Google has expanded the What’s happening feature within Google Business Profiles to restaurants and bars in the United Kingdom, Canada, Australia, and New Zealand. It is now available for multi-location restaurants, not just single-location restaurants.

The What’s happening feature launched back in May as a way for some businesses to highlight events, deals, and specials prominently at the top of your Google Business Profile. Now, Google is bringing it to more countries.

What Google said. Google’s Lisa Landsman wrote on LinkedIn:

How do you promote your “Taco Tuesday” in Toledo and your “Happy Hour” in Houston… right when locals are searching for a place to go?

I’m excited to share that the Google Business Profile feature highlighting what’s happening at your business, such as timely events, specials and deals, has now rolled out for multi-location restaurants & bars across the US, UK, CA, AU & NZ! (It was previously only available for single-location restaurants)

This is a great option for driving real-time foot traffic. It automatically surfaces the unique specials, live music, or events you’re already promoting at a specific location, catching customers at the exact moment they’re deciding where to eat or grab a cocktail.

What it looks like. Here is a screenshot of this feature:

More details. Google’s Lisa Landsman added, “We’ve already seen excellent results from testing and look forward to hearing how this works for you!”

Availability. This feature is only available for restaurants & bars. Google said it hopes to expand to more categories soon. It is also only available in the United States, United Kingdom, Canada, Australia, and New Zealand.

The initial launch was for single-location Food and Drink businesses in the U.S., UK, Australia, Canada, and New Zealand. It is now available for multi-location restaurants, not just single-location restaurants.

Why we care. If you manage restaurants and/or bars, this may be a new way to get more attention and visitors to your business from Google Search. Now, if you manage multi-location restaurants or bars, you can leverage this feature.

Marketing, technology, and business leaders today are asking an important question: how do you optimize for large language models (LLMs) like ChatGPT, Gemini, and Claude? 

LLM optimization is taking shape as a new discipline focused on how brands surface in AI-generated results and what can be measured today. 

For decision makers, the challenge is separating signal from noise – identifying the technologies worth tracking and the efforts that lead to tangible outcomes.

The discussion comes down to two core areas – and the timeline and work required to act on them:

• Tracking and monitoring your brand’s presence in LLMs.
• Improving visibility and performance within them.

Tracking: The foundation of LLM optimization

Just as SEO evolved through better tracking and measurement, LLM optimization will only mature once visibility becomes measurable. 

We’re still in a pre-Semrush/Moz/Ahrefs era for LLMs. 

Tracking is the foundation of identifying what truly works and building strategies that drive brand growth. 

Without it, everyone is shooting in the dark, hoping great content alone will deliver results.

The core challenges are threefold:

• LLMs don’t publish query frequency or “search volume” equivalents.
• Their responses vary subtly (or not so subtly) even for identical queries, due to probabilistic decoding and prompt context.
• They depend on hidden contextual features (user history, session state, embeddings) that are opaque to external observers.

Why LLM queries are different

Traditional search behavior is repetitive – millions of identical phrases drive stable volume metrics. LLM interactions are conversational and variable. 

People rephrase questions in different ways, often within a single session. That makes pattern recognition harder with small datasets but feasible at scale. 

These structural differences explain why LLM visibility demands a different measurement model.

This variability requires a different tracking approach than traditional SEO or marketing analytics.

The leading method uses a polling-based model inspired by election forecasting.

The polling-based model for measuring visibility

A representative sample of 250–500 high-intent queries is defined for your brand or category, functioning as your population proxy. 

These queries are run daily or weekly to capture repeated samples from the underlying distribution of LLM responses.

Tracking tools record when your brand and competitors appear as citations (linked sources) or mentions (text references), enabling share of voice calculations across all competitors. 

Over time, aggregate sampling produces statistically stable estimates of your brand visibility within LLM-generated content.

Early tools providing this capability include:

• Profound.
• Conductor.
• OpenForge.

Consistent sampling at scale transforms apparent randomness into interpretable signals. 

Over time, aggregate sampling provides a stable estimate of your brand’s visibility in LLM-generated responses – much like how political polls deliver reliable forecasts despite individual variations.

Building a multi-faceted tracking framework

While share of voice paints a picture of your presence in the LLM landscape, it doesn’t tell the complete story. 

Just as keyword rankings show visibility but not clicks, LLM presence doesn’t automatically translate to user engagement. 

Brands need to understand how people interact with their content to build a compelling business case.

Because no single tool captures the entire picture, the best current approach layers multiple tracking signals:

• Share of voice (SOV) tracking: Measure how often your brand appears as mentions and citations across a consistent set of high-value queries. This provides a benchmark to track over time and compare against competitors.
• Referral tracking in GA4: Set up custom dimensions to identify traffic originating from LLMs. While attribution remains limited today, this data helps detect when direct referrals are increasing and signals growing LLM influence.
• Branded homepage traffic in Google Search Console: Many users discover brands through LLM responses, then search directly in Google to validate or learn more. This two-step discovery pattern is critical to monitor. When branded homepage traffic increases alongside rising LLM presence, it signals a strong causal connection between LLM visibility and user behavior. This metric captures the downstream impact of your LLM optimization efforts.

Nobody has complete visibility into LLM impact on their business today, but these methods cover all the bases you can currently measure.

Be wary of any vendor or consultant promising complete visibility. That simply isn’t possible yet.

Understanding these limitations is just as important as implementing the tracking itself.

Because no perfect models exist yet, treat current tracking data as directional – useful for decisions, but not definitive.

Dig deeper: In GEO, brand mentions do what links alone can’t

Estimating LLM ‘search volume’

Measuring LLM impact is one thing. Identifying which queries and topics matter most is another.

Compared to SEO or PPC, marketers have far less visibility. While no direct search volume exists, new tools and methods are beginning to close the gap.

The key shift is moving from tracking individual queries – which vary widely – to analyzing broader themes and topics. 

The real question becomes: which areas is your site missing, and where should your content strategy focus?

To approximate relative volume, consider three approaches:

Correlate with SEO search volume

Start with your top-performing SEO keywords. 

If a keyword drives organic traffic and has commercial intent, similar questions are likely being asked within LLMs. Use this as your baseline.

Layer in industry adoption of AI

Estimate what percentage of your target audience uses LLMs for research or purchasing decisions:

• High AI-adoption industries: Assume 20-25% of users leverage LLMs for decision-making.
• Slower-moving industries: Start with 5-10%.

Apply these percentages to your existing SEO keyword volume. For example, a keyword with 25,000 monthly searches could translate to 1,250-6,250 LLM-based queries in your category.

Using emerging inferential tools

New platforms are beginning to track query data through API-level monitoring and machine learning models. 

Accuracy isn’t perfect yet, but these tools are improving quickly. Expect major advancements in inferential LLM query modeling within the next year or two.

Optimizing for LLM visibility

The technologies that help companies identify what to improve are evolving quickly. 

While still imperfect, they’re beginning to form a framework that parallels early SEO development, where better tracking and data gradually turned intuition into science.

Optimization breaks down into two main questions:

• What content should you create or update, and should you focus on quality content, entities, schema, FAQs, or something else?
• How should you align these insights with broader brand and SEO strategies?

Identify what content to create or update

One of the most effective ways to assess your current position is to take a representative sample of high-intent queries that people might ask an LLM and see how your brand shows up relative to competitors. This is where the Share of Voice tracking tools we discussed earlier become invaluable.

These same tools can help answer your optimization questions:

• Track who is being cited or mentioned for each query, revealing competitive positioning.
• Identify which queries your competitors appear for that you don’t, highlighting content gaps.
• Show which of your own queries you appear for and which specific assets are being cited, pinpointing what’s working.

From this data, several key insights emerge:

• Thematic visibility gaps: By analyzing trends across many queries, you can identify where your brand underperforms in LLM responses. This paints a clear picture of areas needing attention. For example, you’re strong in SEO but not in PPC content. 
• Third-party resource mapping: These tools also reveal which external resources LLMs reference most frequently. This helps you build a list of high-value third-party sites that contribute to visibility, guiding outreach or brand mention strategies. 
• Blind spot identification: When cross-referenced with SEO performance, these insights highlight blind spots; topics or sources where your brand’s credibility and representation could improve.

