Meta has struck a multiyear deal to expand its data centers with millions of Nvidia's Grace and Vera CPUs and Blackwell and Rubin GPUs. While Meta has long been using Nvidia's hardware for its AI products, this deal "represents the first large-scale Nvidia Grace-only deployment," which Nvidia says will deliver "significant performance-per-watt improvements in [Meta's] data centers." The deal also includes plans to add Nvidia's next-generation Vera CPUs to Meta's data centers in 2027.

Meta is also working on its own in-house chips for running AI models, but according to the Financial Times, it has run into "technical challenges and rollout …

Read the full story at The Verge.

Microsoft announces at the India AI Impact Summit it is on pace to invest USD $50 billion by the end of the decade to help bring AI to countries across the Global South  

Artificial intelligence is diffusing at an impressive speed, but its adoption around the world remains profoundly uneven. As Microsoft’s latest AI Diffusion Report shows, AI usage in the Global North is roughly twice that of the Global South. And this divide continues to widen. This disparity impacts not only national and regional economic growth, but whether AI can deliver on its broader promise of expanding opportunity and prosperity around the world.

The India AI Impact Summit rightly has placed this challenge at the center of its agenda. For more than a century, unequal access to electricity exacerbated a growing economic gap between the Global North and South. Unless we act with urgency, a growing AI divide will perpetuate this disparity in the century ahead.

Solutions will not come easily. The needs are multifaceted, and will require substantial investments and hard work by governments, the private sector, and nonprofit organizations. But the opportunity is clear. If AI is deployed broadly and used well by a young and growing population, it offers a real prospect for catch-up economic growth for the Global South. It might even provide the biggest such opportunity of the 21^st century.

As a company, we are committed to playing an ambitious and constructive role in supporting this opportunity. This week in Delhi, we’re sharing that Microsoft is on pace to invest $50 billion by the end of the decade to help bring AI to countries across the Global South. This is based on a five-part program to drive AI impact, consisting of the following:

• Building the infrastructure needed for AI diffusion
• Empowering people through technology and skills for schools and nonprofits
• Strengthening multilingual and multicultural AI capabilities
• Enabling local AI innovations that address community needs
• Measuring AI diffusion to guide future AI policies and investments

One thing that is clear this week at the summit in India is that success will require many deep partnerships. These must span borders and bring people and organizations together across the public, private, and nonprofit sectors.

1. Building the infrastructure needed for AI diffusion

Infrastructure is a prerequisite for AI diffusion, requiring reliable electricity, connectivity, and compute capacity. To help address infrastructure gaps and support the growing needs of the Global South, Microsoft has steadily increased its investments in AI-enabling infrastructure across these regions. In our last fiscal year alone, Microsoft invested more than  $8 billion in datacenter infrastructure serving the Global South. This includes new infrastructure in India, Mexico, and countries in Africa, South America, Southeast Asia, and the Middle East.

We’re coupling our investments in datacenters with an ambitious effort to help close the Global South’s connectivity divide. We’ve been pursuing aggressively a global goal to extend internet access to 250 million people in unserved and underserved communities in the Global South, including 100 million people in Africa.

As we announced in November, we’ve already reached 117 million people across Africa through partnerships with organizations such as Cassava Technologies, Mawingu, and others that are building last‑mile networks across rural and urban communities alike. We’re closing in on our global goal of reaching 250 million people and will share an update on that progress soon.

We’re investing in AI infrastructure with sensitivity to digital sovereignty needs. We recognize that in a fragmented world, we must offer customers attractive choices for the use of our offerings. This includes sovereign controls in the public cloud, private sovereign offerings, and close collaboration with national partners.

We pursue all this with commitments to protect cybersecurity, privacy, and resilience. In the age of AI, we ensure that our customers’ AI-based innovations and intellectual property remain in their hands and under their control, rather than being transferred to AI providers.

Critically, we balance our focus on national sovereignty with our efforts to support digital trust and stability across borders. The Global South requires enormous investments to fund infrastructure for datacenters, connectivity, and electricity. It is difficult to imagine meeting all these needs without foreign direct investment, including from international technology firms.

This need is part of what informed our announcement last week at the Munich Security Conference of the new Trusted Tech Alliance. This new partnership brings together 16 leading technology companies from 11 countries and four continents. We’ve agreed together that we will adhere to five core principles designed to ensure trust in technology. Ultimately, we believe the Global South—as well as the rest of the world—needs both to protect its digital sovereignty and benefit from new investments and the best digital innovations the world has to offer.

