that Gemini extracts from the selected sources in your notebook. Sections typically include an executive summary, main themes and ideas, and a conclusion.
• FAQ: Information from the selected sources is formatted into a list of Frequently Asked Questions and answers.
• Timeline: Data from the selected sources will be formatted into a chronological representation of events. This format obviously works best with sources that include specific events and dates.

Once you’ve selected one of the above, Gemini will analyze the selected sources in your notebook and generate a draft version of the document type. It’ll appear as a note listed under the Notes section of the Studio column. Depending on how extensive the content is in your selected sources, the draft may look incomplete, so you’ll need to make edits to it.

A briefing document and a timeline generated from selected sources.

Howard Wen / Foundry

Share and collaborate on your notebook

It’s not only you who can benefit from a notebook you create. You can also share it with others, and they’ll be able to query Gemini about the source material too.

When you share a notebook, you can either restrict others’ permissions so that they can only view and query the notebook’s contents, or allow them to edit it, such as by adding, changing, or removing sources or notes in it. Everyone you share a notebook with can interact with Gemini and copy its responses to their own PC clipboard. But only those with Editor status can add a Gemini response as a note or as a new source to your notebook.

To share a notebook: Click the Share button that’s toward the upper right of your notebook’s workspace. A Share panel opens over the workspace.

Inside the entry box, type the name or email address of someone who’s in your Google contacts. (Note: they must already be in your Google contacts. You cannot type in any email address here.) They will be added to the “People who have access” list.

Sharing a notebook.

Howard Wen / Foundry

By default, a person you add will be granted Viewer access to your notebook. If you want them to collaborate with you on the notebook, click Viewer and change it to Editor.

When you are done adding people and setting their access levels, click the Send button. They’ll be notified by email that you’re sharing this notebook with them (unless you unchecked “Notify people”).

In NotebookLM Plus: Under “Viewer access” on the Share panel, you can allow those you’ve granted only Viewer access to see the sources and notes in your notebook. Or you can restrict them from seeing these — they will only be allowed to chat with Gemini about your notebook.

Get AI-spoken discussions of your notes

The splashiest feature that Google has added to NotebookLM since its launch is Audio Overview, which generates a podcast-like spoken discussion between two AI “hosts” (currently supported in English only). They banter back and forth in a friendly tone, sounding eerily like real people, as they summarize key points in your sources.

This feature may come off as an impressive tech gimmick. But it can be a convenient, and entertaining, way for you (and others you share your notebook with) to listen to your notebook content as if it’s a podcast during your daily commute.

Create an Audio Overview

In the Studio column, under the Audio Overview section, click the Generate button. Or, if you click the Customize button: You can type an instruction to guide what you want the AI hosts to focus their discussion on about your sources.

Gemini will analyze your selected sources and generate an audio file. This usually takes several minutes. The exact amount of time depends on the word length and complexity of your sources.

Note: Ensure that the text in your selected sources reads clearly. This will help generate a discussion between the AI hosts that sounds coherent. Source documents that have headings and subheadings can also help to create a logical flow in their spoken discussion.

Once the audio file is generated, it will appear in the Audio Overview section. Click it to start listening.

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Listening to an audio overview in NotebookLM.

Howard Wen / Foundry

Along the top-right of the audio player are icons that let you rate the overall production quality of the audio summary (thumbs up/down) and share it publicly for others to listen to online. Click the three-dot icon to change the playback speed or to download the audio summary as a WAV file.

Interact with an Audio Overview (experimental feature)

Not only can you listen to the two AI hosts talk about your notebook sources, you can join in on their discussion — literally. Click Interactive mode, which will open a panel in the Studio column showing a sine wave responding to their voices. At the bottom of the column, click the Play audio icon (a right-pointing triangle), and when you feel like taking part in their talk, click the Join button.

