The Download: solar geoengineering’s future, and OpenAI is being sued

This is today’s edition of The Download, our weekday newsletter that provides a daily dose of what’s going on in the world of technology.

Solar geoengineering startups are getting serious

Solar geoengineering aims to manipulate the climate by bouncing sunlight back into space. In theory, it could ease global warming. But as interest in the idea grows, so do concerns about potential consequences.

A startup called Stardust Solutions recently raised a $60 million funding round, the largest known to date for a geoengineering startup. My colleague James Temple has a new story out about the company, and how its emergence is making some researchers nervous.

So far, the field has been limited to debates, proposed academic research, and—sure—a few fringe actors to keep an eye on. Now things are getting more serious. So what does it mean for geoengineering, and for the climate? Read the full story.

—Casey Crownhart

This article is from The Spark, MIT Technology Review’s weekly climate newsletter. To receive it in your inbox every Wednesday, sign up here.

If you’re interested in reading more about solar geoengineering, check out:

+ Why the for-profit race into solar geoengineering is bad for science and public trust. Read the full story.

+ Why we need more research—including outdoor experiments—to make better-informed decisions about such climate interventions.

+ The hard lessons of Harvard’s failed geoengineering experiment, which was officially terminated last year. Read the full story.

+ How this London nonprofit became one of the biggest backers of geoengineering research.

+ The technology could alter the entire planet. These groups want every nation to have a say.

The must-reads

I’ve combed the internet to find you today’s most fun/important/scary/fascinating stories about technology.

1 OpenAI is being sued for wrongful death
By the estate of a woman killed by her son after he engaged in delusion-filled conversations with ChatGPT. (WSJ $)
+ The chatbot appeared to validate Stein-Erik Soelberg’s conspiratorial ideas. (WP $)
+ It’s the latest in a string of wrongful death legal actions filed against chatbot makers. (ABC News)

2 ICE is tracking pregnant immigrants through specifically-developed smartwatches
They’re unable to take the devices off, even during labor. (The Guardian)
+ Pregnant and postpartum women say they’ve been detained in solitary confinement. (Slate $)
+ Another effort to track ICE raids has been taken offline. (MIT Technology Review)

3 Meta’s new AI hires aren’t making friends with the rest of the company
Tensions are rife between the AGI team and other divisions. (NYT $)
+ Mark Zuckerberg is keen to make money off the company’s AI ambitions. (Bloomberg $)
+ Meanwhile, what’s life like for the remaining Scale AI team? (Insider $)

4 Google DeepMind is building its first materials science lab in the UK
It’ll focus on developing new materials to build superconductors and solar cells. (FT $) 

5 The new space race is to build orbital data centers
And Blue Origin is winning, apparently. (WSJ $)
+ Plenty of companies are jostling for their slice of the pie. (The Verge)
+ Should we be moving data centers to space? (MIT Technology Review)

6 Inside the quest to find out what causes Parkinson’s
A growing body of work suggests it may not be purely genetic after all. (Wired $)

7 Are you in TikTok’s cat niche? 
If so, you’re likely to be in these other niches too. (WP $)

8 Why do our brains get tired? 🧠💤
Researchers are trying to get to the bottom of it.  (Nature $)

9 Microsoft’s boss has built his own cricket app 🏏
Satya Nadella can’t get enough of the sound of leather on willow. (Bloomberg $)

10 How much vibe coding is too much vibe coding? 
One journalist’s journey into the heart of darkness. (Rest of World)
+ What is vibe coding, exactly? (MIT Technology Review)

Quote of the day

“I feel so much pain seeing his sad face…I hope for a New Year’s miracle.”

—A child in Russia sends a message to the Kremlin-aligned Safe Internet League explaining the impact of the country’s decision to block access to the wildly popular gaming platform Roblox on their brother, the Washington Post reports.

 One more thing

Why it’s so hard to stop tech-facilitated abuse

After Gioia had her first child with her then husband, he installed baby monitors throughout their home—to “watch what we were doing,” she says, while he went to work. She’d turn them off; he’d get angry. By the time their third child turned seven, Gioia and her husband had divorced, but he still found ways to monitor her behavior. 

One Christmas, he gave their youngest a smartwatch. Gioia showed it to a tech-savvy friend, who found that the watch had a tracking feature turned on. It could be turned off only by the watch’s owner—her ex.

Gioia is far from alone. In fact, tech-facilitated abuse now occurs in most cases of intimate partner violence—and we’re doing shockingly little to prevent it. Read the full story

—Jessica Klein

We can still have nice things

A place for comfort, fun and distraction to brighten up your day. (Got any ideas? Drop me a line or skeet ’em at me.)

+ The New Yorker has picked its best TV shows of 2025. Let the debate commence!
+ Check out the winners of this year’s Drone Photo Awards.
+ I’m sorry to report you aren’t half as intuitive as you think you are when it comes to deciphering your dog’s emotions.
+ Germany’s “home of Christmas” sure looks magical.

How to Scale a Recommerce Business

The idea of selling used or overstock goods is not new. Secondhand and thrift shopping is as old as commerce itself.

What has changed is resale volume and the operational challenges that have emerged. Shops that want to sell used, refurbished, and overstock items should establish repeatable systems for handling sourcing, intake, authentication, grading, and pricing.

Repeatable Recommerce

Sourcing

The initial challenge is consistently finding desirable goods.

The aim is predictable systems for procuring products that turn over quickly and profitably.

  • Returns as inventory. Don’t overlook returned items. They are a reliable source of secondhand stock.
  • Customer trade-ins. Buy-back programs also provide a predictable supply of inventory and encourage repeat purchases. Merchants can let shoppers trade in and trade up apparel, outdoor gear, electronics, and luxury accessories. Carefully define what your business accepts and how credit is issued.
  • Liquidation sourcing. Platforms such as B-Stock, Bulq, and Liquidation.com offer bulk pallets from major retailers. The condition varies widely, often with incomplete manifests. Nonetheless, pallet sourcing remains a low-cost way to learn recommerce, especially in apparel and home goods.
  • Partnerships. Finally, many secondhand ecommerce businesses develop sourcing partnerships with manufacturers or other retailers to purchase clearance, end-of-season, or returned goods.

Intake

In circular commerce, intake drives goods toward a sale.

An effective intake workflow should move every item through a repeatable process, to:

  • Identify,
  • Clean,
  • Measure or test,
  • Document condition,
  • Photograph,
  • Authenticate,
  • Assign a grade,
  • List.

Each step is an opportunity to reduce the time from sourcing to sale. The better the intake process, the better the cash flow.

While each of these tasks is essential, the last three require extra attention.

Authenticate

Some categories of secondhand products require authentication or certification.

For example, a shop that lists a large Prada Galleria bag (which sells new in 2025 for $5,100) had better ensure it’s a genuine Prada. Counterfeits can kill a recommerce business.

Services such as Entrupy, Certilogo, and category-specific verification tools can help. In most cases, submitting photographs will be enough to authenticate an item.

Screenshot of a used Prada bag for sale

A buyer for a used Prada bag seeks quality and brand recognition.

Grading

Recommerce grading can take two forms.

First, the description for every item should address its condition. Grading could be as simple as “like new” or “fair.” For such subjective grades, try to have a repeatable standard. For example, apparel with stitching needs can only be labeled “fair.”

Mistake in grading — too much or too little — erodes trust.

A second form of grading applies to collectible goods. Books, for example, often have grades such as “mint,” “fine,” and “near fine,” each with a specific definition.

