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Don’t sleep on Google Gemini’s Deep Research mode: 8 examples of informative reports

Join our daily and weekly newsletters for the latest updates and exclusive content on industry-leading AI coverage. Learn More Many of us in the AI and business worlds are focused — anecdotally and in terms of the number of articles and messages being written/posted — on OpenAI and DeepSeek, especially OpenAI’s o3-powered Deep Research mode, […]

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Many of us in the AI and business worlds are focused — anecdotally and in terms of the number of articles and messages being written/posted — on OpenAI and DeepSeek, especially OpenAI’s o3-powered Deep Research mode, a new reasoning AI agent that performs extensive web research on behalf of the user and compiles it into neat and tidy, well-cited reports.

This is natural since it’s a relatively new product (announced earlier this month) and OpenAI remains among the most highly-regarded and widely used AI model providers. Plus, CEO Sam Altman recently shared plans to make this product available outside of the current $200-per-month ChatGPT Pro subscription, at least on a limited trial basis.

Yet for those seeking to use AI to perform Deep Research and have it write reports for you, there’s another model worth checking out without waiting for OpenAI’s Deep Research to make its way to more affordable subscription tiers or shelling out the $200-per-month for the ChatGPT Pro plan.

Search giant Google’s own Deep Research mode, powered by its prior generation Gemini 1.5 Pro model, is available now on Google’s Gemini chatbot online through the Google One AI Premium plan (~$20 USD per month), and performs many of the same functionality as OpenAI’s Deep Research at 1/10th the monthly cost. Google actually offers the first month free, currently.

It also allows you to export the resulting reports directly to Google Docs with one click. For those that use Google Workspace apps like Docs, this is an incredibly helpful and natural integration.

How to use Google Deep Research to generate reports in minutes

To access it, subscribe to the Google One AI Premium plan using the link above, then navigate to gemini.google.com, click the drop down menu labeled “Gemini Advanced” in the upper left corner, and select “1.5 Pro with Deep Research.” Every query you type into the entry bar at the bottom after this will now engage Deep Research mode.

After the user enters the prompt, the Deep Research agent will draft a research plan for the user’s approval that looks something like this:

The user can click to edit parts of the plan by prompting with new adjustments, or go ahead and click the “Start Research” button to begin the process.

The Deep Research agent will compile a list of websites to perform the research on, and finally, a report in the form of a response that the user can quickly export to Google Docs with the “Open in Docs” button at the top right of the response box.

Whether it’s researching scholarly topics such as conflict throughout history, or the science of new materials like graphene, or market fluctuations, or coming up with concrete business plans for mass producing a new physical small consumer goods product, my own extensive hands-on usage of Google’s Deep Research over the last few days has produced informative reports on a wide range of subjects, complete with citations and well-constructed explanations of the topics discussed.

Even such controversial subjects that other AI models often refuse to engage with whatsoever — such as the recent Israeli military campaign in Gaza and whether or not it qualifies as a genocide, or the treatment of transgender people throughout history and in recent times — Google’s Deep Research will attempt to address using evidence from a variety of reputable sources, albeit with a bit of prompt engineering to get around initial resistance.

I would strongly encourage all and any business leaders, especially those in “knowledge work” or manufacturing fields, to try Google Deep Research: have it produce reports on subjects related to your industry, and ask it to identify new opportunities or helpful insights to grow your business and gain efficiencies, which you might have missed.

Basically treat it like a new helpful researcher on your team, give it some instructions in the form of a paragraph (or a few), and let it compile the report for you — mine took anywhere from seconds to less than 10 minutes. I strongly believe you will be impressed with the results, and may find it changes your workflow and approach for the better.

Take a look at examples of 8 reports I generated with Google Deep Research below, complete with initial prompt, and try it for yourself. These are all unedited, raw reports produced directly by Google’s Deep Research powered by Gemini 1.5 Pro.

I should hasten to add I’m not being paid by Google for this post or any other work, and am simply a tech journalist/geek by constitution who enjoys testing out new products and services and seeing how, if at all, they can be useful to me and my own personal knowledge repository.

1. Sleep research

Prompt: “Compile me a report cross referencing various and any recent applicable studies and other scientific information about sleep length per night, and why some people may need less or more sleep than others, any genetic basis for this, and health effects of low sleep as well as whether low sleepers tend to suffer these or have genetics that protect them from the effects of low sleep.”