Speaking into your PC’s microphone, you can interrupt the hosts and ask them a question or make a request to clarify a point or explain something in more detail. It’s as if you’ve called in to a live radio show.

srcset="https://b2b-contenthub.com/wp-content/uploads/2025/03/google-notebooklm-15b-interact-with-audio-summary.png?quality=50&strip=all 753w, https://b2b-contenthub.com/wp-content/uploads/2025/03/google-notebooklm-15b-interact-with-audio-summary.png?resize=227%2C300&quality=50&strip=all 227w, https://b2b-contenthub.com/wp-content/uploads/2025/03/google-notebooklm-15b-interact-with-audio-summary.png?resize=527%2C697&quality=50&strip=all 527w, https://b2b-contenthub.com/wp-content/uploads/2025/03/google-notebooklm-15b-interact-with-audio-summary.png?resize=127%2C168&quality=50&strip=all 127w, https://b2b-contenthub.com/wp-content/uploads/2025/03/google-notebooklm-15b-interact-with-audio-summary.png?resize=64%2C84&quality=50&strip=all 64w, https://b2b-contenthub.com/wp-content/uploads/2025/03/google-notebooklm-15b-interact-with-audio-summary.png?resize=363%2C480&quality=50&strip=all 363w, https://b2b-contenthub.com/wp-content/uploads/2025/03/google-notebooklm-15b-interact-with-audio-summary.png?resize=272%2C360&quality=50&strip=all 272w, https://b2b-contenthub.com/wp-content/uploads/2025/03/google-notebooklm-15b-interact-with-audio-summary.png?resize=189%2C250&quality=50&strip=all 189w" width="753" height="996" sizes="(max-width: 753px) 100vw, 753px">

To join in the AI hosts’ discussion, click Join.

Howard Wen / Foundry

Create and manage more notebooks

Once you discover how useful a NotebookLM notebook is for one project, you’ll want to create more.

On the notebook workspace, click the NotebookLM icon at the upper-left corner to go to the NotebookLM home page. This shows all the notebooks under your account.

Find and manage all your notebooks on the NotebookLM home page.

Howard Wen / Foundry

To create a new notebook: Simply click the Create new button, which will open a new workspace for a blank notebook.

To manage your notebooks: On the NotebookLM home page, you can change the title of a notebook or delete it by clicking the three-dot icon on the upper-right of its card. (If your notebooks are shown in list view, this icon will be to the right of the notebook’s title.)

This article was originally published in February 2024 and updated in April 2025.

Related:

• How to use Gemini AI to write (and rewrite) in Google Docs and Gmail
• How to use Gemini AI to make templates in Google Sheets
• Google Workspace: 7 great ways to use the Gemini AI sidebar
• Google Workspace tips and tutorials

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Mesmerized by the scalability, efficiency and flexibility claims from generative AI (genAI) vendors, enterprise execs have been all but tripping over themselves trying to push the technology to its limits. 

The fear of flawed deliverables — based on a combination of hallucinations, imperfect training data and a model that can disregard query specifics and can ignore guardrails — is usually minimized. 

But the Mayo Clinic is trying to push back on all those problematic answers. 

In an interview with VentureBeat, Matthew Callstrom, Mayo’s medical director, explained: “Mayo paired what’s known as the clustering using representatives (CURE) algorithm with LLMs and vector databases to double-check data retrieval.

“The algorithm has the ability to detect outliers or data points that don’t match the others. Combining CURE with a reverse RAG approach, Mayo’s [large language model] split the summaries it generated into individual facts, then matched those back to source documents. A second LLM then scored how well the facts aligned with those sources, specifically if there was a causal relationship between the two.”

(Computerworld reached directly to Callstrom for an interview, but he was not available.)

There are, broadly speaking, two categories for reducing genAI’s lack of reliability: humans in the loop (usually, an awful lot of humans in the loop) or some version of AI watching AI. 

The idea of having more humans monitoring what these tools deliver is typically seen as the safer approach, but it undercuts the key value of genAI — massive efficiencies. Those efficiencies, the argument goes, should allow workers to be redeployed to more strategic work or, as the argument becomes a whisper, to sharply reduce that workforce.

But  at the scale of a typical enterprise, genAI efficiencies could replace the work of thousands of people. Adding human oversight might only require dozens of humans. It still makes mathematical sense.

The AI-watching-AI approach is scarier, although a lot of enterprises are giving it a go. Some are looking to push any liability down the road by partnering with others to do their genAI calculations for them. Still others are looking to pay third-parties to come in and try and improve their genAI accuracy. The phrase “throwing good money after bad” immediately comes to mind.  