When products have a standard and accepted grading system, use it.

Listing

Where to list — offer or sale — a secondhand, refurbished, or overstock item requires market awareness and understanding, and a bit of skill.

The listing should be priced competitively for a given market. A refurbished Xbox juxtaposed with a new one on a retailer’s website may sell at a higher price than on Facebook Marketplace or eBay.

The price difference among markets should not discourage a seller from listing on all or many of them. Instead, it implies the need to use different listing strategies, each emphasizing different features or values.

Product descriptions on Amazon Renewed might focus on the expert refurbishing or like-new performance, while apparel listings on ThreadUp could stress environmental sustainability.

Screenshot of Amazon Renew web page

An Amazon Renew shopper likely differs from one on Threadup focused on environmental sustainability.

Recommerce Success

Recommerce can supplement a retailer’s primary sales channel by extracting value from returns, trade-ups, and overstock inventory.

It can also become a standalone business model, where merchants buy and sell across multiple marketplaces.

Success in either model depends on processes and workflows. Shops that standardize intake, grading, authentication, and listing practices earn consumer trust, resulting in faster turnover and lower returns.

Google Releases December 2025 Core Update via @sejournal, @MattGSouthern

Google has released the December 2025 core update, the company confirmed through its Search Status Dashboard.

The rollout began at 9:25 a.m. Pacific Time on December 11, 2025.

This marks Google’s third core update of 2025, following the March and June core updates earlier this year.

What’s New

Google lists the update as an “incident affecting ranking” on its status dashboard.

The company states the rollout “may take up to three weeks to complete.”

Core updates are broad changes to Google’s ranking systems designed to improve search results overall. Unlike specific updates targeting spam or particular ranking factors, core updates affect how Google’s systems assess content across the web.

2025 Core Update Timeline

The December update follows two previous core updates this year.

The March 2025 core update rolled out from March 13-27, taking 14 days to complete. Data from SEO tracking providers suggested volatility similar to the December 2024 core update.

The June 2025 core update ran from June 30 to July 17, lasting about 16 days. SEO data providers indicated it was one of the larger core updates in recent memory. Some sites previously hit by the September 2023 Helpful Content Update saw partial recoveries during this rollout.

Documentation Update On Continuous Changes

Two days before this core update, Google updated its core updates documentation with new language about ongoing algorithm changes.

The updated documentation now states:

“However, you don’t necessarily have to wait for a major core update to see the effect of your improvements. We’re continually making updates to our search algorithms, including smaller core updates. These updates are not announced because they aren’t widely noticeable, but they are another way that your content can see a rise in position (if you’ve made improvements).”

Google explained that the addition was meant to clarify that content improvements can lead to ranking changes without waiting for the next announced update.

Why This Matters

If you notice ranking fluctuations over the coming weeks, this update is likely a major factor.

Core updates can shift rankings for pages that weren’t doing anything wrong. Google has consistently stated that pages losing visibility after a core update don’t necessarily have problems to fix. The systems are reassessing content relative to what else is available.

The documentation update is a reminder that rankings can change between major updates as Google rolls out smaller core changes behind the scenes.

Looking Ahead

Google will update the Search Status Dashboard when the rollout is complete.

Monitor your rankings and traffic over the next three weeks. If you see changes, document when they occurred relative to the rollout timeline.

Based on 2025’s previous updates, completion typically takes two to three weeks. Google will confirm completion through the dashboard and its Search Central social accounts.

14 Things Executives And SEOs Need To Focus On In 2026 via @sejournal, @DuaneForrester

So many people spent 2025 arguing about whether SEO was dying. It was never dying. It was shifting into a new layer. Discovery continues to move from search boxes to AI systems. Answers now come from models that rewrite your work, summarize competitors, blend sources, and shape decisions before a browser window loads. In 2026, this shift becomes visible enough that executives and SEOs can no longer treat it like an edge case; percentages from sources will shift. The search stack that supported the last 20 years is now only one of several layers that shape customer decisions. (I talk about all this in my new book, “The Machine Layer” (non-affiliate link).)

This matters because the companies that win in 2026 will be the ones treating AI systems as new distribution channels. The companies that lose will be the ones waiting for their analytics dashboards to catch up. You no longer optimize for a single front door. You now optimize for many. Each one is powered by models that decide what to show, who to show it to, and how to describe you.

Here are 14 things that will define competitive advantage in 2026. Each one is already visible in real data. Together, they point to a year where discovery becomes more ambient, more conversational, and more dependent on how well a machine can parse and trust you. And at the end of this list is one heck of a prediction that I bet you didn’t see coming for next year! If I’m being honest, I’m sure a few of you did, but to this depth? Realizing it was all so close?

Grab a coffee or tea, find your favorite spot to read, and let’s get started!

Image Credit: Duane Forrester

1. AI Answer Surfaces Become The New Front Door

ChatGPT, Claude, Gemini, Meta AI, Perplexity, CoPilot, and Apple Intelligence now sit between customers and your website. More and more users ask questions inside these systems before they ever search. And the answers they get are inconsistent. BrightEdge’s analysis showed that AI engines disagree with each other 62% of the time. When engines disagree this much, brand visibility becomes unstable. Executives need reporting that reveals how often their brand appears inside these systems. SEOs need workflows that evaluate chunk retrieval, embedding strength, and citation presence across multiple answer engines.

2. Content Must Be Designed For Machine Retrieval

Microsoft’s 2025 Copilot study analyzed more than 200,000 work sessions. The most common AI-assisted tasks were gathering information, explaining information, and rewriting information. These are the core tasks modern content must support. AI models choose content that is structured, predictable, and easy to embed. If your content lacks clear sectioning, consistent patterns, or explicit definitions, it becomes harder for models to use. This impacts whether you appear in answers. In 2026, your formatting choices become ranking signals for machines.

3. On-Device LLMs Change How People Search

Apple Intelligence runs many tasks locally. It also rewrites queries in more natural conversational patterns. This pushes search activity away from browsers and deeper into the operating system. People will ask their device short, private questions that never hit the web. They will ask follow-up questions inside the OS. They will make decisions without ever visiting a page. This shifts both volume and structure. SEOs will need content designed for lightweight, edge device retrieval.

4. Wearables Start Steering The Discovery Funnel

Meta Ray Bans already support visual queries. The user points at something and asks what it is. Voice and camera replace typing. This increases micro queries tied to real-world context. Expect to see more identify thiswhat does this do, and how do I fix that queries. Wearables compress the distance between stimulus and search. Executives should invest in image quality, product clarity, and structured metadata. SEOs should treat visual search signals as core inputs.

5. Short-Form Video Becomes A Training Input For AI

Video is now a core training signal for modern multimodal models. V-JEPA 2 from Meta AI is trained on an unknown number of hours of raw video and images, but this still shows that large-scale video learning is becoming foundational for motion understanding, physical prediction, and video question answering. Gemini 2.5 from Google DeepMind explicitly supported video understanding, allowing the model to interpret video clips, extract visual and audio context, and reason over sequences. OpenAI’s Sora research demonstrates that state-of-the-art generative video models learn from diverse video inputs to understand motion, physical interactions, transitions, and real-world dynamics. In 2026, your short-form video becomes part of your broader signal footprint. Not only the transcript. The visuals, pacing, motion, and structure become vectors the model can interpret. When your video output and written content diverge, the model will default to whichever medium communicates more clearly and consistently.