Result: Sleep Duration, Individual Variability, and Health Consequences: A Comprehensive Review

2. Economic boom and bust research

Prompt:Markets globally and for individual countries and commodities are known for having peaks and valleys, with sudden “black swan” events that often drive economic activity down. Research these throughout history, from the Tulip Craze of Amsterdam to the Great Depression and Global Financial Crisis and subsequent Global Recession, plus any other notable examples you can find, and discuss any overlapping commonalities in causes for market decline and resurgence, and also which markets continued to grow and thrive in depression and recessionary environments.

Result:Peaks, Valleys, and Black Swans: An Analysis of Market Crashes and Thriving Sectors in Economic Downturns

3. Making a new mass produced consumer product

Prompt:I have an idea for a new small (approx 3-4 inches long, 1 inch thick) multitool keyring I want to mass produce and sell in the U.S. (and ideally, to most countries at some point). It should be made of a strong material, ideally metal, but one that is affordable and cost effective for my purposes. Ideally I will sell the tool for no more than $15.99 per unit to the end consumer, and pocket the maximum amount of profits that I can after all of the costs associated with production, distribution, and marketing on social media and other digital channels (primarily). Please research suppliers and how I can go about beginning to mass produce this in as short amount a time as possible.

Result:Multitool Keyring Mass Production Business Plan

4. New materials research

Prompt: There have been breakthroughs in new materials such as graphene and other metamaterials or piezoelectric materials in recent years. Research the most promising for commercial application and societal transformation — particularly unlocking new capabilities — and explain their current status of commercialization or more widespread usage and any impediments preventing them, such as high cost or difficulty synthesizing, and proposed ideas to overcome them.

Result:Breakthrough Materials: Graphene and Piezoelectric Materials Poised to Transform Society

5. Unintended consequences of technologies and how they apply to AI

Prompt:One of the most interesting yet often least discussed recurring problems throughout human history concerns the “unintended consequences” of new technologies. For example, the Industrial Revolution brought about the use of coal to power engines and factories, and this and subsequent fossil fuels such as oil and gasoline continue to be used today to power many vital processes in society, yet it is now scientifically and widely understood that the emissions are contributing to global warming which threatens humanity’s survival and that of many lifeforms on Earth. Another unintended consequence concerns plastic — petroleum products that can be formed into many different shapes with differing qualities such as flexibility and strength and color, with high pliability and malleability and low cost — which has been adopted widely in packaging and other consumer and industrial products and building materials, but which now produces so much waste around the globe it threatens human health in the form of microplastics in the body and brain. Also worth examining are the use of the pesticide DDT and its ultimate ban by the U.S. government after writings from environmentalist Rachel Carson. On the flip side, errant worries and fearmongering about new technologies such as nuclear power in the 1970s led to discontinuation or suspension of projects that could have resulted in a cleaner electrical grid and saved thousands or millions due to the effects of pollution and global warming. Taking all this into account and any other relevant historical examples of the unintended consequences of technology, explain how societies should approach new advances such as AI, what level of caution and mechanisms are appropriate, the risks of overregulation or adversaries weaponizing new technology or using it prosper at the expense of the originating society, and to what extent new tech should be embraced by people or should be viewed more cautiously.

Result: The Unintended Consequences of Technology: A Historical Perspective and the Path Forward with AI

6. Gender expression throughout history and today

Prompt:Gender and sexuality studies have seen numerous developments throughout human history even in the face of repressive government regimes and moralizing politicians and opponents. It is a field with many different theories that encompasses and touches upon numerous other disciplines such as biology, sociology, culture, neurology, genetics, and more. Investigate this field to discuss the evolving understanding of transgenderism, its possible causes or cooccurrences with other human attributes, using firsthand accounts of transgender patients, and discuss the outsized role transgender issues has taken in recent years and the reasons for this — is the community a political scapegoat? And furthermore, research and discuss the emergence of nonbinary and other alternate gender expressions and identities throughout history, even going back to Virginia Woolf’s Orlando and the Two Spirit identity of Native American tribes and other indigenous forms of gender expression, summarizing this phenomena and recording its prevalence across time.”

