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AI Landlord Screening Tool to Stop Scoring Low-Income Tenants

Avery Winslow
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AI Landlord Screening Tool to Stop Scoring Low-Income Tenants

SafeRent, an AI screening tool used by landlords, will no longer use AI-powered “scores” to evaluate whether someone using housing vouchers would make a good tenant.

Settlement Stems from 2022 Class Action Lawsuit

The settlement stems from a 2022 class action lawsuit filed in Massachusetts. The suit alleged that SafeRent’s scoring system disproportionately harmed people using housing vouchers — specifically Black and Hispanic applicants. In addition to violating Massachusetts law, the complaint also accused SafeRent of breaking the Fair Housing Act, which prohibits housing discrimination.

The Scoring System

As outlined in the initial lawsuit, SafeRent’s scoring algorithm uses factors like credit history and non-rental-related debts to assign a SafeRent Score to potential tenants. Landlords can then use this score to determine whether to accept or deny someone’s rental application. The lawsuit claimed the process isn’t transparent, as SafeRent doesn’t tell landlords how it came up with a person’s score. And the system allegedly assigned lower scores unfairly for Black and Hispanic tenants, as well as people who use housing vouchers, leading landlords to deny their housing applications.

Settlement Terms

Under the five-year settlement, SafeRent will no longer display a tenant screening score for applicants using housing vouchers nationwide, nor can it include a score when landlords use its “affordable” SafeRent Score model. SafeRent’s service also can’t display recommendations on whether to “accept” or “deny” someone’s application if they use housing vouchers. This means landlords will now have to evaluate renters who use housing vouchers based on their entire record — rather than just using their SafeRent score.

Conclusion

The settlement is a step towards reducing housing discrimination and ensuring that all individuals, regardless of income or race, have access to affordable housing options.

FAQs

Q: What does the settlement mean for individuals using housing vouchers?

A: The settlement means that landlords will no longer use SafeRent’s scoring system to evaluate individuals using housing vouchers. Instead, landlords will have to evaluate renters based on their entire record, without relying on the scoring system.

Q: Will SafeRent continue to operate its scoring system?

A: Yes, SafeRent will continue to operate its scoring system, but it will no longer be used for individuals using housing vouchers.

Revolutionizing Education with AI-Driven Lesson Planning

Morgan Tate
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Revolutionizing Education with AI-Driven Lesson Planning

Teaching in the Age of Artificial Intelligence

Key points:

Teaching is an uphill battle that grows harder and more exacting as the years roll by. Between budgetary constraints, bandwidth crunches, and a constantly changing syllabus, teachers have their hands full when it comes to structuring and building their class’s lessons and lectures.

Lesson Planning

The efficiency and impact of any class taught over any length of time is entirely reliant upon the plan that powers it–the plan created before the first student ever steps into the classroom. For reasons detailed above, teachers might not have the resources to devote to this endeavor, which can prove to be harmful to all stakeholders. AI-powered lesson planners like Microsoft’s Shiksha Copilot and MagicSchool.ai help teachers and administrators substantially slash the time it takes to research and create a lesson plan.

Lesson Creation

Once teachers or administrators are satisfied with their handiwork, they have an array of design and multimedia tools, both AI-powered and otherwise, to extract their imagination and plop them into the digital world. Today, most teachers opt for a flavor of blended learning: using both multimedia and text to create a more engaging educational experience.

Testing and Quizzing

The natural next step in a teacher’s odyssey involves creating quizzes, mid-semester exams as well as finals, and it’s a challenging pursuit because of the sheer volume of content that teachers must assimilate and absorb to be able to come up with thought-provoking and meaningful questions that truly test a student’s understanding and learning ability.

Personalizing Feedback at Scale

In my opinion, personalization at scale is the frontrunner in the diadem of benefits that the AI revolution has inspired. The ability to create and personalize feedback at an individual level was, to put it mildly, unrealistic; one that had way too many blocking variables, not the least of which was its failure to scale.