2. Empowering people through technology and skills for schools and nonprofits

Ultimately, datacenters, connectivity, and electricity provide only part of the digital infrastructure a nation needs. History shows that the ability to provide access to technology and technology skills are equally important for economic development.

As a company, we’re focused on this in multiple ways. One critical aspect of our work is based on programs to provide cloud, AI, and other digital technologies to schools and nonprofits across the Global South. Another is our work to advance broad access to AI skills. In our last fiscal year, Microsoft invested more than $2 billion in these programs in the Global South. This includes direct financial grants, technology donations, skilling programs, and below-market product discounts.

AI skills are foundational to ensuring that AI expands opportunity and enables people to pursue more impactful real-world applications. With the launch of Microsoft Elevate in July, we committed to helping 20 million people in and beyond the Global South earn in-demand AI skilling credentials by 2028. After training 5.6 million people across India in 2025, we advanced this work by setting a goal last December to equip 20 million people in India with essential AI skills by 2030.

As part of that commitment, today we are announcing the launch of Elevate for Educators in India to strengthen the capacity of two million teachers across more than 200,000 schools, vocational institutes, and higher education settings. Our goal is to help the country’s teaching workforce lead confidently in an AI‑driven future. The program will be delivered in partnership with India’s national education and workforce training authorities, expanding equitable AI opportunities for eight million students.

Through Microsoft Elevate, we’re also working to introduce new educator credentials and a global professional learning community that enables teachers to share best practices with peers worldwide. This effort will involve large-scale capacity building initiatives, including AI Ambassadors, Educator Academies, AI Productivity Labs, and Centers of Excellence. It will equip 25,000 institutions with inclusive AI infrastructure while integrating AI learning pathways into major government platforms.

3. Strengthening multilingual and multicultural AI capabilities

Language is another major barrier to AI diffusion across the Global South, particularly in regions where digitally underrepresented languages prevail and access to essential services depends on local-language communication. For billions of people worldwide, AI systems perform less consistently in the languages they rely on most than in English.

That’s why we’re announcing this week new steps to increase our investments across the AI lifecycle, from data and models to evaluation and deployment, to strengthen multilingual and multicultural capabilities and support more inclusive AI systems that will better serve the Global South.

First, we’re investing upstream in language data and model capability. This includes support for LINGUA Africa, which builds on what we learned through LINGUA Europe: that investing in language data and model capability in partnership with local communities can materially improve AI performance for underrepresented languages.

Through LINGUA Africa—a $5.5 million open call led by the Masakhane African Languages Hub, Microsoft’s AI for Good Lab, and the Gates Foundation, with additional support from the UK government—we are prioritizing open, responsibly sourced data across text, speech, and vision as well as use-case-driven AI model development. By enabling African languages in high-impact sectors like education, food security, health, and government services, LINGUA Africa aims to ensure AI advances translate into tangible improvements in people’s daily lives.

Second, we’re advancing multilingual and multicultural evaluation tools. We’re helping expand the MLCommons AILuminate benchmark to include major Indic and Asian languages, enabling more reliable measurement of AI safety and security beyond English.

Today, even when automated evaluation tools expand language coverage, they too often rely on machine translation or English-first model behavior, with predictable failures when local expressions shift meaning. Partnering with academic and government institutions in India, Japan, Korea, and Singapore, and with industry, Microsoft is co-leading AILuminate’s multilingual, multicultural, and multimodal expansion that builds from the ground up. With a pilot dataset of 7,000 high-quality text-and-image prompts for Hindi, Tamil, Malay, Japanese, and Korean, we’re developing tools that reflect how risks manifest in local linguistic and cultural contexts, not just how they appear after translation.

Microsoft Research is also advancing Samiksha, a community-centered method for evaluating AI behavior in real-world contexts, in collaboration with Karya and The Collective Intelligence Project in India. Samiksha encodes local language use, culturally specific communication norms, and locally relevant use cases directly into core testing artifacts by surfacing failure modes that English-first evaluations routinely miss.