6. Organic Search Signals Shift Toward Trust And Provenance

Traditional algorithms relied on links, keywords, and click patterns. AI systems shift that weight toward provenance and verification. Perplexity describes its model as retrieval-augmented, pulling from authoritative sources like articles, websites, and journals and surfacing citations to show where information comes from. Independent audits support this direction. A 2023 evaluation of generative search engines found that systems like Perplexity favored content that is factual, well-structured, and supported by external evidence when assembling cited answers. This remains true today as well. SEO industry analysis also shows that pages with clear metadata, consistent topical organization, and visible author identity are more likely to be cited. Naturally, all of this changes what trust looks like. Machines prioritize consistency, clarity, and verifiable sourcing. Executives should focus on data governance and content stability. SEOs should focus on structured citations, author attribution, and semantic coherence across their content ecosystem.

7. Real-Time Cohort Creation Replaces Static Personas

LLMs build temporary cohorts by clustering people with similar intent patterns. These clusters can form in seconds and dissolve just as fast. They are not tied to demographics or personas. They are based on what someone is trying to do right now. This is the basis of the experiential cohort concept. Marketers have not caught up yet. In 2026, cohort-based targeting will shift toward intent embeddings and away from persona documents. SEOs should tune content for intent patterns, not identity attributes.

8. Agent-To-Agent Commerce Becomes Real

Agents will schedule appointments, book travel, reorder supplies, compare providers, and negotiate simple agreements. Your content becomes instructions for another machine. To support that, it must be unambiguous. It must be explicit about requirements, constraints, availability, pricing rules, and exceptions. If you want an agent to pick your business, you need a content model that feeds the agent’s decision tree. Executives should map the top 10 agent-mediated tasks in their industry. SEOs should design content that makes those tasks easy for a machine to interpret.

9. Hardware Acceleration Pushes AI Into Every Routine

NVIDIA, Apple, and Qualcomm are all building hardware optimized for on-device and low-latency AI inference. These chips reduce friction, which increases the number of everyday questions people ask without ever opening a browser. NVIDIA’s data center inference platforms show how much compute is moving toward real-time model execution. Qualcomm’s AI Hub highlights how modern phones can run complex models locally, shrinking the gap between thought and action. Apple’s M-series chips include Neural Engines that support local model execution inside Apple Intelligence. Lower friction means people will ask more small, immediate questions as they move through their day instead of grouping everything into one session. SEOs should plan for discovery happening across many short, assistant-driven interactions rather than a single focused search moment.

10. Query Volume Expands As Voice And Camera Take Over

Voice input grows the long tail. Camera input grows contextual queries. The Microsoft Work Trend Index shows rising AI usage across everyday task categories, including personal information gathering. People ask more questions because speaking is easier than typing. The shape of demand widens, which increases ambiguity. SEOs need stronger intent classification workflows and a better understanding of how retrieval models cluster similar questions.

11. Brand Authority Becomes Machine Measurable

Models determine authority by measuring consistency across your content. They look for stable terminology, clear entity relationships, and patterns in how third parties reference you. They look for alignment between what you publish and how the rest of the web describes your work. This is not the old human quality framework. It is a statistical confidence score. Executives should invest in knowledge graphs. SEOs should map their entity network and tune the language around each entity for stability.

12. Zero-click Environments Become Your Primary Competitor

Answer engines pull from multiple sources and give the user a single synthesized answer. This reduces visits but increases influence. In 2026, the dominant competitors for organic attention are ChatGPT, Perplexity, Gemini, CoPilot, Meta AI, and Apple Intelligence. You do not win by resisting zero click. You win by being the source the engine prefers. Executives must adopt new performance metrics that reflect answer presence. SEOs should run monthly audits of brand visibility across all major platforms, tracking citations, mentions, paraphrases, and omissions.

13. Competitive Intelligence Shifts Into Prompt Space

Your competitors now live inside AI answers, whether they want to or not. Their content becomes part of the same retrieval memory that models use to answer your queries. In 2026, SEOs will evaluate competitor visibility by studying how platforms describe them. You will ask models to summarize competitors, benchmark capabilities, and compare offerings. The insights you get will shape strategy. This becomes a new research channel that executives can use for positioning and differentiation.

14. Your Website Becomes A Training Corpus

AI systems will digest your content many times before a human does. That means your site is now a data repository. It must be structured, stable, and consistent. Publishing sloppy structure or unaligned phrasing creates noise inside retrieval models. Executives should treat their content like a data pipeline. SEOs should think like information architects. The question shifts from how do we rank to how do we become the preferred reference source for a model.

The companies that succeed in 2026 will be the ones that understand this shift early. Visibility now lives in many places at once. Authority is measured by machines, not just people. Trust is earned through structure, clarity, and consistency. The winners will build for a world where discovery is ambient, and answers are synthesized. The losers will cling to dashboards built for a past that is not coming back.

Now, if you’ve read this far, thank you, and I have a surprise – an actual prediction for 2026! I think it’s a big, important one, so buckle up!

I’m calling this Latent Choice Signals, or these, I suppose, as it’s a grouping of signals that paint a picture for the platforms. From the consumer’s POV, this is the essential mental map they’re following: “I saw it, I felt something about it, and I decided not to continue.” This is the core. The user’s mind is making a choice, even if they never articulate it or click anything. That behavior generates meaning. And the system can interpret that meaning at scale. Let’s dig in…

The Prediction No One Sees Coming

By the end of 2026, AI systems will begin optimizing decisions around the patterns users never articulate. Not the queries they type. Not the questions they ask. But the choices they avoid.

This is the shift almost everyone misses, and you can see the edges of it forming across three different fields. When you pull them together, the picture becomes clearer.

First, operating system-level AI is already learning from behavior that is not explicitly expressed. Apple Intelligence is described as a personal intelligence layer that blends generative models with on device personal context to prioritize messages, summarize notifications, and suggest actions across apps. Apple built this for convenience and privacy, but it created something more important. The system must learn over time which suggestions people accept and which they quietly ignore. It sees which notifications get swiped away, which app actions never get used, and which prompts are abandoned. It does not need to read your mind. It only needs to see which proposed actions never earn a tap. Those patterns are already part of how it ranks what to surface next.

Second, recommender systems already treat non-actions as meaningful signals. You see it every time you skip a YouTube video, swipe past a TikTok in under a second, or close Netflix when the row of suggestions feels wrong. These platforms do not publish their exact mechanics, but implicit feedback is a well-established concept in the research world. Classic work on collaborative filtering for implicit feedback datasets shows how systems use viewing, skipping, and browsing behavior to model preference, even when users never rate anything directly. Newer work continues to refine how clicks, views, and avoidance patterns feed recommendation models at scale. It is reasonable to expect LLM-driven assistants to borrow from the same logic. The pattern is too useful to ignore. When you close an assistant, rephrase a question to avoid a certain brand, or scroll past a suggestion without engaging, that is data about what you did not want.

Third, alignment research already trains models to follow what humans prefer, not just what text predicts. OpenAI’s “Learning to summarize with human feedback” work shows how models can be tuned using human comparisons between outputs, with a reward model that learns which responses people think are better. This has been in play for years now. This kind of reinforcement learning from human feedback was built for tasks like summarization and style, but the underlying principle matters here. Models can be optimized around patterns of acceptance and rejection. Over time, conversational systems can extend this to live settings, where corrections, rewrites, and abandonments are treated as signals about what the user did not want, even when they never spell that out.