Conclusion

We’ve just begun to scratch the surface of the applications and implications of using AI to create, tailor, and curate lectures and class materials. As AI companies like OpenAI and Anthropic race ahead in their pursuit of artificial general intelligence (AGI), products like ChatGPT and Claude will continue to get immensely better, benefiting from their chase of a "supreme" intelligence, and educators, teachers, administrators will certainly be among the ones who’d benefit from this consistent stream of innovation.

FAQs

Q: What are some AI-powered lesson planners that can help teachers and administrators create lesson plans more efficiently?
A: Microsoft’s Shiksha Copilot and MagicSchool.ai are two examples of AI-powered lesson planners that can help teachers and administrators create lesson plans more efficiently.

Q: How can teachers use AI to create engaging and topically-relevant lesson plans aligned with the latest innovations in their chosen fields?
A: Teachers can use AI-powered lesson planners like Microsoft’s Shiksha Copilot and MagicSchool.ai to create engaging and topically-relevant lesson plans aligned with the latest innovations in their chosen fields.

Q: What are some AI-powered tools that can help teachers create quizzes and exams that align with their course materials?
A: MagicSchool.ai, ChatGPT, and Chatterbot AI are three examples of AI-powered tools that can help teachers create quizzes and exams that align with their course materials.

Q: How can teachers use AI to personalize feedback at scale?
A: Teachers can use AI-powered chatbots like ChatGPT, Chatterbot AI, Claude, or any LLM-based chatbot to personalize feedback at scale.

GraphRAG Update Boosts AI Search Results

Jordan Waverly
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GraphRAG Update Boosts AI Search Results

The Update to GraphRAG: Improving AI Search Engines’ Ability to Provide Specific and Comprehensive Answers

The Difference Between RAG and GraphRAG

RAG (Retrieval Augmented Generation) combines a large language model (LLM) with a search index (or database) to generate responses to search queries. The search index grounds the language model with fresh and relevant data, reducing the possibility of AI search engines providing outdated or hallucinated answers.

GraphRAG Uses a Two-Step Process

Step 1: Indexing Engine

The indexing engine segments the search index into thematic communities formed around related topics. These communities are connected by entities (e.g., people, places, or concepts) and the relationships between them, forming a hierarchical knowledge graph. The LLM then creates a summary for each community, referred to as a Community Report.

Step 2: Query Step

In the second step, GraphRAG uses the knowledge graph it created to provide context to the LLM so that it can more accurately answer a question.

Update to GraphRAG

Microsoft explains that Retrieval Augmented Generation (RAG) struggles to retrieve information that’s based on a topic because it’s only looking at semantic relationships. GraphRAG outperforms RAG by first transforming all documents in its search index into a knowledge graph that hierarchically organizes topics and subtopics (themes) into increasingly specific layers.

Dynamic Community Selection

The original version of GraphRAG was inefficient because it processed all community reports, including irrelevant lower-level summaries, regardless of their relevance to the search query. Microsoft describes this as a “static” approach since it lacks dynamic filtering. The updated GraphRAG introduces “dynamic community selection,” which evaluates the relevance of each community report. Irrelevant reports and their sub-communities are removed, improving efficiency and precision by focusing only on relevant information.

Takeaways: Results of Updated GraphRAG

Microsoft tested the new version of GraphRAG and concluded that it resulted in a 77% reduction in computational costs, specifically the token cost when processed by the LLM. The improved GraphRAG is able to use a smaller LLM, further reducing costs without compromising the quality of the results.

Conclusion

Dynamic community selection in GraphRAG improves search results quality by generating responses that are more specific, relevant, and supported by source material. The updated GraphRAG is a significant improvement over the original, providing faster and more accurate results.

Frequently Asked Questions

Q: What is the main difference between RAG and GraphRAG?

A: RAG combines a large language model with a search index to generate responses, while GraphRAG uses a knowledge graph created from a search index to generate summaries referred to as community reports.