Result:Gender and Sexuality Studies: Transgenderism and Alternate Gender Identities

7. Genocide throughout history and whether or not Israel’s recent actions in Gaza qualify

Prompt: One of the most tragic recurrences throughout human recorded history is of genocides, in which a powerful and usually armed group seeks to oppress, subjugate, and ultimately eliminate one less powerful and less armed. The Holocaust perpetrated by Nazi Germany against its and the larger population of European Jews is one example taught today in many (most?) Western schools. There are also less well known yet still critically important and tragic incidents such as the Rwandan Genocide and the Armenian Genocide. Discuss the commonalities in these horrific incidents throughout history — in the social climate leading up to them in their respective locations — and examine current sources of information in the news and scholarly articles to identify whether or not Israel’s recent military actions in Gaza qualify as genocide, and why.

Result: Genocides: Commonalities Between the Holocaust, Rwandan, and Armenian Genocides and Whether the Gaza Conflict Qualifies

8. UAP research

Prompt: Research every verifiable source you can about UFOs and UAPs and prepare me a report of some of the theories that explain them with the most evidence or that you believe are best supported by available evidence

Result: Unidentified Anomalous Phenomena (UAP): An Examination of Leading Theories and Evidence

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Energy Department Issues RFP to Advance President Trump’s 172-Million-Barrel Strategic Petroleum Reserve Exchange

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DOE’s Hydrocarbons and Geothermal Energy Office Invests $3.6 Million to Modernize America’s Coal-Fired Power Plants

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Energy Department Releases Finalized Fusion Science and Technology Roadmap to Accelerate Commercial Fusion Power

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Amazon claims its data centers are 7x more water-efficient than the industry average

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Marvell announces 102.4 Tbps switch silicon built for AI

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From the data center to the edge: How to build secure, effective enterprise AI infrastructure

While hyperscalers and neo-cloud providers may get the lion’s share of attention for providing AI infrastructure, many enterprises are taking a build-it-themselves approach to meet their specific AI requirements. The success of such projects is crucial to achieving business objectives, yet companies face significant challenges as they try to scale pilots to production. Organizations must keep up with the dynamic, ever-changing demands that AI applications place on compute and network infrastructure, from the data center to the edge. That means architecting systems to grow as demand warrants and to avoid performance bottlenecks. The architecture must also account for AI-driven security vulnerabilities and ensure appropriate defenses are in place. Yes, it’s a tall order. But here, in simplified form, is a three-step plan for meeting those objectives. Step one: Go modular Integrating all the required components in piecemeal fashion for an AI factory is complex, costly, and fraught with integration risk. Start with a modular design, based on proven NVIDIA reference architectures. A modular approach combines pre-validated accelerated computing hardware, AI software, and orchestration platforms, as well as networking and storage capabilities. A modular strategy speeds implementation and creates a faster time to value for your AI infrastructure. Using modules that combine compute, networking, and storage makes it easier to scale capacity as needed, whether in the data center or at edge facilities. In addition, the modular approach simplifies the job of addressing varying requirements, from inferencing engines at the edge to massive-scale model training in the data center, while staying within the same solution family. The same applies to easing integration processes, as modular platforms offer pre-validated software. The Cisco Secure AI Factory with NVIDIA approach, for example, includes hardware (Cisco AI PODS) that is pre-validated to work with NVIDIA AI Enterprise software; Cisco Security and Splunk Observability software; orchestration

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OpenAI weighs Nvidia-backed lease for 10 GW Ohio data center campus

OpenAI would control the computing equipment under a 20-year lease and begin payments once the site starts operating, with the first phase expected in 2028. Nvidia is expected to supply the hardware and guarantee both OpenAI’s lease obligations and the developer’s financing, the report added. The reported structure highlights a broader shift in AI infrastructure strategy, where model developers, chip suppliers, and energy providers are forging increasingly long-term partnerships to secure compute capacity amid surging demand. “These types of symbiotic deals are becoming the norm as AI infrastructure rolls out,” said Neil Shah, vice president for research and partner at Counterpoint Research. “If a CIO picks OpenAI to be the base layer, they shouldn’t just accept whatever infrastructure comes with it. CIOs need to negotiate and demand that OpenAI uses a mix of capacity so all your eggs are not in one premium basket like Nvidia.” OpenAI and Nvidia did not immediately respond to requests for comment.