Finally, we’re working to scale content provenance for linguistic diversity. For trusted AI deployment, the ecosystem benefits from tools to identify the provenance of digital content like images, audio, or video, distinguishing whether it’s AI-generated. With partners in the Coalition for Content Provenance and Authenticity (C2PA), Microsoft is helping extend content provenance standards beyond an English-ready baseline. This includes forthcoming support for multiple Indic languages across metadata, specifications, and UX guidance, alongside efforts to support mobile-first deployment. With these investments, hundreds of millions more people in India will be better equipped to identify synthetic media in their primary language.

4. Enabling local AI innovations that address community needs

As India’s guiding sutras for the AI Impact Summit recognize, AI must be applied to address pressing challenges in collaboration with people and organizations in the Global South. Microsoft’s increasing investments prioritize locally defined problems, locally grounded expertise, and real-world impact. Our goal is straightforward: to ensure that AI solutions are not only technically sound, but socially relevant and sustainable.

Today, Microsoft is announcing a new AI initiative to strengthen food security across Sub-Saharan Africa, starting in Kenya and designed to scale across the region. Across Global South communities, food security and sustainable agriculture are critical to resilience and progress. In collaboration with NASA Harvest, the government of Kenya, and the East Africa Grain Council, our AI for Good Lab will use AI on top of satellite data to provide critical, timely food security insights. This builds on what we’ve learned in helping to address rice farming challenges in India, where severe groundwater depletion prompted 150,000 farmers in Punjab to adopt water-saving methods. In collaboration with The Nature Conservancy, Microsoft’s AI for Good Lab developed a classification system with satellite imagery to empower policymakers to track adoption of sustainable rice farming practices, target interventions, and measure water management impacts at scale.

Through Project Gecko, Microsoft Research is also co-designing AI technologies with local communities in East Africa and South Asia to support agriculture. This work includes the Paza family of automatic speech recognition models that can operate on mobile devices across six Kenyan languages, multilingual Copilots, and a Multimodal Critical Thinking (MMCT) Agent that can reason over community-generated video, voice, and text. Microsoft also launched PazaBench—the first automatic speech recognition leaderboard, with initial coverage of 39 African languages—and developed two playbooks for multilingual and multicultural capabilities, Paza and Vibhasha. Likewise, our AI for Good Lab developed a reproducible pipeline for adapting open-weight large language models to low-resource languages, demonstrating measurable gains for languages such as Chichewa, Inuktitut, and Māori.

5. Measuring AI diffusion to guide future AI policies and investments

Finally, accelerating diffusion requires a firm understanding of where AI is being used, how it is being adopted, and where gaps persist. Building on our AI Diffusion Reports and Microsoft GitHub’s long track record of contributing to the OECD AI Policy Observatory, the WIPO Global Innovation Index, and other cross‑country analyses, we’re increasing our investments in research and data sharing to track AI diffusion.

We’re advancing new methods for sharing AI adoption metrics. For example, based on models used in public code repositories hosted on Microsoft GitHub and privacy-preserving aggregated usage signals from Azure Foundry, we’re scaling this work through contributions to the forthcoming Global AI Adoption Index developed by the World Bank.

Signals from the global developer community that builds, adapts, and deploys AI-enabled software round out adoption research. At 24 million, the Indian developer community is the second largest national community on GitHub, where developers learn about and collaborate with the world on AI. The Indian community is also the fastest growing among the top 30 largest economies, with growth at more than 26 percent each year since 2020 and a recent surge of over 36 percent in annual growth as of Q4 2025. Indian developers rank second globally in open-source contributions, second in GitHub Education users, and second in contributions to public generative AI projects, with readiness to use tools like GitHub Copilot across academic, enterprise, and public interest settings enabling AI diffusion.

Insights from this evidence base help inform investments in infrastructure, language capabilities, skilling, or beyond, supporting more targeted and effective interventions to expand AI’s benefits. They also create a common empirical baseline to track progress over time—so AI diffusion becomes something we can measure and shape, not just observe.

Sustaining impact at scale through coordinated global action

For AI to diffuse broadly and deliver meaningful impact across regions, several conditions matter. As a company, we are focused on the need for accessible AI infrastructure, systems that work reliably in real-world contexts, and technologies that can be applied toward local challenges and opportunities. Microsoft is committed to working with partners to advance this work, including sharing data to track progress.

The post Acting urgently to close the growing AI divide appeared first on Microsoft On the Issues.