Q: How does GraphRAG improve search results quality?

A: GraphRAG uses dynamic community selection to evaluate the relevance of each community report and remove irrelevant reports and their sub-communities, improving efficiency and precision by focusing only on relevant information.

Q: What are the benefits of the updated GraphRAG?

A: The updated GraphRAG reduces computational costs by 77%, uses a smaller LLM, and provides faster and more accurate results by generating responses that are more specific, relevant, and supported by source material.

Q: What is the significance of dynamic community selection in GraphRAG?

A: Dynamic community selection in GraphRAG improves search results quality by generating responses that are more specific, relevant, and supported by source material.

Bayesian Optimization

Parker Lennox
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Bayesian Optimization

Bayesian Optimization: A Suite of Techniques for Hyperparameter Tuning

In this article, we will explore Bayesian Optimization, a suite of techniques used to tune hyperparameters in machine learning algorithms. We will also discuss its relation to Active Learning and other acquisition functions.

The Gold Mining Problem

Let’s consider a classic problem: gold mining. We want to find the location with the maximum gold content in an unknown land. We can’t drill at every location due to the high cost. We need to minimize the number of drillings while finding the location of maximum gold quickly.

Active Learning

Active Learning is a technique used to minimize labeling costs while maximizing modeling accuracy. In our gold mining problem, we can use Active Learning to estimate the true gold distribution. We can start with a prior assumption of a smoothly distributed gold content and update our model as we drill at different locations.

Bayesian Optimization

In Bayesian Optimization, our goal is to find the location of maximum gold content. We don’t need to estimate the true gold distribution accurately. We can use an acquisition function to determine which point to evaluate next. Acquisition functions are heuristics that balance exploration and exploitation.

Acquisition Functions

We have seen various acquisition functions, including:

  1. Expected Improvement (EI): This function balances exploration and exploitation by considering the expected improvement at each point.
  2. Probability of Improvement (PI): This function focuses on the probability of improvement at each point.
  3. Thompson Sampling: This function samples a function from the surrogate posterior and optimizes it.
  4. Upper Confidence Bound (UCB): This function combines the mean and uncertainty of the surrogate model.

Thompson Sampling

Thompson Sampling is another acquisition function that samples a function from the surrogate posterior and optimizes it. It balances exploration and exploitation by considering the uncertainty of the surrogate model.

Other Acquisition Functions

We can combine existing acquisition functions to overcome their limitations. For example, we can combine Probability of Improvement and Expected Improvement to form a linear combination.

Conclusion

In this article, we have explored Bayesian Optimization, a suite of techniques used to tune hyperparameters in machine learning algorithms. We have discussed its relation to Active Learning and other acquisition functions. We have seen various acquisition functions, including Expected Improvement, Probability of Improvement, Thompson Sampling, and Upper Confidence Bound. We have also discussed the importance of balancing exploration and exploitation in Bayesian Optimization.

References

  • Rasmussen, C. E., & Williams, C. K. I. (2004). Gaussian processes for machine learning. MIT Press.
  • scikit-learn, Gaussian Processes.
  • Javier González, Gaussian Processes.
  • Distill, Gaussian Processes.

Nvidia: Blackwell Chip Is Fine

Avery Winslow
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Nvidia: Blackwell Chip Is Fine

Nvidia’s Earnings Report: A Bellwether for the AI Industry

A large portion of Nvidia’s growth this quarter was driven by data center revenue, totaling $30.8 billion for the quarter, which was up 112 percent from last year. The company’s gross profit margin was 74.5 percent, essentially flat from a year ago. But analysts expect that Nvidia’s margins could shrink as the company shifts to producing more Blackwell chips, which cost more to make than their less advanced predecessors.