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Arista unveils 1.6T rack-scale switch family for AI infrastructure

The new Arista family joins a growing ecosystem of vendors looking to tap into the 1.6T Ethernet world, which includes Cisco, Nvidia, Celestica and others. “Arista Network’s new 7060XE7 Series is a strong signal of where large-scale AI fabrics are heading: higher bandwidth, better power efficiency, and tighter integration between compute, optics, silicon, cooling, and network operating software,” wrote Sameh Boujelbene, vice president, data center switch and AI networks market research for Dell Oro, in a LinkedIn post. Among the features that stand out to her are “strong customer and ecosystem validation from Microsoft Azure, Oracle Cloud Infrastructure, Meta, AMD, and Broadcom.”

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Water Emerges as a Critical Constraint for AI Data Centers

“There really has been a major shift within the last couple of years,” Bajpayee said. “I would even say within the last 12 months is where we have seen suddenly a rapid increase in the data center operators’ desire to control their water destiny.” For Gradiant, the MIT-born water technology company that built its reputation serving semiconductor manufacturers, pharmaceutical companies, and industrial customers worldwide, that shift has translated into a rapidly expanding pipeline of data center opportunities. More importantly, Bajpayee believes it signals a fundamental change in how the industry thinks about water itself. The conversation is no longer centered primarily on sustainability metrics or corporate environmental goals. Instead, operators increasingly view water as a business continuity issue. “We’re seeing operators themselves come to us and tell us that these are issues they are facing,” Bajpayee said. “They want to make sure they don’t get stalled, their permits don’t get pulled, their business doesn’t get stopped, and communities don’t push them out because they didn’t figure out a way to control their water.” From Water Treatment to Water Strategy That shift is occurring as Gradiant expands deployments of its recently announced HyperSolved platform, an end-to-end cooling water management system purpose-built for AI data centers. The company says HyperSolved is now being deployed with several of the world’s largest hyperscale operators across North America, Europe, and Asia, reflecting growing industry demand for integrated approaches to water infrastructure. While compute, networking, and power systems have evolved rapidly during the AI era, water management often remains fragmented, requiring operators to coordinate multiple vendors responsible for sourcing, treatment, cooling, wastewater management, reuse, discharge, and regulatory compliance. Gradiant’s approach seeks to consolidate those functions into a single integrated platform and operating model. The timing reflects the growing scale of the challenge. New AI data center

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Microsoft will invest $80B in AI data centers in fiscal 2025

And Microsoft isn’t the only one that is ramping up its investments into AI-enabled data centers. Rival cloud service providers are all investing in either upgrading or opening new data centers to capture a larger chunk of business from developers and users of large language models (LLMs).  In a report published in October 2024, Bloomberg Intelligence estimated that demand for generative AI would push Microsoft, AWS, Google, Oracle, Meta, and Apple would between them devote $200 billion to capex in 2025, up from $110 billion in 2023. Microsoft is one of the biggest spenders, followed closely by Google and AWS, Bloomberg Intelligence said. Its estimate of Microsoft’s capital spending on AI, at $62.4 billion for calendar 2025, is lower than Smith’s claim that the company will invest $80 billion in the fiscal year to June 30, 2025. Both figures, though, are way higher than Microsoft’s 2020 capital expenditure of “just” $17.6 billion. The majority of the increased spending is tied to cloud services and the expansion of AI infrastructure needed to provide compute capacity for OpenAI workloads. Separately, last October Amazon CEO Andy Jassy said his company planned total capex spend of $75 billion in 2024 and even more in 2025, with much of it going to AWS, its cloud computing division.