Understand the overlap between SEO and LLM optimization

LLMs may be reshaping discovery, but SEO remains the foundation of digital visibility.

Across five competitive categories, brands ranking on Google’s first page appeared in ChatGPT answers 62% of the time – a clear but incomplete overlap between search and AI results.

That correlation isn’t accidental. 

Many retrieval-augmented generation (RAG) systems pull data from search results and expand it with additional context. 

The more often your content appears in those results, the more likely it is to be cited by LLMs.

Brands with the strongest share of voice in LLM responses are typically those that invested in SEO first. 

Strong technical health, structured data, and authority signals remain the bedrock for AI visibility.

What this means for marketers:

• Don’t over-focus on LLMs at the expense of SEO. AI systems still rely on clean, crawlable content and strong E-E-A-T signals.
• Keep growing organic visibility through high-authority backlinks and consistent, high-quality content.
• Use LLM tracking as a complementary lens to understand new research behaviors, not a replacement for SEO fundamentals.

Redefine on-page and off-page strategies for LLMs

Just as SEO has both on-page and off-page elements, LLM optimization follows the same logic – but with different tactics and priorities.

Off-page: The new link building

Most industries show a consistent pattern in the types of resources LLMs cite:

• Wikipedia is a frequent reference point, making a verified presence there valuable.
• Reddit often appears as a trusted source of user discussion.
• Review websites and “best-of” guides are commonly used to inform LLM outputs.

Citation patterns across ChatGPT, Gemini, Perplexity, and Google’s AI Overviews show consistent trends, though each engine favors different sources.

This means that traditional link acquisition strategies, guest posts, PR placements, or brand mentions in review content will likely evolve. 

Instead of chasing links anywhere, brands should increasingly target:

• Pages already being cited by LLMs in their category.
• Reviews or guides that evaluate their product category.
• Articles where branded mentions reinforce entity associations.

The core principle holds: brands gain the most visibility by appearing in sources LLMs already trust – and identifying those sources requires consistent tracking.

On-page: What your own content reveals

The same technologies that analyze third-party mentions can also reveal which first-party assets, content on your own website, are being cited by LLMs. 

This provides valuable insight into what type of content performs well in your space.

For example, these tools can identify:

• What types of competitor content are being cited (case studies, FAQs, research articles, etc.).
• Where your competitors show up but you don’t.
• Which of your own pages exist but are not being cited.

From there, three key opportunities emerge:

• Missing content: Competitors are cited because they cover topics you haven’t addressed. This represents a content gap to fill.
• Underperforming content: You have relevant content, but it isn’t being referenced. Optimization – improving structure, clarity, or authority – may be needed.
• Content enhancement opportunities: Some pages only require inserting specific Q&A sections or adding better-formatted information rather than full rewrites.

Leverage emerging technologies to turn insights into action

The next major evolution in LLM optimization will likely come from tools that connect insight to action.

Early solutions already use vector embeddings of your website content to compare it against LLM queries and responses. This allows you to:

• Detect where your coverage is weak.
• See how well your content semantically aligns with real LLM answers.
• Identify where small adjustments could yield large visibility gains.

Current tools mostly generate outlines or recommendations.

The next frontier is automation – systems that turn data into actionable content aligned with business goals.

Timeline and expected results

While comprehensive LLM visibility typically builds over 6-12 months, early results can emerge faster than traditional SEO. 

The advantage: LLMs can incorporate new content within days rather than waiting months for Google’s crawl and ranking cycles. 

However, the fundamentals remain unchanged.

Quality content creation, securing third-party mentions, and building authority still require sustained effort and resources. 

Think of LLM optimization as having a faster feedback loop than SEO, but requiring the same strategic commitment to content excellence and relationship building that has always driven digital visibility.

From SEO foundations to LLM visibility

LLM traffic remains small compared to traditional search, but it’s growing fast.

A major shift in resources would be premature, but ignoring LLMs would be shortsighted. 

The smartest path is balance: maintain focus on SEO while layering in LLM strategies that address new ranking mechanisms.

Like early SEO, LLM optimization is still imperfect and experimental – but full of opportunity. 

Brands that begin tracking citations, analyzing third-party mentions, and aligning SEO with LLM visibility now will gain a measurable advantage as these systems mature.

In short:

• Identify the third-party sources most often cited in your niche and analyze patterns across AI engines.
• Map competitor visibility for key LLM queries using tracking tools.
• Audit which of your own pages are cited (or not) – high Google rankings don’t guarantee LLM inclusion.
• Continue strong SEO practices while expanding into LLM tracking – the two work best as complementary layers.

Approach LLM optimization as both research and brand-building.

Don’t abandon proven SEO fundamentals. Rather, extend them to how AI systems discover, interpret, and cite information.

Seattle is looking to celebrate and accelerate its leadership in artificial intelligence at the very moment the first wave of the AI economy is crashing down on the region’s tech workforce.

That contrast was hard to miss Monday evening at the opening reception for Seattle AI Week 2025 at Pier 70. On stage, panels offered a healthy dose of optimism about building the AI future. In the crowd, buzz about Amazon’s impending layoffs brought the reality of the moment back to earth.

A region that rose with Microsoft and then Amazon is now dealing with the consequences of Big Tech’s AI-era restructuring. Companies that hired by the thousands are now thinning their ranks in the name of efficiency and focus — a dose of corporate realism for the local tech economy.

The double-edged nature of this shift is not lost on Washington Gov. Bob Ferguson.

“AI, and the future of AI, and what that means for our state and the world — each day I do this job, the more that moves up in my mind in terms of the challenges and the opportunities we have,” Ferguson told the AI Week crowd. He touted Washington’s concentration of AI jobs, saying his goal is to maximize the benefits of AI while minimizing its downsides.

Seattle AI Week, led by the Washington Technology Industry Association, was started last year after a Forbes list of the nation’s top 50 AI startups included none from Seattle, said the WTIA’s Nick Ellingson, opening this year’s event. That didn’t seem right. Was it a messaging problem?

“A bunch of us got together and said, let’s talk about all the cool things happening around AI in Seattle, and let’s expand the tent beyond just tech things that are happening,” Ellingson explained.

So maybe that’s the best measuring stick: how many startups will this latest shakeout spark, and how can the Seattle region’s startup and tech leaders make it happen? Can the region become less dependent on the whims of the Microsoft and Amazon C-suites in the process? 

“Washington has so much opportunity. It’s one of the few capitals of AI in the world,” said WTIA’s Arry Yu in her opening remarks. “People talk about China, people talk about Silicon Valley — there are a few contenders, but really, it’s here in Seattle. … The future is built on data, on powerful technology, but also on community. That’s what makes this place different.”

And yet, “AI is a sleepy scene in Seattle, where people work at their companies, but there’s very little activity and cross-pollinating outside of this,” said Nathan Lambert, senior research scientist with the Allen Institute for AI, during the opening panel discussion.

No, we don’t want to become San Francisco or Silicon Valley, Lambert added. But that doesn’t mean the region can’t cherry-pick some of the ingredients that put Bay Area tech on top.

Whether laid-off tech workers will start their own companies is a common question after layoffs like this. In the Seattle region at least, that outcome has been more fantasy than reality. 

This is where AI could change things, if not with the fabled one-person unicorn then with a bigger wave of new companies born of this employment downturn. Who knows, maybe one will even land on that elusive Forbes AI 50 list. (Hey, a region can dream!)

But as the new AI reality unfolds in the regional workforce, maybe the best question to ask is whether Seattle’s next big thing can come from its own backyard again.

Microsoft and OpenAI announced the long-awaited details of their new partnership agreement Tuesday morning — with concessions on both sides that keep the companies aligned but not in lockstep as they move into their next phases of AI development.

Under the arrangement, Microsoft gets a 27% equity stake in OpenAI’s new for-profit entity, the OpenAI Group PBC (Public Benefit Corporation), a stake valued at approximately $135 billion. That’s a decrease from 32.5% equity but not a bad return on an investment of $13.8 billion.