Nvidia’s Dominance in the AI Industry

Nvidia’s earnings reports are seen as an important bellwether for the AI industry. The chip architect’s advanced GPUs, which power complex neural network processing, are what made the current generative AI boom possible. As Silicon Valley giants raced to build new chatbots and image-generation tools over the past few years, Nvidia’s revenue exploded, allowing it to surpass Apple as the most valuable public company in the world. Since the launch of ChatGPT in November of 2022, Nvidia’s stock price has increased nearly tenfold.

The Importance of Nvidia’s GPUs

Almost every major tech company working on AI, even those building their own processing units, rely heavily on Nvidia GPUs to train their AI models. Meta, for example, has said that it is building its latest AI technology on a cluster of more than 100,000 Nvidia H100s. Smaller AI startups, meanwhile, have been left without enough AI compute power as Nvidia struggled to keep up with demand.

The Launch of Blackwell

Blackwell, Nvidia’s newest GPU, is made up of two pieces of silicon each equivalent to the size of its previous chip, Hopper, which are combined together into a single component. This design has resulted in a chip that’s supposedly four times faster and with more than double the number of transistors as its predecessor.

But the launch of Blackwell hasn’t been all smooth sailing. Originally slated to ship in the second quarter, the new chip hit a production snag, reportedly delaying the rollout by a few months. Huang took responsibility for the problem, calling it a “design flaw” that “caused the yield to be low.” Huang told Reuters in August that Nvidia’s longtime chipmaking partner, Taiwan Semiconductor Manufacturing Company Limited, helped Nvidia correct the issue.

Future Outlook

Moorhead told WIRED he remains bullish on Nvidia and is confident that the generative AI market will continue to grow for at least the next 12 to 18 months, despite some recent reports suggesting AI progress is starting to plateau.

“I think the only way shareholders would have a mutiny is if they were concerned about the capital expenditures or the profitability of the hyperscalers,” Moorhead said, referring to big tech companies like Amazon, Google, Microsoft, and Meta that are heavily invested in AI cloud services. “But I think they’re just going to keep buying up Nvidia until that day actually comes.” Enterprise AI is still an area of growth for Nvidia as well, he added.

Enterprise AI Adoption

On today’s earnings call, Nvidia chief financial officer Colette Kress said Nvidia’s enterprise AI tools are in “full throttle,” including an operating platform that lets other businesses build their own copilots and AI agents. Customers include Salesforce, SAP, and ServiceNow, she said.

Huang echoed the same thing later in the call: “We’re starting to see enterprise adoption of agentic AI,” he said. “It’s really the latest rage.”

Conclusion

Nvidia’s dominance in the AI industry is expected to continue, driven by its advanced GPUs and growing demand for AI compute power. The company’s shift to producing more Blackwell chips may impact its margins, but analysts remain bullish on the company’s future prospects.

FAQs

Q: What is the current state of Nvidia’s stock price?
A: Nvidia’s stock price has increased nearly tenfold since the launch of ChatGPT in November 2022.

Q: What is the significance of Nvidia’s earnings reports?
A: Nvidia’s earnings reports are seen as an important bellwether for the AI industry, as the company’s revenue and growth are closely tied to the demand for AI compute power.

Q: What is the impact of Blackwell’s launch on Nvidia’s margins?
A: Analysts expect that Nvidia’s margins could shrink as the company shifts to producing more Blackwell chips, which cost more to make than their less advanced predecessors.

Q: What is the current state of the generative AI market?
A: Despite some recent reports suggesting AI progress is starting to plateau, analysts remain bullish on the generative AI market, expecting it to continue growing for at least the next 12 to 18 months.

I’m OBSESSED with these 10 NEW LORAs

Phil
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I’m OBSESSED with these 10 NEW LORAs

The In-Context Loras: Unlocking Amazing Capabilities

Product Shots

The In-Context Loras is a powerful tool that can take your product shots to the next level. With its advanced capabilities, you can create high-quality images that showcase your products in the best possible light. Whether you’re looking to create product shots for e-commerce, marketing materials, or social media, the In-Context Loras is the perfect solution.