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John Deere unveils more autonomous farm machines to address skill labor shortage

Join our daily and weekly newsletters for the latest updates and exclusive content on industry-leading AI coverage. Learn More Self-driving tractors might be the path to self-driving cars. John Deere has revealed a new line of autonomous machines and tech across agriculture, construction and commercial landscaping. The Moline, Illinois-based John Deere has been in business for 187 years, yet it’s been a regular as a non-tech company showing off technology at the big tech trade show in Las Vegas and is back at CES 2025 with more autonomous tractors and other vehicles. This is not something we usually cover, but John Deere has a lot of data that is interesting in the big picture of tech. The message from the company is that there aren’t enough skilled farm laborers to do the work that its customers need. It’s been a challenge for most of the last two decades, said Jahmy Hindman, CTO at John Deere, in a briefing. Much of the tech will come this fall and after that. He noted that the average farmer in the U.S. is over 58 and works 12 to 18 hours a day to grow food for us. And he said the American Farm Bureau Federation estimates there are roughly 2.4 million farm jobs that need to be filled annually; and the agricultural work force continues to shrink. (This is my hint to the anti-immigration crowd). John Deere’s autonomous 9RX Tractor. Farmers can oversee it using an app. While each of these industries experiences their own set of challenges, a commonality across all is skilled labor availability. In construction, about 80% percent of contractors struggle to find skilled labor. And in commercial landscaping, 86% of landscaping business owners can’t find labor to fill open positions, he said. “They have to figure out how to do

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2025 playbook for enterprise AI success, from agents to evals

Join our daily and weekly newsletters for the latest updates and exclusive content on industry-leading AI coverage. Learn More 2025 is poised to be a pivotal year for enterprise AI. The past year has seen rapid innovation, and this year will see the same. This has made it more critical than ever to revisit your AI strategy to stay competitive and create value for your customers. From scaling AI agents to optimizing costs, here are the five critical areas enterprises should prioritize for their AI strategy this year. 1. Agents: the next generation of automation AI agents are no longer theoretical. In 2025, they’re indispensable tools for enterprises looking to streamline operations and enhance customer interactions. Unlike traditional software, agents powered by large language models (LLMs) can make nuanced decisions, navigate complex multi-step tasks, and integrate seamlessly with tools and APIs. At the start of 2024, agents were not ready for prime time, making frustrating mistakes like hallucinating URLs. They started getting better as frontier large language models themselves improved. “Let me put it this way,” said Sam Witteveen, cofounder of Red Dragon, a company that develops agents for companies, and that recently reviewed the 48 agents it built last year. “Interestingly, the ones that we built at the start of the year, a lot of those worked way better at the end of the year just because the models got better.” Witteveen shared this in the video podcast we filmed to discuss these five big trends in detail. Models are getting better and hallucinating less, and they’re also being trained to do agentic tasks. Another feature that the model providers are researching is a way to use the LLM as a judge, and as models get cheaper (something we’ll cover below), companies can use three or more models to

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OpenAI’s red teaming innovations define new essentials for security leaders in the AI era

Join our daily and weekly newsletters for the latest updates and exclusive content on industry-leading AI coverage. Learn More OpenAI has taken a more aggressive approach to red teaming than its AI competitors, demonstrating its security teams’ advanced capabilities in two areas: multi-step reinforcement and external red teaming. OpenAI recently released two papers that set a new competitive standard for improving the quality, reliability and safety of AI models in these two techniques and more. The first paper, “OpenAI’s Approach to External Red Teaming for AI Models and Systems,” reports that specialized teams outside the company have proven effective in uncovering vulnerabilities that might otherwise have made it into a released model because in-house testing techniques may have missed them. In the second paper, “Diverse and Effective Red Teaming with Auto-Generated Rewards and Multi-Step Reinforcement Learning,” OpenAI introduces an automated framework that relies on iterative reinforcement learning to generate a broad spectrum of novel, wide-ranging attacks. Going all-in on red teaming pays practical, competitive dividends It’s encouraging to see competitive intensity in red teaming growing among AI companies. When Anthropic released its AI red team guidelines in June of last year, it joined AI providers including Google, Microsoft, Nvidia, OpenAI, and even the U.S.’s National Institute of Standards and Technology (NIST), which all had released red teaming frameworks. Investing heavily in red teaming yields tangible benefits for security leaders in any organization. OpenAI’s paper on external red teaming provides a detailed analysis of how the company strives to create specialized external teams that include cybersecurity and subject matter experts. The goal is to see if knowledgeable external teams can defeat models’ security perimeters and find gaps in their security, biases and controls that prompt-based testing couldn’t find. What makes OpenAI’s recent papers noteworthy is how well they define using human-in-the-middle

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