At the same time, OpenAI has contracted to purchase an incremental $250 billion in Microsoft Azure cloud services. However, in a significant concession in return for that certainty, Microsoft will no longer have a “right of first refusal” on new OpenAI cloud workloads.

Microsoft, meanwhile, will retain its intellectual property rights to OpenAI models and products through 2032, an extension of the timeframe that existed previously. 

A key provision of the new agreement centers on Artificial General Intelligence (AGI), with any declaration of AGI by OpenAI now subject to verification by an independent expert panel. This was a sticking point in the earlier partnership agreement, with an ambiguous definition of AI potentially triggering new provisions of the prior arrangement. 

Microsoft and OpenAI had previously announced a tentative agreement without providing details. More aspects of the deal are disclosed in a joint blog post from the companies.

Shares of Microsoft are up 2% in early trading after the announcement. The company reports earnings Wednesday afternoon, and some analysts have said the uncertainty over the OpenAI arrangement has been impacting Microsoft’s stock. 

The post AMD CEO on new $1 billion AI supercomputer partnership with the Department of Energy appeared first on StartupHub.ai.

“We are super excited to announce a new partnership with the Department of Energy,” stated Lisa Su, Chair and CEO of AMD, during a CNBC interview. This monumental $1 billion collaboration will usher in the development of two advanced supercomputers, designed to tackle some of the most complex scientific challenges facing humanity. The partnership signifies […]

The post AMD CEO on new $1 billion AI supercomputer partnership with the Department of Energy appeared first on StartupHub.ai.

The post Bitcoin Miners’ AI Pivot: A Strategic Masterclass in Energy and Compute appeared first on StartupHub.ai.

The burgeoning demand for artificial intelligence, a computational arms race among hyperscalers, has illuminated a critical bottleneck: access to reliable, scalable power. This very challenge, as discussed by CleanSpark CEO Matthew Schultz with CNBC’s Jordan Smith, is precisely where Bitcoin miners like CleanSpark find their strategic advantage. Their conversation unveils a nuanced pivot, not merely […]

The post Bitcoin Miners’ AI Pivot: A Strategic Masterclass in Energy and Compute appeared first on StartupHub.ai.

The post OpenAI Recapitalization Reshapes AI Landscape with Microsoft at the Helm appeared first on StartupHub.ai.

The recent finalization of OpenAI’s recapitalization plan marks a pivotal moment in the trajectory of artificial intelligence, not just for the involved parties but for the entire tech ecosystem. On CNBC, David Faber broke down the intricate details of this agreement, joined by Jim Cramer, who offered his characteristic sharp market commentary. Their discussion illuminated […]

The post OpenAI Recapitalization Reshapes AI Landscape with Microsoft at the Helm appeared first on StartupHub.ai.

The post Wild Moose Emerges from Stealth with $7 Million Seed Round to Redefine Site Reliability Engineering with AI appeared first on StartupHub.ai.

Wild Moose, the AI-powered Site Reliability Engineering (SRE) platform acting as a first responder for production incidents, today announced its emergence from stealth with $7 million in seed funding. The round was led by iAngels, with participation from Y Combinator, F2 Venture Capital, Maverick Ventures, and others. The company is also backed by a distinguished […]

The post Wild Moose Emerges from Stealth with $7 Million Seed Round to Redefine Site Reliability Engineering with AI appeared first on StartupHub.ai.

The post Gemini for Education: Google’s AI Dominates Higher Ed appeared first on StartupHub.ai.

Google's Gemini for Education is rapidly integrating into higher education, offering no-cost AI tools to over 1000 institutions and 10 million students.

The post Gemini for Education: Google’s AI Dominates Higher Ed appeared first on StartupHub.ai.

The post Securitize IPO to bring tokenization to Nasdaq at $1.25B appeared first on StartupHub.ai.

The Securitize IPO is a bellwether moment, creating the first publicly-traded company focused purely on the infrastructure for tokenizing real-world assets.

The post Securitize IPO to bring tokenization to Nasdaq at $1.25B appeared first on StartupHub.ai.

The post FriendliAI Expands Ultra-Fast AI Inference Platform with Nebius AI Cloud Integration appeared first on StartupHub.ai.

Enterprises can now deploy large-scale AI inference with FriendliAI’s optimized stack on Nebius AI infrastructure, combining top performance with cost efficiency.

The post FriendliAI Expands Ultra-Fast AI Inference Platform with Nebius AI Cloud Integration appeared first on StartupHub.ai.

AMD will power two new AI supercomputers in the US, using MI355X and MI430X accelerators The US Department of Energy has announced a $1 billion deal under which AMD will deliver two next-generation supercomputers to the Oak Ridge National Laboratory (ORNL). These systems are designed to expand the US’s leadership in artificial intelligence (AI) and […]

The post US cuts a $1 billion deal with AMD to build two new AI Supercomputers appeared first on OC3D.

By Jeff Seibert

I’ve been building products and companies my entire career — Increo, Box, Crashlytics, Twitter and now, Digits — and I’ve had the privilege of speaking with some of the sharpest minds in venture and entrepreneurship along the way.

One recent conversation with a legendary investor really crystallized for me a set of truths about startups: what success really is, why some founders thrive while others burn out, and how to navigate the inevitable chaos of building something from nothing.

Here are some of the lessons I’ve internalized from years of building, observing and learning.

Success has no finish line

In the startup world, we talk a lot about IPOs, acquisitions and valuations. But those are milestones, not destinations.

The companies that endure don’t “win” and stop — they keep creating, adapting and pushing forward. They’re playing an infinite game, where the only goal is to remain in the game.

When you’re building something truly generative — driven by a purpose greater than yourself — there’s no point at which you can say “done.” If your company has a natural stopping point, you may be building the wrong thing.

You don’t choose the work — the work chooses you

The best founders I’ve met — and the best moments I’ve had as a founder — come from an almost irrational pull toward solving a specific problem I myself experienced.

You may want to start a company, but if you have to talk yourself into your idea, it probably won’t survive contact with reality. The founders who succeed are often the ones who can’t not work on their thing.

Starting a company shouldn’t be a career move — it should be the last possible option after every other path fails to scratch the itch.

The real killer: founder fatigue

Most companies don’t die because of one bad decision or one tough competitor. They die because the founders run out of energy.

Fatigue erodes vision, motivation and creativity. Protecting your own drive — keeping it clean and focused — may be the single most important survival skill you have.

That means staying close to the product, protecting time for customer work, and avoiding the slow drift into managing around problems instead of solving them.

Customer > competitor

It’s easy to get caught up in competitor moves, investor chatter or market gossip. But the most important question is always: Are we delivering joy to the customer?

If you’re losing focus, sign up for your own product as a brand-new user. Feel the friction. Fix it. Repeat.

At Digits, we run our own signup and core flows every week. It’s uncomfortable — it surfaces flaws we’d rather not see — but it keeps us anchored to the only metric that matters: customer delight.

Boards should ask questions, not give answers

Over the years, I’ve learned the most effective boards aren’t presentation theaters — they’re discussion rooms.

The best structure I’ve seen:

• No slides;
• A narrative pre-read sent in advance; and
• A deep dive into one essential question.

Good directors help you widen your perspective. They don’t hand you a to-do list. Rather, they help you see the problem in a way that makes the answer obvious.

Twitter: lessons from a phenomenon

When I think back to my time at Twitter, the most enduring lesson is that not all companies are built top-down. Some — like Twitter — are shaped more by their users than their executives.

Features like @mentions, hashtags and retweets didn’t come from a product roadmap — they came from the community.

That’s messy, but it’s also powerful. Sometimes your job isn’t to control the phenomenon, rather it’s to keep it healthy without smothering what made it magical in the first place.

Why now is a great time to start

If you’re building today, you have an advantage over the so-called “unicorn zombies” that raised massive rounds pre-AI and are now locked into defending old business models.

Fresh founders can design from scratch for the new reality; there’s no legacy to protect, no sacred cows to defend.

The macro environment? Irrelevant. The only timing that matters is when the problem calls you so strongly that not working on it feels impossible.

If there’s one takeaway from all of this, it’s that success is continuing. The real prize is the ability to keep playing, keep serving and keep creating.