Logo Placement

One of the most impressive features of the In-Context Loras is its ability to accurately place logos on products. This means that you can create professional-looking images with your logo prominently displayed, without having to manually edit the image. The In-Context Loras uses advanced algorithms to ensure that the logo is perfectly aligned and proportioned, giving you a high-quality image that looks like it was created by a professional designer.

Consistent Characters

Another key feature of the In-Context Loras is its ability to create consistent characters across all of your product shots. This means that you can create a cohesive brand image by using the same characters in all of your product shots. The In-Context Loras uses advanced character recognition technology to ensure that the characters are consistent and accurate, giving you a professional-looking image that reflects your brand’s identity.

Designs on Products

The In-Context Loras is not just limited to product shots and logo placement. It can also be used to create custom designs on products. Whether you’re looking to create custom labels, packaging, or other designs, the In-Context Loras is the perfect solution. With its advanced design capabilities, you can create high-quality designs that are tailored to your specific needs.

Conclusion

The In-Context Loras is a powerful tool that can help you create high-quality product shots, accurately place logos, create consistent characters, and design custom products. With its advanced capabilities and ease of use, it’s the perfect solution for anyone looking to take their product shots to the next level.

FAQs

Q: What is the In-Context Loras?

A: The In-Context Loras is a powerful tool that can help you create high-quality product shots, accurately place logos, create consistent characters, and design custom products.

Q: What are the benefits of using the In-Context Loras?

A: The benefits of using the In-Context Loras include high-quality product shots, accurate logo placement, consistent characters, and custom design capabilities.

Q: Is the In-Context Loras easy to use?

A: Yes, the In-Context Loras is designed to be easy to use, even for those with limited design experience. With its intuitive interface and advanced capabilities, you can create high-quality images in no time.

Q: Can I use the In-Context Loras for e-commerce, marketing materials, or social media?

A: Yes, the In-Context Loras is perfect for e-commerce, marketing materials, or social media. With its advanced capabilities, you can create high-quality images that showcase your products in the best possible light.

Google Cracks Down on Forbes-Style Search Engine Manipulation

Emerson Blake
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Google Cracks Down on Forbes-Style Search Engine Manipulation

Forbes Dominates Search Terms, But at What Cost?

Under the strength of Forbes’ long-existing and well-linked site, Forbes Marketplace/Advisor has dominated the search term "best cbd gummies" for "an eternity," according to SEO analyst Lily Ray. Forbes has similarly dominated "best pet insurance," and long came up as the second result for "how to get rid of roaches," as detailed in a blog post by Lars Lofgren. If people click on this high-ranking result, and then click on a link to buy a product or request a roach removal consultation, Forbes typically gets a cut.

Forbes Marketplace’s SEO Services

Forbes Marketplace had seemingly also provided SEO-minded review services to CNN and USA Today, as detailed by Lofgren. Lofgren’s term for this business, "Parasite SEO," took hold in corners critical of the trend. Ars has contacted Forbes for comment and will update this post with response.

"The Unfair, Exploitative Nature" of "Parasite SEO"

Google writes that it had reviewed "situations where there might be varying degrees of first-party involvement" (most publishers’ review sites indicate some kind of oversight or editorial standards linked to the primary site). But however arranged, "no amount of first-party involvement alters the fundamental third-party nature of the content or the unfair, exploitative nature of attempting to take advantage of the host sites’ ranking signals."

The Consequences of "Parasite SEO"

Using third-party content in such a way as to take advantage of a high search quality ranking, outside the site’s primary focus, is considered spam. That delivers a major hit to a site’s Google ranking, and the impact is already being felt.

Google’s Response

The SEO reordering does not affect more established kinds of third-party content, like wire service reports, syndication, or well-marked sponsored content, as detailed in Google’s spam policy section about site reputation abuse. As seen on the SEO subreddit, and on social media, Google has given sites running afoul of its updated policy a "Manual Action" rather than relying only on its algorithm to catch the often opaque arrangements.