The lack of effective ways to improve genAI reliability internally is a key factor in why so many proof-of-concept trials got approved quickly, but never moved into production. 

Some version of throwing more humans into the mix to keep an eye on genAI outputs seems to be winning the argument, for now. “You have to have a human babysitter on it. AI watching AI is guaranteed to fail,” said Missy Cummings, a George Mason University professor and director of Mason’s Autonomy and Robotics Center (MARC). 

“People are going to do it because they want to believe in the (technology’s) promises. People can be taken in by the self-confidence of a genAI system,” she said, comparing it to the experience of driving autonomous vehicles (AVs).

When driving an AV, “the AI is pretty good and it can work. But if you quit paying attention for a quick second,” disaster can strike, Cummings said. “The bigger problem is that people develop an unhealthy complacency.”

Rowan Curran, a Forrester senior analyst, said Mayo’s approach might have some merit. “Look at the input and look at the output and see how close it adheres,” Curran said. 

Curran argued that identifying the objective truth of a response is important, but it’s also important to simply see whether the model is even attempting to directly answer the query posed, including all of the query’s components. If the system concludes that the “answer” is non-responsive, it can be ignored on that basis.

Another genAI expert is Rex Booth, CISO for identity vendor Sailpoint. Booth said that simply forcing LLMs to explain more about their own limitations would be a major help in making outputs more reliable.

For example, many — if not most — hallucinations happen when the model can’t find an answer in its massive database. If the system were set up to simply say, “I don’t know,” or even the more face-saving, “The data I was trained on doesn’t cover that,” confidence in outputs would likely rise.

Booth focused on how current data is. If a question asks about something that happened in April 2025 — and the model knows its training data was last updated in December 2024 — it should simply say that rather than making something up.  “It won’t even flag that its data is so limited,” he said. 

He also said that the concept of “agents checking agents” can work well — provided each agent is assigned a discrete task. 

But IT decision-makers should never assume those tasks and that separation will be respected. “You can’t rely on the effective establishment of rules,” Booth said. “Whether human or AI agents, everything steps outside the rules. You have to be able to detect that once it happens.”

Another popular concept for making genAI more reliable is to force senior management — and especially the board of directors — to agree on a risk tolerance level, put it in writing and publish it. This would ideally push senior managers and execs to ask the tough questions about what can go wrong with these tools and how much damage they could cause. 

Reece Hayden,  principal analyst with ABI Research, is skeptical about how much senior management truly understands genAI risks.

“They see the benefits and they understand the 10% inaccuracy, but they see it as though they are human-like errors: small mistakes, recoverable mistakes,” Hayden said. But when algorithms go off track, they can make errors light years more serious than humans.

For example, humans often spot-check their work. But “spot-checking genAI doesn’t work,” Hayden said. “In no way does the accuracy of one answer indicate the accuracy of other answers.”

It’s possible the reliability issues won’t be fixed until enterprise environments adapt to become more technologically hospitable to genAI systems.

“The deeper problem lies in how most enterprises treat the model like a magic box, expecting it to behave perfectly in a messy, incomplete and outdated system,” said Soumendra Mohanty, chief strategy officer at AI vendor Tredence. “GenAI models hallucinate not just because they’re flawed, but because they’re being used in environments that were never built for machine decision-making. To move past this, CIOs need to stop managing the model and start managing the system around the model. This means rethinking how data flows, how AI is embedded in business processes, and how decisions are made, checked and improved.”

Mohanty offered an example: “A contract summarizer should not just generate a summary, but it should validate which clauses to flag, highlight missing sections and pull definitions from approved sources. This is decision engineering defining the path, limits, and rules for AI output, not just the prompt.”

There is a psychological reason execs tend to resist facing this issue. Licensing genAI models is stunningly expensive. And after making a massive investment in the technology, there’s natural resistance to pouring even more money into it to make outputs reliable.

And yet, the whole genAI game has to be focused on delivering the goods. That means not only looking at what works, but dealing with what doesn’t. There’s going to be a substantial cost to fixing things when these erroneus answers or flawed actions are discovered.

It’s galling, yes; it is also necessary. The same people who will be praised effusively about the benefits of genAI will be the ones blamed for errors that materialize later. It’s your career — choose wisely.

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