If you’re standing at the edge, wondering if you should start — start. Take one step. See if it grows. And if it does, welcome to the infinite game.

 Jeff Seibert is the founder and CEO of Digits, the world’s first AI-native accounting platform. He previously served as Twitter‘s head of consumer product and starred in the Emmy Award-winning Netflix documentary “The Social Dilemma.”

Illustration: Dom Guzman

While startup investment has been climbing lately, not all industries are partaking in the gains.

Cleantech is one of the spaces that’s been mostly left out. Overall funding to the space is down this year, despite some pockets of bullishness in areas like fusion and battery recycling.

The broad trend: Cleantech- and sustainability-related startup investment has been on a downward trajectory for several years now. And so far, 2025 is on track to be another down year.

On the bright side, however, there’s been some pickup in recent months, boosted by big rounds for companies in energy storage, fusion and other cleantech subsectors.

The numbers: Investors put an estimated $20 billion into seed- through growth-stage funding to companies in cleantech, EV and sustainability-related categories so far this year.

That puts 2025 funding on track to come in well below last year’s levels, which were already at a multiyear low.

Still, quarter by quarter, the pattern looks more encouraging. Investment hit a low point in Q1 of this year and recovered some in the subsequent two quarters. The current quarter is also off to a strong start.

Noteworthy recent rounds

The largest cleantech-related round of the year closed this month. Base Power, a provider of residential battery backup systems and electricity plans, raised $1 billion in Series C funding. The Austin, Texas-based company says its systems allow energy providers to more efficiently harness renewable power.

The second-largest round was Commonwealth Fusion Systems’ $863 million Series B2 financing. The Devens, Massachusetts-based company says it is moving closer to being the first in the world to commercialize fusion power.

For a bigger-picture view, below we put together a list of 10 of the year’s largest cleantech- and sustainability-related financings.

The broad takeaway: Startups innovating for an era of rising power consumption

Not to over-generalize, but if there was one big takeaway from recent cleantech and sustainability startup funding, it would be that founders and investors recognize that these are times of ever-escalating energy demand. They’re planning accordingly, looking to tap new sources of power, fusion in particular, as well as better utilize and scale existing clean energy sources.

Related Crunchbase query and list:

• Cleantech, EV & Sustainability-Related Funding, 2025 YTD
• Large 2025 Financings For Cleantech, EV And Sustainability Startups

Illustration: Dom Guzman

A new report exploring the potential for the Pacific Northwest to stake its claim as the global leader in responsible AI offers a paradoxical view. The Cascadia region, which includes Seattle, Portland and Vancouver, B.C., is described as a proven, promising player in the sphere — but with significant risks that threaten its success.

“We created companies that transformed global commerce,” writes former Gov. Chris Gregoire in a forward to the document. “Now we have the chance to add another chapter — one where Cascadia becomes the world’s standard-bearer for innovation that uplifts both people and planet.”

The Cascadia Innovation Corridor, which Gregoire chairs, released the report this morning as it kicks off its two-day conference. The economic advocacy group’s eighth annual event is being held in Seattle.

The study is built on an analysis by the Boston Consulting Group that ranks Cascadia’s three metro areas against 15 comparable regions in the U.S. and Canada for their economic competitiveness, including livability, workforce, and business and innovation climate. Seattle came in fourth behind Boston, Austin and Raleigh, while Portland ranked 13th and Vancouver 14th.

Over the past decade, the region’s gross domestic product and populations have both grown significantly, and when combined, their economies approach the 18th largest in the world.

Cascadia’s strengths, the report explains, include tech engines such as cloud giants Microsoft and Amazon in Washington, silicon chip manufacturing in Oregon, and quantum innovation in Vancouver, as well as academic excellence from the University of Washington, University of British Columbia and Oregon State University.

But as time goes on and as business and civic leaders aim for the prize of AI dominance, cracks in the system are increasingly troubling.

• Business costs are rising and there are mounting regulatory concerns — but it’s a tricky picture. Seattle, for example, often turns to B&O and headcount taxes to cover costs, while the state struggles to balance budgets in the absence of an income tax.
• Housing affordability is continuing to decline for many residents in these metro areas.
• Skilled tech workers are leaving Portland, in particular, and Seattle relies heavily on foreign workers receiving H1-B visas, which are less certain under the Trump administration.
• The clean, affordable energy that was once abundant in the Pacific Northwest is decreasingly available as droughts reduce river flows that drive hydropower dams and electricity demand increases with rapid data center growth.

The report notes that multiple regions around the U.S. and Canada have created AI-focused hubs with hundreds of millions of dollars in public and private funding to bolster their hold on the sector.

New Jersey has a half-billion dollar “AI Moonshot” program including tax incentives and public-worker AI training programs; New York’s “Empire AI Consortium” has an AI computing training center at the University of Buffalo and startup supports; and California has a public-private task force to increase AI adoption within government services and connecting tech leaders with state agencies.

For its part, Seattle Mayor Bruce Harrell announced a “responsible AI plan” this fall that provides guidelines for the municipality’s use of artificial intelligence and its support of the AI tech sector as an economic driver, which includes the earlier launches of the startup-focused AI House and Foundations.

But what the region really needs to succeed is a collaborative effort tapping all of the metro areas’ assets.

“For Cascadia, the lesson is clear: without a coordinated strategy that links our strengths in cloud computing, semiconductors, and research, we risk falling behind,” states the Cascadia Innovation Corridor report. “Acting together, we can position Cascadia not just to keep pace, but to lead.”

With the iPhone 17 lineup now in the hands of consumers, the legendary rumor mill that typically gyrates towards Apple's new products is naturally pivoting towards the next year's iPhone 18 lineup as well as the much-anticipated iPhone 20, which is due in 2027 and would commemorate 20 years since the first iPhone launched all the way back in 2007. Now, a new rumor suggests that Apple is transitioning towards simplified buttons in stages, with the iPhone 18 lineup transitioning towards a less complicated mechanical button for camera control, which will be replaced entirely by solid-state buttons in the iPhone […]

Read full article at https://wccftech.com/apple-iphone-18-to-use-a-simplified-camera-control-button-iphone-20-to-feature-haptics-instead-of-mechanical-buttons/

Death Stranding 2: On the Beach now supports the PlayStation 5's power saver mode, and its implementation is among the most interesting to date, according to a new technical analysis. In the latest episode of their weekly podcast, the tech experts at Digital Foundry examined how the two entries in the Kojima Productions series support Power Saver Mode, a newly introduced operating mode for the PlayStation 5 console that cuts CPU resources in half, halves the memory bandwidth, and reduces CPU and GPU clocks to reduce the system's power consumption. While the implementation in Death Stranding: Director's Cut was not […]

Read full article at https://wccftech.com/death-stranding-2-on-the-beach-most-interesting-power-saver-mode/

The INSPIRE series RTX 5050 is probably the smallest RTX 5050 editions, which offer a single fan design and weigh just 551 grams. MSI Launches Small Form-Factor RTX 5050 INSPIRE ITX and OC GPUs, Boasting Dual-Slot Thickness MSI has officially launched two new GeForce RTX 5050 cards in the INSPIRE series. These are probably the smallest RTX 5050 cards on the market, boasting a dual-slot design and a single-fan cooler to ensure compatibility with very small ITX cases. Apart from MSI, PNY also has a similarly compact GeForce RTX 5050, which measures just 147mm. The INSPIRE ITX RTX 5050 cards […]

Read full article at https://wccftech.com/msi-intros-geforce-rtx-5050-inspire-itx-and-oc-cards-measuring-just-147mm/

EA is pushing its employees to use AI for basically every task, but the results can be flawed, resulting in more work for developers. Business Insider recently talked with current EA staff, who confirmed that the company's leadership has spent the past year or so pushing its 15,000 employees to use AI for virtually every task, from producing code and concept art for games to advising managers how to speak to staff about a certain number of topics, including pay or promotions. The AI tools used to produce code are among those creating the most issues for developers. It is […]

Read full article at https://wccftech.com/ea-is-pushing-employees-to-use-ai-for-everything-including-producing-code-requiring-manual-fixing/