Sign up below to receive the full video - Content Strategy for Startups.

Here’s a scenario you’ll recognize. Your founder writes an incredible LinkedIn post, part origin story, part manifesto, and it lands. Thousands of views. Genuine engagement. People reaching out. Then… nothing. Weeks pass. The company goes quiet. And all that momentum evaporates.

This isn’t bad luck. It’s a structural problem. And it’s the most common content mistake startups make.

Most early-stage companies don’t have a content strategy; they have content moments. And there’s a big difference between the two.

What a content strategy actually is (and isn’t)

Let’s start with what it isn’t, because this is where most teams go wrong. A content strategy is not the brilliant founder post. It’s not a viral LinkedIn update, a polished website, an editorial calendar, or an AI-generated article. These are tactics. Useful tactics, sure,  but tactics in search of a strategy.

A real content strategy is a clear, agreed-upon, written-down product narrative. It defines who your product serves, what problem it solves, why the approach is distinct, and how you disseminate that story consistently across channels over time,  in a way that actually drives pipeline. The goal isn’t one amazing post. It’s knowing how to repeat that output with energy, at the right cadence, to the right person, at the right moment in their journey.

Don’t fall into these startup content traps

A few patterns come up again and again that stall content efforts before they gain traction:

• Relying entirely on the founder’s personal brand rather than building content around the company

• Sporadic “flash in the pan” content that doesn’t compound over time

• Assuming AI tools will do the strategic heavy lifting (they won’t)

• Outdated content and dead links that erode trust faster than no content at all

None of these are fatal. But they all share a root cause: the absence of a clear system.

The three questions you need to answer first

Before you write a single word of content, you need honest answers to three questions. Who, specifically, is your product for? Not the general audience of say “cloud architects”  but cloud architects who are mid-migration, frustrated with X, and actively looking for Y right now. What problem are you solving, and can you describe it in a way that makes your ideal customer feel seen? And what is the defining narrative, the one thread that ties everything you publish together?

Someone reading your content needs to feel that you understand their exact situation before they’ll give you their email address, let alone book a demo. Broad content gets polite nods, maybe a return to your website. Specific content gets immediate action.

AI is a collaborator, not a strategist

There’s a version of AI-enabled content that works beautifully: repurposing a strong anchor piece into a LinkedIn summary, a pull quote, a short video script. Derivative content, with a human-in-the-loop reviewing every output, can multiply your reach without increasing your workload.

There’s also a version that quietly kills your credibility: pointing an AI at your homepage and publishing whatever comes back. The output will be the lowest common denominator, technically accurate, hopefully, but totally personality-free, and invisible to the people you’re trying to reach.

The rule is simple. AI can help you scale content. It cannot create the strategy behind it.

Don’t overthink, start small

You don’t need a six-person content team or a 12-month editorial calendar to do this right. You need one good anchor piece per month,  a substantive blog post or a video and then a system in place for derivative content across channels. One post can become a LinkedIn summary, three pull quotes, a 30-second video script, and a GitHub discussion prompt. That’s a month of presence from a few focused hours of work.

The keyword is ongoing. Content that compounds is content that continues.

One last thing

Content strategy isn’t about doing more. It’s about doing the right things, in the right order, with a system that keeps you moving forward. Whether you build this internally or bring in outside help, the investment is worth it because the alternative is great work that nobody sees.

Content Strategy for Startups

It details:

• Why content strategy gets deprioritized (and why that’s expensive)

• What it is — and what it 

• The content traps that stall momentum

• Defining your audience with surgical precision

• The “peanut butter & jelly” framework for product-led content

• Your best content already exists

• Cadence, channels, ownership: the execution system

• Smart AI vs. AI slop — real examples

• The minimum viable content strategy for an early-stage team

How have large language models impacted hacking? Richard talks to Erica Burgess about her experiences using LLMs for red team hacking, collecting bug bounties, and identifying vulnerabilities in systems. Erica discusses the power of LLMs to generate a variety of viewpoints on a potential exploit and help the hacker think "out of the box." Coordinating multiple agents to attempt a variety of exploits, retrieve information, and otherwise deal with the drudgery parts of hacking means a skilled operator can move faster - what once would be days of work can be minutes. Where does AI in hacking go? Lots of scary places - but also pointing the way to new ways to protect systems!

Links

• Burninator Sec

Recorded January 24, 2026