Put these three domains together, and a larger pattern emerges. As AI systems move into glasses, phones, laptops, cars, and operating systems, they will gain precise visibility into the choices people avoid. These avoidance patterns will become signals that inform how assistants rank options, choose providers, and recommend products.

This will not feel like surveillance. The model is not peeking into your private life. It is watching your interaction patterns with the system itself. It sees where you hesitate, which suggestions you skip, which tasks you hand off, which providers create follow-up questions, which prices cause users to pause, which explanations reduce confidence, and which interfaces break the chain of intent. These are all first-party behavioral signals the assistant is already allowed to use. And that platforms see these signals on a global scale.

In 2026, these Latent Choice Signals will become strong enough that they form a new optimization layer. A silent ranking system built around friction. If your brand generates hesitation, the assistant will reduce your visibility long before your analytics flag a problem. If your content creates confusion during synthesis, it will be bypassed during retrieval. If your policies trigger too many follow-up questions, the model will favor a competitor with clearer flows. The user will never know why. All they will see is the assistant presenting a different option.

This is the layer that will blindside executives. Dashboards will look normal. Rankings may appear stable. Traffic may hold steady. Yet conversions inside AI-mediated decisions will drift. Customers will stop choosing you, not because you lost traditional ranking signals, but because you introduced cognitive friction the model can detect and optimize against.

The winners will be the companies that treat avoidance as a measurable signal. They will analyze which parts of their product and content cause hesitation. They will refine policies to reduce ambiguity. They will simplify offerings. They will align explanations with how models process uncertainty. They will build experiences that reduce agent-level friction and improve confidence inside a retrieval sequence.

By late 2026, negative intent signals may become one of the strongest competitive filters in digital business. Not because users say anything, but because their silence now has structure the model can learn from. Anyone watching today’s data can see this shift forming, but almost no one is naming it. Yet the early indicators are already here, hiding between the interactions users never get far enough to complete.

This is the prediction that will define the next phase of AI-driven discovery. And the companies that understand it early will be the ones the assistants prefer.

More Resources:


This post was originally published on Duane Forrester Decodes.


Featured Image: Collagery/Shutterstock

Well-Known SEO Explains Why AI Agents Are Coming For You & What To Do Now via @sejournal, @theshelleywalsh

I’m carefully watching the development of agentic SEO, as I believe over the next few years, as capabilities improve, agents will have a significant impact on the industry. I’m not suggesting this will be a seamless replacement of talent with a highly capable machine intelligence. There is going to be a lot of trial and error, but I do think we are going to see radical shifts in how the online space operates. Not unlike how automation transformed manufacturing.

Marie Haynes has long been a well-known expert in the industry who shared her learnings on E-E-A-T and Google’s algorithm through her popular Search News You Can Use newsletter.

A few years ago, Marie made the decision to retire her SEO agency and went all in on learning AI systems, as she believes we’re at the beginning of a profound transformation.

Marie wrote a recent article, “Hype or not, should you be investing in AI agents?” about what SEOs need to understand about this rapidly developing space. So, I invited her to IMHO to dive more into this topic.

Marie believes AI will radically change our world for the better, and she believes every business will have AI agents.

You can watch the full interview with Marie on the IMHO recording at the end, or continue reading the article summary.

“The idea that we optimize for appearing as one of the 10 blue links on Google is already gone.”

Experimenting With Gemini Gems

Marie’s practical advice for anyone wanting to understand agents is to start with Gems:

“If you take one thing from this conversation, it’s to try to create some Gemini Gems,” Marie emphasized. “Eventually I’m fairly certain that these gems will morph into agentic workflows.”

To illustrate, she shared a process she called her “originality Gem,” which contains a 500+ word prompt that captures how she evaluates content, along with examples of truly original content in its knowledge base.

“We’re not far from the day where all of my processes that I do for SEO can be handled by agentic workflows that occasionally pull on me for some advice,” Marie said.

The Power Of Chaining Agents

The next progression and real potential come from chaining agents together to create agentic workflows.

The power that this gives opportunity to is that we can use our knowledge and experience to teach AI like a team of assistants to do the work that can be automated.

We would then orchestrate the process and, like a conductor, sit and guide the agents to perform the work as we become the human-in-the-loop to review the output.

Once we have downloaded our knowledge to the agents, and the systems work, we can scale ourselves to handle exponential clients.

“Instead of me handling just a small handful of clients, all of a sudden I could have a hundred clients and do the same work because it’s all going through my workflow,” Marie said.

The challenge here is the skill in prompting the agents and constructing them to achieve the desired output.

“The future of our industry is not about optimizing for an engine, but about acting as the interface between businesses and technology, and we will be the human experts who teach, guide, and implement AI agents.”

Why Gemini Over ChatGPT

I asked Marie why she focuses on Gemini over ChatGPT, and her response was based on futureproofing: “The main reason why I use Gemini is not to accomplish things today, but to grow my skills in what’s coming tomorrow.”

Marie went on to explain that “Google’s got a whole ecosystem that you can see it coming together like right now,” and she believes that Google will be the winner in the AI race.

“I think that Google is going to win the game. I think it’s always been their game to win. So I make it a point to use Gemini as much as I can.”

Transformations Will Follow The Money

Marie’s prediction for the next few years is for workflows to become embedded. “Sundar Pichai, CEO of Google, said this way back in March, that, in two to four years, every agentic workflow will be deeply embedded into our day-to-day work.”

However, she thinks the real transformations will come when businesses start making money from agentic workflows.

“It’s wild how many trillions of dollars are being spent on developing AI, yet there’s not a whole lot of financial output at this point,” Marie noted, referencing a McKinsey study showing 95% of businesses using AI aren’t making money from it yet [Editor’s note: McKinsey was 80%; MIT said 95%].

“It’s very similar to SEO. There was a day where there were just a small handful of people who figured out how to improve on Google. Once people started making good money from understanding SEO, there was a lot of attention. Tools were created and a whole industry popped up. I think that’s going to happen again. Will it be within the next 12 months? I don’t know. I feel like it might be a little bit longer.”

What SEOs Should Do Now

Overwhelm is a real issue to be aware of, and with developments moving so quickly, there is a huge learning curve to essentially retrain. Even for those working on this full-time.

Marie made a commitment when she went all in on AI research. “I made it my full-time job to stay on top of what’s happening, and even I get overwhelmed with all the stuff that’s happening with AI,” she explained.

Marie’s advice is to keep learning, keep trying things, and experiment with writing prompts.

“The next time you go to do a task, try to create an agent that would do this for you,” she suggested. Even if you don’t finish, you’ll learn skills for the next attempt.

Also, persevere instead of taking the first failure. “Try to figure out what they can do, instead of just telling everybody, ‘Oh, it can’t do this.’ Find ways you can use it.”

For development teams, she recommends vibe coding with tools like Google’s Anti Gravity or AI Studio. “You can deploy a whole website without even knowing any HTML,” Marie said.

She also advocates for deep research reports using either Gemini or ChatGPT to analyze how competitors are using AI, providing immediate value to clients while building skills.

The Future Of SEO

Marie referenced Sundar Pichai calling AI technology more profound than fire or electricity in its impact on society. Despite acknowledging her bias after investing significant time in understanding AI, she maintains there’s going to be societal disruption.

“Being able to understand what’s happening in the world and distill it down to what’s important to your clients will be a superpower,” she said. Although, she does admit, there is still a lot of learning and grey areas to move through as we navigate the edge of technology.

“If you’re feeling lost, you’re not alone because imagine right now we’re sort of at the forefront of all of these changes happening.”