Intel's CEO, Lip-Bu Tan, has discussed the stake taken by the US government in the company, claiming that it was a necessary step to ensure that the American chipmaker could compete with Taiwan's TSMC. Intel's CEO Also Tells Specifics About His Meeting With President Trump, Calling It a Massive Success Well, the interest from the Trump administration in Intel was indeed a surprise for many of us, but for CEO Lip-Bu Tan, this initiative was "good to have", as he claims that it is similar to how Taiwan supports TSMC or South Korea backs the likes of Samsung Foundry. In […]

Read full article at https://wccftech.com/intel-ceo-says-us-government-stake-was-a-deliberate-move/

OnexPlayer has officially launched its flagship handheld, the OneXfly Apex, with a liquid-cooled AMD Ryzen AI MAX+ 395 SoC. AMD Ryzen AI MAX+ 395 Gets Liquid-Cooled Inside A Handheld With OneXPlayer's OneXfly Apex The OneXfly Apex handheld was teased last month and is positioned to be a flagship device featuring the AMD Ryzen AI MAX+ 395 SoC. This SoC has already been featured in other handhelds such as GPD Win 5 and Ayaneo Next 2. Now, OneXPlayer is rolling out its own high-end handheld, offering a nice upgrade vs the Ryzen AI 300 "Strix Point" stack. Just to recap the […]

Read full article at https://wccftech.com/onexfly-apex-handheld-launch-amd-ryzen-ai-max-395-liquid-cooling-128-gb-85wh-battery/

The popular PS3 emulator has updated its latest GPU recommendation list to AMD's RDNA and NVIDIA's Turing series. RPCS3 Announces Updated GPU Requirements for the Emulator; Recommends At Least AMD RX 5000 or NVIDIA RTX 2000 Series RPCS3 has just announced the new recommended GPU requirements for its popular PS3 emulator, which comes as a result of major GPU manufacturers ending support for some of its older generation GPU series. RPCS3 announced on X that it no longer "recommends" the AMD RX 400 and NVIDIA GTX 900 series GPUs as the recommended GPUs. The newer GPU recommendations now start with […]

Read full article at https://wccftech.com/rpcs3-removes-amd-rx-400-500-and-nvidia-gtx-900-1000-series-from-recommended-gpu-requirements/

A vapor chamber will make a significant difference to the overall temperatures of the M6 iPad Pro, with Apple previously reported to bring this cooling upgrade to its flagship tablet lineup. The California-based giant is often known to commence product development several months in advance, and according to the latest report, Apple is already in talks with two suppliers that could manufacture this crucial component. The M6 iPad Pro’s vapor chamber is reported to be provided by a Chinese and Taiwanese manufacturer Considering that the M6 iPad Pro launch will materialize approximately 18 months after the M5 iPad Pro’s inception, […]

Read full article at https://wccftech.com/apple-shortlisting-m6-ipad-pro-vapor-chamber-suppliers/

Mockin is a tool for simulating professional real-time AI interviews with personalized feedback and resume matching.

We’ve gathered 200+ real interview questions to practice, get personalized feedback, and master the STAR method to ace your next interview! Built for designers by designers.

View startup

While enterprises looking to sell goods and services online wait for the backbone of agentic commerce to be hashed out, PayPal is hoping its new features will bridge the gap.

The payments company is launching a discoverability solution that allows enterprises to make its product available on any chat platform, regardless of the model or agent payment protocol. 

PayPal, which is one of the participants for Google’s Agent Payments Protocol (AP2), found that it can leverage its relationship with merchants and enterprises to help pave the way for an easier transition into agentic commerce and offer the kind of flexibility they learned will benefit the ecosystem. 

Michelle Gill, PayPal general manager for small business and financial services, told VentureBeat that AI-powered shopping will continue to grow, so enterprises and brands need to start laying the groundwork early. 

“We think that merchants who've historically sold through web stores, particularly in the e-commerce space, are really going to need a way to get active on all of these large language models,” Gill said. “The challenge is that no one really knows how fast all of this is going to move. The issue that we’re trying to help merchants think through is how to do all of this as low-touch as possible while using the infrastructure you already have without doing a bazillion integrations.”

She added AI shopping would also bring about “a resurgence from consumers trying to ensure their investment is protected.”

PayPal partnered with website builder Wix, Cymbio, Commerce and Shopware to bring products to chat platforms like Perplexity. 

Agent-powered shopping 

PayPal’s Agentic Commerce Services include two features. The first is Agent Ready, which would allow existing PayPal merchants to accept payments on AI platforms. The second is called Shop Sync, which will enable companies’ product data to be discoverable through different AI chat interfaces. It takes a company’s catalog information and plug its inventory and fulfillment data to chat platforms. 

Gill said the data goes into a central repository where AI models can ingest the information. 

Right now, companies can access shop sync with Agent Ready coming in 2026. 

Gill said Agentic Commerce Services is a one-to-many solution, that would be helpful right now, as different LLMs scrape different data sources to surface information. 

Other benefits include:

• Fast integration with current and future partners

• More product discovery over the traditional search, browse and cart experiences

• Preserved customer insights and relationships where the brand continues to have control over their records and communications with customers. 

Right now, the service is only available through Perplexity, but Gill said more platforms will be added soon. 

Fragmented AI platforms 

Agentic commerce is still very much in the early stages. AI agents are just beginning to get better at reading a browser. while platforms like ChatGPT, Gemini and Perplexity can now surface products and services based on user queries, people cannot technically buy things from chat yet.

There’s a race right now to create a standard to enable agents to transact on behalf of users and pay for items. Other than Google’s AP2, OpenAI and Stripe have the Agentic Commerce Protocol (ACP) and Visa launched its Trusted Agent Protocol. 

Other than enabling a trust layer for agents to transact, another issue enterprises face with agentic commerce is fragmentation. Different chat platforms use different models which also interpret information in slightly different ways. Gill said PayPal learned that when it comes to working with merchants, flexibility is important. 

“How do you decide if you're going to spend your time integrating with Google, Microsoft, ChatGPT or Perplexity? And each one of them right now has a different protocol, a different catalog, config, a different everything. That is a lot of time to make a bet as to like where you should spend your time,” Gill said. 

AI tools can help teams move faster than ever – but speed alone isn’t a strategy.

As more marketers rely on LLMs to help create and optimize content, credibility becomes the true differentiator. 

And as AI systems decide which information to trust, quality signals like accuracy, expertise, and authority matter more than ever.

It’s not just what you write but how you structure it. AI-driven search rewards clear answers, strong organization, and content it can easily interpret.

This article highlights key strategies for smarter AI workflows – from governance and training to editorial oversight – so your content remains accurate, authoritative, and unmistakably human.

Create an AI usage policy

More than half of marketers are using AI for creative endeavors like content creation, IAB reports.

Still, AI policies are not always the norm. 

Your organization will benefit from clear boundaries and expectations. Creating policies for AI use ensures consistency and accountability.

Only 7% of companies using genAI in marketing have a full-blown governance framework, according to SAS.

However, 63% invest in creating policies that govern how generative AI is used across the organization. 

Even a simple, one-page policy can prevent major mistakes and unify efforts across teams that may be doing things differently.

As Cathy McPhillips, chief growth officer at the Marketing Artificial Intelligence Institute, puts it: 

• “If one team uses ChatGPT while others work with Jasper or Writer, for instance, governance decisions can become very fragmented and challenging to manage. You’d need to keep track of who’s using which tools, what data they’re inputting, and what guidance they’ll need to follow to protect your brand’s intellectual property.” 

So drafting an internal policy sets expectations for AI use in the organization (or at least the creative teams).

When creating a policy, consider the following guidelines: 

• What the review process for AI-created content looks like. 
• When and how to disclose AI involvement in content creation. 
• How to protect proprietary information (not uploading confidential or client information into AI tools).
• Which AI tools are approved for use, and how to request access to new ones.
• How to log or report problems.

Logically, the policy will evolve as the technology and regulations change. 

Keep content anchored in people-first principles

It can be easy to fall into the trap of believing AI-generated content is good because it reads well. 

LLMs are great at predicting the next best sentence and making it sound convincing. 