Conclusion

Forbes’ dominance of search terms has come under scrutiny, with many questioning the ethics of "Parasite SEO." While Forbes has not commented on the matter, Google has taken steps to address the issue, labeling such practices as spam and taking action against sites that engage in them.

Frequently Asked Questions

Q: What is "Parasite SEO"?
A: "Parasite SEO" refers to the practice of creating content on a third-party site, often with the intention of manipulating search engine rankings and generating revenue.

Q: Is "Parasite SEO" illegal?
A: No, "Parasite SEO" is not illegal, but it is considered a form of spam and can result in penalties from search engines like Google.

Q: Has Google taken action against sites engaging in "Parasite SEO"?
A: Yes, Google has taken action against sites that have been found to be engaging in "Parasite SEO," including giving them a "Manual Action" and demoting their search rankings.

Embedding AI Across the Enterprise

Parker Lennox
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Embedding AI Across the Enterprise

Understand Your Business Challenges

AI for the sake of AI only adds more tools to your tech stack. Before you can talk about how your organization is going to use AI, it’s critical to first understand the problems your business is facing.

Is there a bottleneck in your operations? Are you struggling to make sense of overwhelming amounts of data? Do you need more personalized customer engagement strategies? Or are there bigger questions, like how to differentiate yourself in your industry?

Understanding these challenges will help you determine where AI can have the greatest impact and ensure that its integration delivers real business value.

Study How AI can Help Solve Business Challenges

Once you’ve identified your business challenges, it’s time to think about how AI can help address them. AI can contribute to solving challenges at different stages of its adoption. To fully realize AI’s value, organizations must understand the three phases of AI adoption.

Phase 1: Operational Efficiency (AI as an Assistant)

In this initial phase, AI is used primarily to improve efficiencies by assisting employees with tasks like content creation, data analysis, and summarization, and thought partnership.

AI acts as a tireless assistant, boosting individual productivity — from marketers using ChatGPT to generate initial drafts of content to finance analysts using AI to compile reports, identify trends, and flag potential risks.

Phase 2: Workflow Automation (AI as an Optimizer)

As businesses gain more experience with AI, they move into optimizing processes. In this phase, AI is integrated into workflows to automate broader business processes, improving cross-departmental collaboration and overall efficiency.

AI now starts to impact teams, not just individuals. For example, product teams use AI to synthesize customer feedback in real-time and then use AI to convert that unstructured data into a structured product brief in a matter of minutes, not days.

Phase 3: Agentic AI (AI as a Performer)

When people talk about AI today, they talk about it through the lens of either phase one or two. But, the next phase is already here: AI operating autonomously. Examples include AI-powered customer service agents, AI-led marketing campaigns, and even AI tools that manage entire business functions. In this phase, AI takes over tasks that previously required human intervention, allowing employees to focus on more strategic initiatives.

Address Barriers to AI Adoption

As with any new technology, there will be factors that can get in the way of adoption. Consider the people, processes, and/or tool challenges that can slow innovation and growth. Whatever those problems are, they may also prevent an organization from embedding AI across the enterprise.

Some common barriers are:

  • Functional silos and fragmented processes: To break down this barrier, organizations must champion cross-departmental collaboration, standardize workflows, and create a culture of transparency. Aligning goals and using inter-connected tools enhances efficiency and ensures smoother, more integrated operations across the board.
  • Education: Microsoft found that 78% of AI users bring their own AI tools to work, but its impact is limited when these efforts are isolated among individuals and their teams. According to their survey, leaders recognize the value of AI, but "the pressure to show immediate ROI is making [them] move slowly." To embed AI across an organization, it’s crucial to provide everyone with access to AI tools and ensure that they understand when and how to use them.
  • Culture: Organizations must cultivate a culture where employees feel safe to make mistakes as they learn to use AI. And yet, Miro found that more than one in four leaders say that their organizations lack a culture of experimentation, which gets in the way of innovation. Encouraging experimentation and fostering psychological safety around AI adoption will help employees embrace the technology and push its boundaries.