For those who do persevere, there will be significant rewards. Eventually, business owners will be clamoring for people who can explain AI and implement it. The professionals who develop these skills now will be extremely valuable in the future.

“The people who know how to use AI, know how to create agents, and know how to make money from AI are going to be extremely valuable in the future.”

Watch the full video interview with Marie Haynes here:

Thank you to Marie Haynes for offering her insights and being my guest on IMHO.

More Resources:


Featured Image: Shelley Walsh/Search Engine Journal

Ask A PPC: What Are Learning Periods In Digital Marketing? via @sejournal, @navahf

Most ad platforms have something called a “learning period.” This is not a period for the marketer to observe and learn from the performance. Instead, it’s a period of time ranging from 48 hours to two to four weeks when the ad platform is learning how the campaigns should behave based on conversion rates and auction prices of targets.

There is a lot of debate in the industry around learning periods and how much they impact or don’t impact performance at various stages of an account’s life. This post will:

  • Outline exactly what’s covered in learning periods.
  • What can reset learning periods, and whether you should be concerned about that.
  • Strategies to work with learning periods at various stages of an account.

Note: This is written by a Microsoft employee, and the content is intended to be a platform-agnostic take on learning periods.

What Is Covered In Learning Periods?

Learning periods revolve mostly around conversion tracking and bidding. However, they can also be impacted by ad creative.

Campaigns in learning periods might under- or overbid in the first few days of going live. This is because the algorithm is learning what auction prices (CPCs or CPMs) will serve the campaign based on the targets chosen. However, if the campaign is in an older account, it might clear this learning faster. Additionally, if there is a lot of data (either historical data from other campaigns or spend to gather data more quickly) it’s possible to clear learning periods faster.

Ad creative learning periods revolve around which creative is served more often and paired with other supplied creative. While you can pin creative to force it to serve in specific spots, that may limit the placements available to you.

What Triggers Learning Periods And How Concerned Should You Be?

Lots of things can trigger learning periods, though how “severe” the learning period is depends on historical data as well as the specific changes being made.

Here is a list of common actions a marketer can take that would trigger learning periods:

  • Pausing a campaign for more than 72 hours.
  • Changing the budget more than 15% in a 7-day period.
  • Pausing a keyword/ad that has conversions to launch a new one.
  • Changing TCPA/TROAS goals (especially if they are large changes).
  • Adding a new campaign (learning period contained in the new campaign).

Note that changing creative in an existing ad, as well as small pauses, are not enough to trigger a learning period. This is because there’s enough data to counteract a small interruption.

However, if you’re making changes to all creative, that creative will still need to go through editorial. If you will be making that kind of wholesale change to an ad, it might be better to create a new ad and then change the rotation to rotate indefinitely.

Learning periods typically mean spend fluctuation (i.e., spending more per click or not serving as often as you were before). Ideally, you would make any needed changes to your campaigns before a major event like seasonal shopping events or major times for your service. However, if you can’t avoid those changes, these are the signs to look for that you might need to build in an extra 15-20% “learning period budget” to clear it faster:

  1. Impression share lost to rank goes up by more than 30%. If your impression share lost to rank is on the rise, that’s a sign you’re being forced to underbid for your targets. This is very common in learning periods as ad platforms wrestle with new conversion data.
  2. Average CPCs rise/fall by ~50%. While related to impression share, the most obvious sign of learning periods is fluctuating CPCs. Many understandably find it frustrating when CPCs rise, the more insidious change is when they drop. This is a sign you’re likely not serving for previously attainable queries.
  3. Drops in CTR, especially if drops in conversion rate follow. Learning periods in creative mean your headlines, descriptions, image assets, and other components of your ads may not serve in ideal pairings. If your ad was previously getting decent CTR and that has fallen, it could be a sign that learning periods are causing less-than-ideal pairings. This also could be a sign that the creative you’re testing are not ideal.

How To Work With Learning Periods At All Stages Of An Account

It’s important to put learning periods in context: They’re not monsters, and they’re not imaginary. They’re akin to taking a nap in the middle of the day or taking it easy if you get a migraine. Successful account management requires us to work with learning periods, but not allow them to dominate our strategy.

In new accounts, it’s fair to be fearless. Everything is new, and there’s a built-in expectation that campaigns will take extra time to ramp up. This is the time to make any needed changes and be bold in structure choices. Once the account finds its rhythm (i.e., consistent conversion volumes), it will be much harder to make bigger changes without initiating learning periods.

Accounts with at least 90 days of data should embrace the historical data they have. It means new campaigns will ramp up faster, and you likely can lean into conversion-based bidding. However, any major budget change (more than 15%) will likely cause fluctuations. This is why week-over-week increases until you reach the ideal budget are better.

Once you have more than a year of data, you should be pretty stable and able to launch new entities without issue. Major changes to existing entities with conversions should only be undertaken if absolutely necessary, and even then, you may want to use data exclusions to help the algorithms recover.

Learning periods are a normal part of managing campaigns. The key is to understand what triggers them and how to work with them.

More Resources:


Featured Image: Paulo Bobita/Search Engine Journal

WordPress Meets Vibe Coding: White-Labeled Platform & API For Search-Ready AI Websites

This post was sponsored by 10Web. The opinions expressed in this article are the sponsor’s own.

Not long ago, building a website meant a discovery call, a proposal, a sitemap, and a few weeks of back and forth. Today, we go from “I need a website” to “Why isn’t it live yet?” People are getting used to typing a short prompt and seeing an entire site structure, design, and a first-draft of their site in minutes. That doesn’t replace all the strategy, UX, or growth work, but it changes expectations about how fast the first version should appear, and how teams work.

This shift puts pressure on everyone who sits between the user and the web: agencies, MSPs, hosting companies, domain registrars, and SaaS platforms. If your users can get an AI-generated site somewhere else in a few clicks, you better catch the wave or be forgotten.

That’s why the real competition is moving to those who control distribution and can embed an AI-native, white-label builder directly into products. WordPress still powers over 43% of all websites globally, and remains the default foundation for many of these distribution players.

Now that AI-native builders, reseller suites, and website builder APIs are available on top of WordPress, who will own that experience and the recurring revenue that comes with it.

AI & Vibe Coding Is Turning Speed-To-Launch Into a Baseline 

AI site builders and vibe coding tools have taught people a new habit: describe what you want, get a working draft of a site almost immediately.

Instead of filling out long briefs and waiting for mockups, users can:

  • Type or paste a business description,
  • Point to a few example sites,
  • Click generate,
  • And see a homepage, key inner pages, and placeholder copy appear in minutes.

For non-technical users, this is magic. For agencies and infrastructure providers, it’s a new kind of pressure. The baseline expectation has become seeing something live quickly and refining it afterward.

This demand is everywhere:

  • Small businesses want a site as soon as they buy a domain or sign up for SaaS.
  • Creators expect their website to follow them seamlessly from the tools they already use.
  • Teams inside larger organizations need landing pages and microsites created on demand, without long internal queues.

If you’re an agency, MSP, hosting provider, domain registrar, or SaaS platform, you’re now measured against that baseline, no matter what your stack was designed for. Bolting on a generic external builder isn’t enough. Users want websites inside the experience they trust and already pay you for, with your branding, your billing, and your support.

AI-native builders that are built directly into your stack are no longer a nice bonus but an essential part of your product.

With Vibe Coding Leveling The Field: What Is Your Differentiator? 