But reviewing each sentence, paragraph, and the overall structure with a critical eye is absolutely necessary.

Think: Would an expert say it like that? Would you normally write like that? Does it offer the depth of human experience that it should?

“People-first content,” as Google puts it, is really just thinking about the end user and whether what you are putting into the world is adding value. 

Any LLM can create mediocre content, and any marketer can publish it. And that’s the problem. 

People-first content aligns with Google’s E-E-A-T framework, which outlines the characteristics of high-quality, trustworthy content.

E-E-A-T isn’t a novel idea, but it’s increasingly relevant in a world where AI systems need to determine if your content is good enough to be included in search.

According to evidence in U.S. v. Google LLC, we see quality remains central to ranking:

• “RankEmbed and its later iteration RankEmbedBERT are ranking models that rely on two main sources of data: [redacted]% of 70 days of search logs plus scores generated by human raters and used by Google to measure the quality of organic search results.” 

It suggests that the same quality factors reflected in E-E-A-T likely influence how AI systems assess which pages are trustworthy enough to ground their answers.

So what does E-E-A-T look like practically when working with AI content? You can:

• Review Google’s list of questions related to quality content: Keep these in mind before and after content creation.
• Demonstrate firsthand experience through personal insights, examples, and practical guidance: Weave these insights into AI output to add a human touch.
• Use reliable sources and data to substantiate claims: If you’re using LLMs for research, fact-check in real time to ensure the best sources. 
• Insert authoritative quotes either from internal stakeholders or external subject matter experts: Quoting internal folks builds brand credibility while external sources lend authority to the piece.
• Create detailed author bios: Include:
  • Relevant qualifications, certifications, awards, and experience.
  • Links to social media, academic papers (if relevant), or other authoritative works.
• Add schema markup to articles to clarify the content further: Schema can clarify content in a way that AI-powered search can better understand.
• Become the go-to resource on the topic: Create a depth and breadth of material on the website that’s organized in a search-friendly, user-friendly manner. You can learn more in my article on organizing content for AI search.

Dig deeper: Writing people-first content: A process and template

Train the LLM 

LLMs are trained on vast amounts of data – but they’re not trained on your data. 

Put in the work to train the LLM, and you can get better results and more efficient workflows. 

Here are some ideas.

Maintain a living style guide

If you already have a corporate style guide, great – you can use that to train the model. If not, create a simple one-pager that covers things like:

• Audience personas.
• Voice traits that matter.
• Reading level, if applicable.
• The do’s and don’ts of phrases and language to use. 
• Formatting rules such as SEO-friendly headers, sentence length, paragraph length, bulleted list guidelines, etc. 

You can refresh this as needed and use it to further train the model over time. 

Build a prompt kit  

Put together a packet of instructions that prompts the LLM. Here are some ideas to start with: 

• The style guide
  • This covers everything from the audience personas to the voice style and formatting.
  • If you’re training a custom GPT, you don’t need to do this every time, but it may need tweaking over time. 
• A content brief template
  • This can be an editable document that’s filled in for each content project and includes things like:
    • The goal of the content.
    • The specific audience.
    • The style of the content (news, listicle, feature article, how-to).
    • The role (who the LLM is writing as).
    • The desired action or outcome.
• Content examples
  • Upload a handful of the best content examples you have to train the LLM. This can be past articles, marketing materials, transcripts from videos, and more. 
  • If you create a custom GPT, you’ll do this at the outset, but additional examples of content may be uploaded, depending on the topic. 
• Sources
  • Train the model on the preferred third-party sources of information you want it to pull from, in addition to its own research. 
  • For example, if you want it to source certain publications in your industry, compile a list and upload it to the prompt.  
  • As an additional layer, prompt the model to automatically include any third-party sources after every paragraph to make fact-checking easier on the fly.
• SEO prompts
  • Consider building SEO into the structure of the content from the outset.  
  • Early observations of Google’s AI Mode suggest that clearly structured, well-sourced content is more likely to be referenced in AI-generated results.

With that in mind, you can put together a prompt checklist that includes:

• Crafting a direct answer in the first one to two sentences, then expanding with context.
• Covering the main question, but also potential subquestions (“fan-out” queries) that the system may generate (for example, questions related to comparisons, pros/cons, alternatives, etc.).
• Chunking content into many subsections, with each subsection answering a potential fan-out query to completion.
• Being an expert source of information in each individual section of the page, meaning it’s a passage that can stand on its own.
• Provide clear citations and semantic richness (synonyms, related entities) throughout. 

Dig deeper: Advanced AI prompt engineering strategies for SEO

Create custom GPTs or explore RAG 

A custom GPT is a personalized version of ChatGPT that’s trained on your materials so it can better create in your brand voice and follow brand rules. 

It mostly remembers tone and format, but that doesn’t guarantee the accuracy of output beyond what’s uploaded.

Some companies are exploring RAG (retrieval-augmented generation) to further train LLMs on the company’s own knowledge base. 

RAG connects an LLM to a private knowledge base, retrieving relevant documents at query time so the model can ground its responses in approved information.

While custom GPTs are easy, no-code setups, RAG implementation is more technical – but there are companies/technologies out there that can make it easier to implement. 

That’s why GPTs tend to work best for small or medium-scale projects or for non-technical teams focused on maintaining brand consistency.

RAG, on the other hand, is an option for enterprise-level content generation in industries where accuracy is critical and information changes frequently.

Run an automated self-review

Create parameters so the model can self-assess the content before further editorial review. You can create a checklist of things to prompt it.

For example:

• “Is the advice helpful, original, people-first?” (Perhaps using Google’s list of questions from its helpful content guidance.) 
• “Is the tone and voice completely aligned with the style guide?” 

Have an established editing process 

Even the best AI workflow still depends on trained editors and fact-checkers. This human layer of quality assurance protects accuracy, tone, and credibility.

Editorial training

About 33% of content writers and 24% of marketing managers added AI skills to their LinkedIn profiles in 2024.

Writers and editors need to continue to upskill in the coming year, and, according to the Microsoft 2025 annual Work Trend Index, AI skilling is the top priority.  

Professional training creates baseline knowledge so your team gets up to speed faster and can confidently handle outputs consistently.

This includes training on how to effectively use LLMs and how to best create and edit AI content.

In addition, training content teams on SEO helps them build best practices into prompts and drafts.

Editorial procedures

Ground your AI-assisted content creation in editorial best practices to ensure the highest quality. 

This might include:

• Identifying the parts of the content creation workflow that are best suited for LLM assistance.
• Conducting an editorial meeting to sign off on topics and outlines. 
• Drafting the content.
• Performing the structural edit for clarity and flow, then copyediting for grammar and punctuation.
• Getting sign-off from stakeholders.  

The AI editing checklist

Build a checklist to use during the review process for quality assurance. Here are some ideas to get you started:

• Every claim, statistic, quote, or date is accompanied by a citation for fact-checking accuracy.
• All facts are traceable to credible, approved sources.
• Outdated statistics (more than two years) are replaced with fresh insights. 
• Draft meets the style guide’s voice guidelines and tone definitions. 
• Content adds valuable, expert insights rather than being vague or generic.
• For thought leadership, ensure the author’s perspective is woven throughout.
• Draft is run through the AI detector, aiming for a conservative percentage of 5% or less AI. 
• Draft aligns with brand values and meets internal publication standards.
• Final draft includes explicit disclosure of AI involvement when required (client-facing/regulatory).

Grounding AI content in trust and intent

AI is transforming how we create, but it doesn’t change why we create.

Every policy, workflow, and prompt should ultimately support one mission: to deliver accurate, helpful, and human-centered content that strengthens your brand’s authority and improves your visibility in search. 

Dig deeper: An AI-assisted content process that outperforms human-only copy

Google's John Mueller said case sensitivity matters and SEOs shouldn't just hope it works.

The post Google’s Advice On Canonicals: They’re Case Sensitive appeared first on Search Engine Journal.

The post Beyond LLMs: Crafting Robust AI with Multi-Method Agentic Architectures appeared first on StartupHub.ai.