Focus on Privacy and Security Concerns

Last, but certainly not least, think about the privacy and security concerns that come with AI. As organizations integrate AI, CISOs and generals counsels alike cite security as a major — perhaps, the greatest — concern when it comes to deploying this technology. They’re right. Despite all its benefits, AI does come with potential risks, including potential data manipulation, privacy breaches, and model vulnerabilities.

To mitigate these risks, organizations should develop strong AI governance policies, conduct regular audits, and stay informed about evolving threats. Transparent communication and ongoing education, combined with frequent reviews of security practices, ensures that AI can be deployed confidently while upholding the highest security and privacy standards.

Evolve Together

By following these four steps — understanding your business challenges, identifying AI solutions to those challenges, addressing the barriers to adopting AI, and mitigating privacy and security risks — organizations can move from just tinkering with AI to making it central and integral to an organization’s operations. Each step is essential to unlocking AI’s full potential and ensuring it benefits all teams.

Embedding AI throughout your organization removes constraints and inefficiencies, allowing teams to innovate quickly and freeing people to be more creative. But know that AI is not a silver bullet for all of a business’s problems. We still need human interactions to gauge and respond to the challenges organizations face. AI simply plays a key role in turning those problems into opportunities for innovation and growth.

Conclusion

In conclusion, AI is not just a technology, but a strategic tool that can help organizations solve their most pressing challenges. To fully realize AI’s value, organizations must understand their business challenges, identify AI solutions to those challenges, address the barriers to adopting AI, and mitigate privacy and security risks. By following these four steps, organizations can move from just tinkering with AI to making it central and integral to their operations.

FAQs

Q: What are the three phases of AI adoption?
A: The three phases of AI adoption are operational efficiency (AI as an assistant), workflow automation (AI as an optimizer), and agentic AI (AI as a performer).

Q: What are some common barriers to AI adoption?
A: Some common barriers to AI adoption include functional silos and fragmented processes, education, and culture.

Q: How can organizations mitigate privacy and security risks associated with AI?
A: Organizations can mitigate privacy and security risks associated with AI by developing strong AI governance policies, conducting regular audits, and staying informed about evolving threats.

Q: What is the role of AI in solving business challenges?
A: AI plays a key role in solving business challenges by providing insights, automating processes, and enhancing decision-making.

Full Steam Ahead

Avery Winslow
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Full Steam Ahead

Nvidia Becomes World’s Most Valuable Company with AI Chips

Nvidia has become the world’s most valuable company on the back of AI chips, passing Microsoft and Apple along the way, and in today’s Q3 2025 earnings, the company suggested its record AI revenue and profits are only the beginning.

Blackwell AI Servers in “Full Production”

While The Information recently reported that its new flagship Blackwell AI servers might have cooling issues, the company didn’t address that on today’s call — instead, Nvidia assured investors that Blackwell is in “full production,” is “full steam” ahead, and that the company would continue to deliver more of the chips each quarter from here on out.

Nvidia has already shipped 13,000 Blackwell samples to its customers this quarter, said CFO Colette Kress, and Nvidia CEO Jensen Huang claimed Blackwell’s success can already be measured in the billions as a result. “As you can see from all the systems being stood up, Blackwell is in great shape,” Huang told investors.

Data Center Fortunes Outstrip Gaming Business

While Nvidia has long been known as a graphics and gaming company, pioneering the GPU, its data center fortunes have now outstripped its other businesses by an order of magnitude. Gaming is now merely a $2-3 billion business each quarter for Nvidia, but its AI-infused data centers raked in $30.7 billion this past quarter, making up the vast majority of its $35 billion in quarterly earnings.