In this environment, the biggest advantage doesn’t belong to whoever ships the flashiest AI demo. It belongs to whoever owns the distribution channels:

  • Agencies and MSPs, the ground level players holding client relationships and trust.
  • Hosting and cloud providers where businesses park their infrastructure.
  • Domain registrars where the online journey starts.
  • SaaS platforms, already owning the critical data needed to reflect and sync with company websites.

These players already control the key moments when someone goes from thinking they need a website to taking action.

  • Buying a domain
  • Using a vertical SaaS product
  • Working with an MSP or agency retainer
  • Adding a new location, service, or product line

If, at those moments, the platform automatically provides an AI-generated, editable site under the same login, billing, and support, the choice of stack is made by default. Users simply stay with the builder that’s already built into the service or product they use.

This is why white-label builders, reseller suites, and website builder APIs matter. They give distribution owners the opportunity to:

  • Brand the website experience as their own
  • Decide on the underlying technology (e.g., AI-native WordPress)
  • Bundle sites with hosting, marketing, or other services
  • Keep the recurring revenue and data inside their ecosystem

In other words, as AI pushes the web toward instant presence, distribution owners who embed website creation into their existing flows become the gatekeepers of which tools, stacks, and platforms win.

How To Connect WordPress Development, SEO & Vibe Coding

For most distribution owners, WordPress is still the safest base to standardize on. It powers a huge share of the web, has a deep plugin and WooCommerce ecosystem, and a large talent pool, which makes it easier to run thousands of sites without being tied to a single vendor. Its open-source nature also allows full rebranding and custom flows, exactly what white-label providers need, while automated provisioning, multisite, and APIs make it a natural infrastructure layer for branded site creation at scale. The missing piece has been a truly AI-native, generation-first builder. The latest AI-powered WordPress tools are closing that gap and expanding what distribution owners can offer out of the box.

Use AI-Native WordPress & White Label Embeddable Solutions

Most of the visible WordPress innovation around AI and websites has happened in standalone AI builders or coding assistants, relying on scattered plugins and lightweight helpers. The CMS is solid, but the first version of a site is still mostly assembled by hand.

AI-native WordPress builders move AI into the core flow: from intent straight to a structured, production-ready WordPress site in one step. In 10Web’s case, Vibe for WordPress is the first to bring Vibe Coding to the market with a React front end and deep integrations with WordPress. As opposed to previous versions of the builder or other website builders working off of generic templates and content, Vibe for WordPress allows the customer to have unlimited freedom during and after website generation via chat based AI and using natural language.

For distribution owners, AI only matters if it is packaged in a way they can sell, support, and scale. At its core, the 10Web’s White Label solution is a fully white-labeled AI website builder and hosting environment that partners brand as their own, spanning the dashboard, onboarding flows, and even the WordPress admin experience.

Instead of sending customers to a third-party tool, partners work in a multi-tenant platform where they can:

  • Brand the entire experience (logo, colors, custom domain).
  • Provision and manage WordPress sites, hosting, and domains at scale.
  • Package plans, track usage and overages, and connect their own billing and SSO.

In practice, a telco, registrar, or SaaS platform can offer AI-built WordPress websites under its own brand without building an editor, a hosting stack, or a management console from scratch.

APIs and White-Label: Quickly Code New Sites Or Allow Your Clients To Feel In Control

There is one fine nuance, yet so important. Speed alone isn’t a deciding factor on who wins the next wave of web creation. Teams that can wire that speed directly into their distribution channels and workflows will be the first to the finish line.

The White label platforms and APIs are two sides of the same strategy. The reseller suite gives partners a turnkey, branded control center; the API lets them take the same capabilities and thread them through domain purchase flows, SaaS onboarding, or MSP client portals.

From there, partners can:

  • Generate sites and WooCommerce stores from prompts or templates.
  • Provision hosting, domains, and SSL, and manage backups and restore points via API.
  • Control plugins, templates, and vertical presets so each tenant or region gets a curated, governed stack.
  • Pull usage metrics, logs, and webhooks into their own analytics and billing layers.

For MSPs and agencies treating websites as a packaged, recurring service, see more predictable revenue and stickier client relationships. They bake “website included” into retainers, care plans, and bundles, using white-label reseller dashboard to keep everything under their own brand.

As for SaaS platform and vertical solutions, instead of just giving partners a branded dashboard, 10Web’s Website Builder API lets them embed AI-powered WordPress site creation and lifecycle management directly into their own products. At a high level, it’s a white-label AI builder you plug in via API so your users can create production-ready WordPress sites and stores in under a minute, without ever leaving your app.

In this model, when someone buys a domain, signs up for a SaaS tool, or comes under an MSP contract, they experience the AI website Builder as a built-in part of the product. And the distribution owner, armed with white-label and API tools, is the one who captures the recurring value of that relationship.

The Next Wave

WordPress remains the foundation distribution owners trust, the layer they know can scale from a single landing page to thousands of client sites. With 10Web’s  AI-native builder, reseller dashboard, and API, it isn’t playing catch-up anymore, but is quickly becoming the engine behind fast, governed, repeatable site creation.

For agencies, MSPs, cloud infrastructure providers, and SaaS platforms, that means they can sell websites as a packaged service. The winners of the next wave are the ones who wire AI-native, white-label WordPress into their distribution and turn “website included” into their default.

Unlock new revenue by selling AI. Websites, Hosting, AI Branding, AI Agents, SMB Tools, and your own services.


Image Credits

Featured Image: Image by 10Web. Used with permission.

How one controversial startup hopes to cool the planet

Stardust Solutions believes that it can solve climate change—for a price.

The Israel-based geoengineering startup has said it expects  nations will soon pay it more than a billion dollars a year to launch specially equipped aircraft into the stratosphere. Once they’ve reached the necessary altitude, those planes will disperse particles engineered to reflect away enough sunlight to cool down the planet, purportedly without causing environmental side effects. 

The proprietary (and still secret) particles could counteract all the greenhouse gases the world has emitted over the last 150 years, the company stated in a 2023 pitch deck it presented to venture capital firms. In fact, it’s the “only technologically feasible solution” to climate change, the company said.

The company disclosed it raised $60 million in funding in October, marking by far the largest known funding round to date for a startup working on solar geoengineering.

Stardust is, in a sense, the embodiment of Silicon Valley’s simmering frustration with the pace of academic research on the technology. It’s a multimillion-dollar bet that a startup mindset can advance research and development that has crept along amid scientific caution and public queasiness.

But numerous researchers focused on solar geoengineering are deeply skeptical that Stardust will line up the government customers it would need to carry out a global deployment as early as 2035, the plan described in its earlier investor materials—and aghast at the suggestion that it ever expected to move that fast. They’re also highly critical of the idea that a company would take on the high-stakes task of setting the global temperature, rather than leaving it to publicly funded research programs.

“They’ve ignored every recommendation from everyone and think they can turn a profit in this field,” says Douglas MacMartin, an associate professor at Cornell University who studies solar geoengineering. “I think it’s going to backfire. Their investors are going to be dumping their money down the drain, and it will set back the field.”

The company has finally emerged from stealth mode after completing its funding round, and its CEO, Yanai Yedvab, agreed to conduct one of the company’s first extensive interviews with MIT Technology Review for this story.

Yedvab walked back those ambitious projections a little, stressing that the actual timing of any stratospheric experiments, demonstrations, or deployments will be determined by when governments decide it’s appropriate to carry them out. Stardust has stated clearly that it will move ahead with solar geoengineering only if nations pay it to proceed, and only once there are established rules and bodies guiding the use of the technology.