“Large language models have well-known issues and constraints. And so if you want to solve complex problems, you’re going to want to adopt what’s called multi-method agentic AI, which combines large language models with other kinds of proven automation technologies so that you can build more adaptable, more transparent systems that are much more likely […]

The post Beyond LLMs: Crafting Robust AI with Multi-Method Agentic Architectures appeared first on StartupHub.ai.

The post Google Arts & Culture Elevates Virtual Travel with AI Tours appeared first on StartupHub.ai.

Google Arts & Culture is redefining virtual exploration with new AI tours for North Gyeongsang, South Korea, featuring interactive, Gemini-powered commentary.

The post Google Arts & Culture Elevates Virtual Travel with AI Tours appeared first on StartupHub.ai.

The post Nvidia’s AI Imperative: Beyond Moore’s Law, Network is the New Compute appeared first on StartupHub.ai.

Michael Kagan, CTO of Nvidia and co-founder of Mellanox, recently engaged in a candid discussion with Sonya Huang and Pat Grady at Sequoia’s Europe100 event, offering profound insights into Nvidia’s meteoric rise as the architect of AI infrastructure. His commentary illuminated the pivotal role of the Mellanox acquisition in transforming Nvidia from a mere chip […]

The post Nvidia’s AI Imperative: Beyond Moore’s Law, Network is the New Compute appeared first on StartupHub.ai.

Amazon confirmed Tuesday that it is cutting about 14,000 corporate jobs, citing a need to reduce bureaucracy and become more efficient in the new era of artificial intelligence.

In a message to employees, posted on the company’s website, Amazon human resources chief Beth Galetti signaled that the cutbacks are expected to continue into 2026, while indicating that the company will also continue to hire in key strategic areas.

Reuters reported Monday that the number of layoffs could ultimately total as many as 30,000 people, which is still a possibility as the cutbacks continue into next year. At that scale, the overall number of job cuts could eventually be the largest in Amazon’s history, exceeding the 27,000 positions that the company eliminated in 2023 across multiple rounds of layoffs.

“This generation of AI is the most transformative technology we’ve seen since the Internet, and it’s enabling companies to innovate much faster than ever before,” wrote Galetti, senior vice president of People Experience and Technology. Amazon needs “to be organized more leanly, with fewer layers and more ownership, to move as quickly as possible for our customers and business,” she explained.

Amazon’s corporate workforce numbered around 350,000 people in early 2023, the last time the company provided a public number. At that scale, the initial reduction of 14,000 represents about 4% of Amazon’s corporate workforce. However, the number is a much smaller fraction of its overall workforce of 1.55 million people, which includes workers in its warehouses.

Although the cuts are expected to be global, they are likely to hit especially hard in the Seattle region, home to the company’s first headquarters and its largest corporate workforce. The tech hub has already felt the impact of major layoffs by Microsoft and many other companies in recent months.

The cuts come two days before Amazon’s third quarter earnings report. Amazon and other cloud giants have been pouring billions into capital expenses to boost AI capacity. Cutting jobs is one way of showing operating-expense discipline to Wall Street.

In a memo to employees in June, Amazon CEO Andy Jassy wrote that he expected Amazon’s total corporate workforce to get smaller over time as a result of efficiency gains from AI. 

Jassy took over as Amazon CEO from founder Jeff Bezos in mid-2021. In recent years he has been pushing to reduce management layers and eliminate bureaucracy inside the company, saying he wants Amazon to operate like the “world’s largest startup.” 

Bloomberg News reported this week that Jassy has told colleagues that parts of the company remain “unwieldy” despite the 2023 layoffs and other efforts to streamline operations. 

Reuters cited sources saying the magnitude of the cuts is also a result of Amazon’s strict return-to-office policy failing to cause enough employees to quit voluntarily. Amazon brought workers back five days a week earlier this year.

Impacted teams and people will be notified of the layoffs today, Galetti wrote.

Amazon is offering most impacted employees 90 days to find a new role internally, though the timing may vary based on local laws, according to the message. Those who do not find a new position at Amazon or choose to leave will be offered severance pay, outplacement services, health insurance benefits, and other forms of support.

John Carmack reports performance issues with Nvidia’s DGX Spark AI system John Carmack, ID Software founder and former CTO of Oculus VR, has been testing an Nvidia DGX Spark AI system. So far, he is not impressed by the performance the system has delivered. His system appears to be maxing out at 100 watts, which […]

The post Nvidia DGX Spark delivers half quoted performance for John Carmack appeared first on OC3D.

Battlefield is getting a free-to-play Battle Royale mode EA has confirmed that Battlefield REDSEC will launch on October 28th at 3 PM GMT, a free-to-play Battle Royale game that debuts alongside Battlefield 6’s Season 1 content. Battlefield REDSEC acts as EA’s counter to Call of Duty: Warzone. Currently, exact details for the new game are […]

The post Battlefield REDSEC is launching today – Here’s what you need to know appeared first on OC3D.

There's little doubt that The Matrix franchise is criminally underserved when it comes to videogame adaptations, despite being theoretically a perfect fit for the medium. In the 26 years since the original movie's theatrical debut, we only got two decent games: 2003's single player action/adventure game Enter the Matrix and 2005's MMORPG The Matrix Online. More recently, the interactive experience The Matrix Awakens was released in late 2021, but it was really just a tech demo for Unreal Engine 5 and a tease at the level of quality that gaming fans of the IP never really got to fully experience. […]

Read full article at https://wccftech.com/the-matrix-creators-wanted-kojima-make-a-game-on-the-ip-konami-refused/

Today at GTC 2025, NVIDIA's CEO, Jensen Huang, will deliver the opening keynote live from Washington, US, for the first time. NVIDIA GTC Comes To Washington, D.C, US: CEO Jensen Huang To Talk About Next Chapter of AI, Watch It Live Here NVIDIA's GTC 2025 is just a few hours away, and while you might be wondering, didn't GTC already happen a few months back? Well, it should be mentioned that while GTC used to be a one-time per annum affair in the past, the recent growth and success have turned NVIDIA's GTC into more of a quarterly event. As […]

Read full article at https://wccftech.com/watch-nvidia-gtc-2025-ceo-jensen-huang-keynote-live-washington-us/

The iPhone 17 lineup is expected to be Apple’s last to ship with Qualcomm’s 5G modems as the company prepares its transition to ship all of its iPhone 18 models with the C2 baseband chip. This in-house solution was said to be in development shortly after the iPhone 16e was announced, and while we will witness its materialization in 2026, a new report states that, unlike other Apple chipsets like the A20 and A20 Pro, it will not leverage TSMC’s newest 2nm process, but a lithography that is a couple of generations old. The C2 5G modem will reportedly be mass […]

Read full article at https://wccftech.com/apple-c2-to-be-mass-produced-on-older-tsmc-process-says-report/

A team of modders is working on Bully Online, a modification for the PC version of Bully: Scholarship Edition that promises to allow players to roam the grounds of Bullworth Academy and the nearby town with their friends. The Wii and Xbox 360 versions of Scholarship Edition did have a multiplayer mode, but it was limited to two players and only allowed them to face off in the class minigames. According to community creator SWEGTA, Bully Online promises much more, including free roam support, solo and group minigames, and even a role-playing system. They were able to add a 'fully […]

Read full article at https://wccftech.com/bully-online-mod-promises-let-you-roam-rockstars-classic-with-friends/

This morning, indie Chinese developer ChillyRoom unveiled Loulan: The Cursed Sand, one of the games funded through the PlayStation China Hero Project. The game is a hack 'n' slash action RPG viewed from a Diablo-like camera. The setting is the ancient Silk Road, in China's Western Regions. Loulan: The Cursed Sand tells the tragic love story of an exiled royal guard who returns to the titular fallen kingdom amidst the chaos of war in search of his beloved princess. Players will step into the game as the skeletal warrior known as 'The Cursed Sand', mastering the power of sand as […]

Read full article at https://wccftech.com/loulan-the-cursed-sand-chinese-hack-n-slash-arpg/

Samsung looks to be all set to announce its first triple-folding smartphone, the Galaxy Z TriFold, and even though the device is expected to be limited to a few markets, it was high time that we saw smartphones gravitate to a new form factor. Just before the official announcement happens, a series of images provides a first look at the Galaxy Z TriFold, showing a dual-infolding structure that can transform into a large-screen tablet. The Galaxy Z TriFold was on display at the Samsung booth at the K-Tech Showcase, with one report stating that the prototype did not display any […]

Read full article at https://wccftech.com/samsung-galaxy-z-trifold-first-look-image-gallery/

The most powerful way to build professional mobile apps.