Record Profit

Much of that is pure profit for Nvidia, too: $14.8 billion in Q1, $16.6 billion in Q2, and now a profit of $19.3 billion in Q3. (Microsoft and Apple made $24.7 billion and $21.4 billion in profit, respectively, this past quarter, though.)

Overlapping Chip Demand

Practically, that means there’s substantial overlap between each new generation of chips as businesses order them and stick them into data centers. Though Blackwell is Nvidia’s latest and greatest, Nvidia says today that the H200 it announced last year is actually now the fastest-selling product it’s ever made, growing to multiple billions of dollars’ worth this past quarter.

Expectations for Future Demand

Nvidia’s H100 was its original winning AI product, though it took longer to see its current success; Huang says the company expects demand for its H-series chips to continue through most of next year.

Conclusion

Nvidia’s record-breaking AI revenue and profits are a testament to its dominance in the data center market. With its latest flagship Blackwell AI servers in “full production” and its H-series chips flying off the shelves, the company is poised to continue its success in the coming quarters.

FAQs

Q: What is the current value of Nvidia’s AI chips?
A: Nvidia’s AI chips have become the most valuable part of the company, with data center earnings making up the vast majority of its quarterly earnings.

Q: What is the current status of Blackwell AI servers?
A: Nvidia assures investors that Blackwell AI servers are in “full production” and are expected to continue delivering strong sales in the coming quarters.

Q: What is the future demand for Nvidia’s H-series chips?
A: Nvidia expects demand for its H-series chips to continue through most of next year, with the company’s H200 being the fastest-selling product it’s ever made.

Supercharged AI Development on RTX AI PCs

Emerson Blake
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Supercharged AI Development on RTX AI PCs

Generative AI-powered laptops and PCs are unlocking advancements in gaming, content creation, productivity, and development. Today, over 600 Windows apps and games are already running AI locally on more than 100 million GeForce RTX AI PCs worldwide, delivering fast, reliable, and low-latency performance.

At Microsoft Ignite, NVIDIA and Microsoft announced tools to help Windows developers quickly build and optimize AI-powered apps on RTX AI PCs, making local AI more accessible. These new tools enable application and game developers to harness powerful RTX GPUs to accelerate complex AI workflows for applications such as AI agents, app assistants, and digital humans.

### RTX AI PCs Power Digital Humans With Multimodal Small Language Models

Meet James, an interactive digital human knowledgeable about NVIDIA and its products. James uses a collection of NVIDIA NIM microservices, NVIDIA ACE, and ElevenLabs digital human technologies to provide natural and immersive responses.

NVIDIA ACE is a suite of digital human technologies that brings life to agents, assistants, and avatars. To achieve a higher level of understanding so that they can respond with greater context-awareness, digital humans must be able to visually perceive the world like humans do.

### Enhancing Digital Human Interactions

Enhancing digital human interactions with greater realism demands technology that enables perception and understanding of their surroundings with greater nuance. To achieve this, NVIDIA developed multimodal small language models that can process both text and imagery, excel in role-playing, and are optimized for rapid response times.

The NVIDIA Nemovision-4B-Instruct model, soon to be available, uses the latest NVIDIA VILA and NVIDIA NeMo framework for distilling, pruning, and quantizing to become small enough to perform on RTX GPUs with the accuracy developers need.

### Turbocharge Gen AI With NVIDIA TensorRT Model Optimizer for Windows

When bringing models to PC environments, developers face the challenge of limited memory and compute resources for running AI locally. And they want to make models available to as many people as possible, with minimal accuracy loss.

Today, NVIDIA announced updates to NVIDIA TensorRT Model Optimizer (ModelOpt) to offer Windows developers an improved way to optimize models for ONNX Runtime deployment.

### Conclusion

Generative AI-powered laptops and PCs are unlocking advancements in gaming, content creation, productivity, and development. With the introduction of new tools and technologies, developers can now harness the power of RTX AI PCs to accelerate complex AI workflows and create more realistic digital humans.

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