That decision, he says, will likely be dictated by how bad climate change becomes in the coming years.

“It could be a situation where we are at the place we are now, which is definitely not great,” he says. “But it could be much worse. We’re saying we’d better be ready.”

“It’s not for us to decide, and I’ll say humbly, it’s not for these researchers to decide,” he adds. “It’s the sense of urgency that will dictate how this will evolve.”

The building blocks

No one is questioning the scientific credentials of Stardust. The company was founded in 2023 by a trio of prominent researchers, including Yedvab, who served as deputy chief scientist at the Israeli Atomic Energy Commission. The company’s lead scientist, Eli Waxman, is the head of the department of particle physics and astrophysics at the Weizmann Institute of Science. Amyad Spector, the chief product officer, was previously a nuclear physicist at Israel’s secretive Negev Nuclear Research Center.

Stardust CEO Yanai Yedvab (right) and Chief Product Officer Amyad Spector (left) at the company’s facility in Israel.
ROBY YAHAV, STARDUST

Stardust says it employs 25 scientists, engineers, and academics. The company is based in Ness Ziona, Israel, and plans to open a US headquarters soon. 

Yedvab says the motivation for starting Stardust was simply to help develop an effective means of addressing climate change. 

“Maybe something in our experience, in the tool set that we bring, can help us in contributing to solving one of the greatest problems humanity faces,” he says.

Lowercarbon Capital, the climate-tech-focused investment firm  cofounded by the prominent tech investor Chris Sacca, led the $60 million investment round. Future Positive, Future Ventures, and Never Lift Ventures, among others, participated as well.

AWZ Ventures, a firm focused on security and intelligence technologies, co-led the company’s earlier seed round, which totaled $15 million.

Yedvab says the company will use that money to advance research, development, and testing for the three components of its system, which are also described in the pitch deck: safe particles that could be affordably manufactured; aircraft dispersion systems; and a means of tracking particles and monitoring their effects.

“Essentially, the idea is to develop all these building blocks and to upgrade them to a level that will allow us to give governments the tool set and all the required information to make decisions about whether and how to deploy this solution,” he says. 

The company is, in many ways, the opposite of Make Sunsets, the first company that came along offering to send particles into the stratosphere—for a fee—by pumping sulfur dioxide into weather balloons and hand-releasing them into the sky. Many researchers viewed it as a provocative, unscientific, and irresponsible exercise in attention-gathering. 

But Stardust is serious, and now it’s raised serious money from serious people—all of which raises the stakes for the solar geoengineering field and, some fear, increases the odds that the world will eventually put the technology to use.

“That marks a turning point in that these types of actors are not only possible, but are real,” says Shuchi Talati, executive director of the Alliance for Just Deliberation on Solar Geoengineering, a nonprofit that strives to ensure that developing nations are included in the global debate over such climate interventions. “We’re in a more dangerous era now.”

Many scientists studying solar geoengineering argue strongly that universities, governments, and transparent nonprofits should lead the work in the field, given the potential dangers and deep public concerns surrounding a tool with the power to alter the climate of the planet. 

It’s essential to carry out the research with appropriate oversight, explore the potential downsides of these approaches, and publicly publish the results “to ensure there’s no bias in the findings and no ulterior motives in pushing one way or another on deployment or not,” MacMartin says. “[It] shouldn’t be foisted upon people without proper and adequate information.”

He criticized, for instance, the company’s claims to have developed what he described as their “magic aerosol particle,” arguing that the assertion that it is perfectly safe and inert can’t be trusted without published findings. Other scientists have also disputed those scientific claims.

Plenty of other academics say solar geoengineering shouldn’t be studied at all, fearing that merely investigating it starts the world down a slippery slope toward its use and diminishes the pressures to cut greenhouse-gas emissions. In 2022, hundreds of them signed an open letter calling for a global ban on the development and use of the technology, adding the concern that there is no conceivable way for the world’s nations to pull together to establish rules or make collective decisions ensuring that it would be used in “a fair, inclusive, and effective manner.”

“Solar geoengineering is not necessary,” the authors wrote. “Neither is it desirable, ethical, or politically governable in the current context.”

The for-profit decision 

Stardust says it’s important to pursue the possibility of solar geoengineering because the dangers of climate change are accelerating faster than the world’s ability to respond to it, requiring a new “class of solution … that buys us time and protects us from overheating.”

Yedvab says he and his colleagues thought hard about the right structure for the organization, finally deciding that for-profits working in parallel with academic researchers have delivered “most of the groundbreaking technologies” in recent decades. He cited advances in genome sequencing, space exploration, and drug development, as well as the restoration of the ozone layer.

He added that a for-profit structure was also required to raise funds and attract the necessary talent.

“There is no way we could, unfortunately, raise even a small portion of this amount by philanthropic resources or grants these days,” he says.

He adds that while academics have conducted lots of basic science in solar geoengineering, they’ve done very little in terms of building the technological capacities. Their geoengineering research is also primarily focused on the potential use of sulfur dioxide, because it is known to help reduce global temperatures after volcanic eruptions blast massive amounts of it into the stratospheric. But it has well-documented downsides as well, including harm to the protective ozone layer.

“It seems natural that we need better options, and this is why we started Stardust: to develop this safe, practical, and responsible solution,” the company said in a follow-up email. “Eventually, policymakers will need to evaluate and compare these options, and we’re confident that our option will be superior over sulfuric acid primarily in terms of safety and practicability.”

Public trust can be won not by excluding private companies, but by setting up regulations and organizations to oversee this space, much as the US Food and Drug Administration does for pharmaceuticals, Yedvab says.

“There is no way this field could move forward if you don’t have this governance framework, if you don’t have external validation, if you don’t have clear regulation,” he says.

Meanwhile, the company says it intends to operate transparently, pledging to publish its findings whether they’re favorable or not.

That will include finally revealing details about the particles it has developed, Yedvab says. 

Early next year, the company and its collaborators will begin publishing data or evidence “substantiating all the claims and disclosing all the information,” he says, “so that everyone in the scientific community can actually check whether we checked all these boxes.”

In the follow-up email, the company acknowledged that solar geoengineering isn’t a “silver bullet” but said it is “the only tool that will enable us to cool the planet in the short term, as part of a larger arsenal of technologies.”

“The only way governments could be in a position to consider [solar geoengineering] is if the work has been done to research, de-risk, and engineer safe and responsible solutions—which is what we see as our role,” the company added later. “We are hopeful that research will continue not just from us, but also from academic institutions, nonprofits, and other responsible companies that may emerge in the future.”

Ambitious projections

Stardust’s earlier pitch deck stated that the company expected to conduct its first “stratospheric aerial experiments” last year, though those did not move ahead (more on that in a moment).

On another slide, the company said it expected to carry out a “large-scale demonstration” around 2030 and proceed to a “global full-scale deployment” by about 2035. It said it expected to bring in roughly $200 million and $1.5 billion in annual revenue by those periods, respectively.

Every researcher interviewed for this story was adamant that such a deployment should not happen so quickly.

Given the global but uneven and unpredictable impacts of solar geoengineering, any decision to use the technology should be reached through an inclusive, global agreement, not through the unilateral decisions of individual nations, Talati argues. 

“We won’t have any sort of international agreement by that point given where we are right now,” she says.

A global agreement, to be clear, is a big step beyond setting up rules and oversight bodies—and some believe that such an agreement on a technology so divisive could never be achieved.