Discussion | Link

The workspace for music creators

Discussion | Link

Turn your AI tool into a personal email marketing assistant

Discussion | Link

Your LinkedIn post studio from sources to impact

Discussion | Link

Private voice reminders for Mac with no cloud or accounts

Discussion | Link

Organize & edit local files with AI from your browser

Discussion | Link

Know What You Ship. Secure What You Depend On.

Discussion | Link

Compare API models by benchmarks, cost & capabilities

Discussion | Link

Secure file uploads for Intercom, Crisp & Zendesk

Discussion | Link

Personal health coach built with Gemini

Discussion | Link

Elon's answer to Wikipedia

Discussion | Link

The first add-on to manage Google Sheets tabs efficiently

Discussion | Link

Automatically adds short poems to your photos, for free.

Discussion | Link

Vibe Your Agentic Workflows

Discussion | Link

The post On-Policy Distillation LLMs Redefine Post-Training Efficiency appeared first on StartupHub.ai.

On-policy distillation LLMs from Thinking Machines Lab offer a highly efficient and cost-effective method for post-training specialized smaller models, combining direct learning with dense feedback.

The post On-Policy Distillation LLMs Redefine Post-Training Efficiency appeared first on StartupHub.ai.

The post Tensormesh exits stealth with $4.5M to slash AI inference caching costs appeared first on StartupHub.ai.

Tensormesh's AI inference caching technology eliminates redundant computation, promising to make enterprise AI cheaper and faster to run at scale.

The post Tensormesh exits stealth with $4.5M to slash AI inference caching costs appeared first on StartupHub.ai.

Self Hosted Project is platform to launch your own VPN server in minutes—no tech skills needed. Full root access, 32+ locations, total privacy. No KYC, no logs, 7 protocols. Just your server. VPN done right. Self-hosted. Self-controlled.

View startup

Meta plans to lay off more than 100 employees in Washington state as part of a broader round of cuts within its artificial intelligence division.

A new filing with the state’s Employment Security Department shows 101 employees impacted, including 48 in Bellevue, 23 in Seattle, and four in Redmond, along with 23 remote workers based in Washington.

The filing lists dozens of affected roles across Meta’s AI research and infrastructure units, including software engineers, AI researchers, and data scientists. Meta product managers, privacy specialists, and compliance analysts were also affected.

Meta is cutting around 600 positions in its AI unit, Axios reported last week. The company is investing heavily in AI and wants to create a “more agile operation,” according to an internal memo cited by Axios. Meta has just under 3,000 roles within its superintelligence lab, CNBC reported.

The separations at Meta in Washington take effect Dec. 22, according to the Worker Adjustment and Retraining Notification (WARN) notice filed Oct. 22.

Meta employs thousands of people across multiple offices in the Seattle region, one of its largest engineering hubs outside Menlo Park.

The latest reductions mark another contraction for Meta’s Pacific Northwest footprint following multiple rounds of layoffs over the past several years.

The company’s rapid expansion in Seattle over the past decade made it one of the emblems of the region’s tech boom, coinciding with Microsoft’s resurgence and Amazon’s rise.

Among the Bay Area titans, Google was among the first to establish a Seattle-area engineering office, way back in 2004. However, it was Facebook’s decision to open its own outpost across from Pike Place Market in 2010 that really got the attention of their Silicon Valley tech brethren.

In the decade that followed, out-of-town companies set up more than 130 engineering centers in the region.

However, more recently Meta has made moves to trim its Seattle-area footprint.

Apple earlier this year took over a building previously occupied by Meta in Seattle’s South Lake Union neighborhood, near Amazon’s headquarters. CoStar reported in April that Meta listed its other Arbor Blocks building for sublease.

Meta previously gobbled up much of the planned office space at the Spring District, a sprawling development northeast of downtown Bellevue, including a building that was originally going to be a new REI headquarters. But it has subleased some of the space since then to companies such as Snowflake, which recently took an entire building from Meta at the Spring District.

Meta’s office in Redmond, near Microsoft’s headquarters, is focused on its mixed reality development.

GeekWire has reached out to the company for an updated Seattle-area headcount.

Meta’s cuts come amid reported layoffs at Amazon that could impact up to 30,000 workers.

Tech companies have laid off more than 128,000 employees this year, according to Layoffs.fyi. Last year, companies cut nearly 153,000 positions.

Quietnet blocks ads, tracking, and harmful websites before they even reach your phone, laptop, or TV — no apps needed, and it works for every device in your home or office.

We make the internet faster, safer, and more private for families and small businesses — without the noise. We're not backed by big tech or VC money. We're privacy-focused, bootstrapped, and already seeing people pay for peace and quiet online. QuietNet is built by people who care — no ads, no tracking, just a cleaner, safer internet for your family or team.

View startup

AMD gains $3 billion by divesting from ZT Systems’ manufacturing business – retains key talent AMD has confirmed that it has officially divested from ZT Systems’ manufacturing business, selling it to Sanmina for $3 billion. This recoups most of AMD’s acquisition costs from its ZT Systems (ZTS) purchase earlier this year, and secures AMD a […]

The post AMD gains $3 billion divesting from ZT Systems’ manufacturing business appeared first on OC3D.

Voxtara is your personal AI speech coach that helps you become a more confident and effective public speaker. Whether you're preparing for a big presentation, teaching a class, or pitching to investors, Voxtara provides instant, actionable feedback to help you improve.

Key Features: • AI-Powered Analysis: Get comprehensive feedback on clarity, pacing, confidence, engagement, and body language • Video Recording: Record practice sessions up to 5 minutes • Deep-Dive Reports: Detailed analysis • Progress Tracking: Watch your speaking skills improve over time with detailed analytics • Practice Reminders: Set custom reminders • Session History: Review past performances

View startup

These quick tips could help you get more traction with your email outreach. 

With cyber scams on the increase, TikTok's looking to help raise awareness among its user community.

YouTube launched Ask Studio, an AI assistant in YouTube Studio that analyzes channel data to surface comment insights, performance analysis, and content ideas.

The post YouTube Introduces ‘Ask Studio’ AI For Channel Analytics appeared first on Search Engine Journal.

OpenAI is telling companies that “relationship building” with AI has limits. Emotional dependence on ChatGPT is considered a safety risk, with new guardrails in place.

The post OpenAI Flags Emotional Reliance On ChatGPT As A Safety Risk appeared first on Search Engine Journal.

The post Edge AI: The Key to Sustainable AI Energy Efficiency appeared first on StartupHub.ai.

Arm and SCSP's new paper highlights edge computing as the strategic imperative for achieving AI energy efficiency and securing U.S. competitiveness.

The post Edge AI: The Key to Sustainable AI Energy Efficiency appeared first on StartupHub.ai.

The post America’s AI Future: AMD Powers U.S. Sovereign AI appeared first on StartupHub.ai.

AMD and the DOE are launching Lux and Discovery supercomputers at ORNL, a $1 billion investment to establish secure U.S. Sovereign AI infrastructure.

The post America’s AI Future: AMD Powers U.S. Sovereign AI appeared first on StartupHub.ai.

The post ASEAN’s AI Ambition: Infrastructure, Innovation, and Tailored Governance appeared first on StartupHub.ai.

“Infrastructure is destiny,” declared James Hairston, Head of International Policy & Partnerships for Asia, Africa, & Latin America at OpenAI, encapsulating the strategic imperative facing Southeast Asia in the burgeoning age of artificial intelligence. This powerful statement set the stage for a compelling discussion at the Bloomberg Business Summit at ASEAN in Kuala Lumpur, where […]

The post ASEAN’s AI Ambition: Infrastructure, Innovation, and Tailored Governance appeared first on StartupHub.ai.