There’s also still a vast amount of research that must be done to better understand the negative side effects of solar geoengineering generally and any ecological impacts of Stardust’s materials specifically, adds Holly Buck, an associate professor at the University of Buffalo and author of After Geoengineering.

“It is irresponsible to talk about deploying stratospheric aerosol injection without fundamental research about its impacts,” Buck wrote in an email.

She says the timelines are also “unrealistic” because there are profound public concerns about the technology. Her polling work found that a significant fraction of the US public opposes even research (though polling varies widely). 

Meanwhile, most academic efforts to move ahead with even small-scale outdoor experiments have sparked fierce backlash. That includes the years-long effort by researchers then at Harvard to carry out a basic equipment test for their so-called ScopeX experiment. The high-altitude balloon would have launched from a flight center in Sweden, but the test was ultimately scratched amid objections from environmentalists and Indigenous groups. 

Given this baseline of public distrust, Stardust’s for-profit proposals only threaten to further inflame public fears, Buck says.

“I find the whole proposal incredibly socially naive,” she says. “We actually could use serious research in this field, but proposals like this diminish the chances of that happening.”

Those public fears, which cross the political divide, also mean politicians will see little to no political upside to paying Stardust to move ahead, MacMartin says.

“If you don’t have the constituency for research, it seems implausible to me that you’d turn around and give money to an Israeli company to deploy it,” he says.

An added risk is that if one nation or a small coalition forges ahead without broader agreement, it could provoke geopolitical conflicts. 

“What if Russia wants it a couple of degrees warmer, and India a couple of degrees cooler?” asked Alan Robock, a professor at Rutgers University, in the Bulletin of the Atomic Scientists in 2008. “Should global climate be reset to preindustrial temperature or kept constant at today’s reading? Would it be possible to tailor the climate of each region of the planet independently without affecting the others? If we proceed with geoengineering, will we provoke future climate wars?”

Revised plans

Yedvab says the pitch deck reflected Stardust’s strategy at a “very early stage in our work,” adding that their thinking has “evolved,” partly in response to consultations with experts in the field.

He says that the company will have the technological capacity to move ahead with demonstrations and deployments on the timelines it laid out but adds, “That’s a necessary but not sufficient condition.”

“Governments will need to decide where they want to take it, if at all,” he says. “It could be a case that they will say ‘We want to move forward.’ It could be a case that they will say ‘We want to wait a few years.’”

“It’s for them to make these decisions,” he says.

Yedvab acknowledges that the company has conducted flights in the lower atmosphere to test its monitoring system, using white smoke as a simulant for its particles, as the Wall Street Journal reported last year. It’s also done indoor tests of the dispersion system and its particles in a wind tunnel set up within its facility.

But in response to criticisms like the ones above, Yedvab says the company hasn’t conducted outdoor particle experiments and won’t move forward with them until it has approval from governments. 

“Eventually, there will be a need to conduct outdoor testing,” he says. “There is no way you can validate any solution without outdoor testing.” But such testing of sunlight reflection technology, he says, “should be done only working together with government and under these supervisions.”

Generating returns  

Stardust may be willing to wait for governments to be ready to deploy its system, but there’s no guarantee that its investors will have the same patience. In accepting tens of millions in venture capital, Stardust may now face financial pressures that could “drive the timelines,” says Gernot Wagner, a climate economist at Columbia University. 

And that raises a different set of concerns.

Obliged to deliver returns, the company might feel it must strive to convince government leaders that they should pay for its services, Talati says. 

“The whole point of having companies and investors is you want your thing to be used,” she says. “There’s a massive incentive to lobby countries to use it, and that’s the whole danger of having for-profit companies here.”

She argues those financial incentives threaten to accelerate the use of solar geoengineering ahead of broader international agreements and elevate business interests above the broader public good.

Stardust has “quietly begun lobbying on Capitol Hill” and has hired the law firm Holland & Knight, according to Politico.

It has also worked with Red Duke Strategies, a consulting firm based in McLean, Virginia, to develop “strategic relationships and communications that promote understanding and enable scientific testing,” according to a case study on the company’s  website. 

“The company needed to secure both buy-in and support from the United States government and other influential stakeholders to move forward,” Red Duke states. “This effort demanded a well-connected and authoritative partner who could introduce Stardust to a group of experts able to research, validate, deploy, and regulate its SRM technology.”

Red Duke didn’t respond to an inquiry from MIT Technology Review. Stardust says its work with the consulting firm was not a government lobbying effort.

Yedvab acknowledges that the company is meeting with government leaders in the US, Europe, its own region, and the Global South. But he stresses that it’s not asking any country to contribute funding or to sign off on deployments at this stage. Instead, it’s making the case for nations to begin crafting policies to regulate solar geoengineering.

“When we speak to policymakers—and we speak to policymakers; we don’t hide it—essentially, what we tell them is ‘Listen, there is a solution,’” he says. “‘It’s not decades away—it’s a few years away. And it’s your role as policymakers to set the rules of this field.’”

“Any solution needs checks and balances,” he says. “This is how we see the checks and balances.”

He says the best-case scenario is still a rollout of clean energy technologies that accelerates rapidly enough to drive down emissions and curb climate change.

“We are perfectly fine with building an option that will sit on the shelf,” he says. “We’ll go and do something else. We have a great team and are confident that we can find also other problems to work with.”

He says the company’s investors are aware of and comfortable with that possibility, supportive of the principles that will guide Stardust’s work, and willing to wait for regulations and government contracts.

Lowercarbon Capital didn’t respond to an inquiry from MIT Technology Review.

‘Sentiment of hope’

Others have certainly imagined the alternative scenario Yedvab raises: that nations will increasingly support the idea of geoengineering in the face of mounting climate catastrophes. 

In Kim Stanley Robinson’s 2020 novel, The Ministry for the Future, India unilaterally forges ahead with solar geoengineering following a heat wave that kills millions of people. 

Wagner sketched a variation on that scenario in his 2021 book, Geoengineering: The Gamble, speculating that a small coalition of nations might kick-start a rapid research and deployment program as an emergency response to escalating humanitarian crises. In his version, the Philippines offers to serve as the launch site after a series of super-cyclones batter the island nation, forcing millions from their homes. 

It’s impossible to know today how the world will react if one nation or a few go it alone, or whether nations could come to agreement on where the global temperature should be set. 

But the lure of solar geoengineering could become increasingly enticing as more and more nations endure mass suffering, starvation, displacement, and death.

“We understand that probably it will not be perfect,” Yedvab says. “We understand all the obstacles, but there is this sentiment of hope, or cautious hope, that we have a way out of this dark corridor we are currently in.”

“I think that this sentiment of hope is something that gives us a lot of energy to move on forward,” he adds.

Securing VMware workloads in regulated industries

At a regional hospital, a cardiac patient’s lab results sit behind layers of encryption, accessible to his surgeon but shielded from those without strictly need-to-know status. Across the street at a credit union, a small business owner anxiously awaits the all-clear for a wire transfer, unaware that fraud detection systems have flagged it for further review.

Such scenarios illustrate how companies in regulated industries juggle competing directives: Move data and process transactions quickly enough to save lives and support livelihoods, but carefully enough to maintain ironclad security and satisfy regulatory scrutiny.

Organizations subject to such oversight walk a fine line every day. And recently, a number of curveballs have thrown off that hard-won equilibrium. Agencies are ramping up oversight thanks to escalating data privacy concerns; insurers are tightening underwriting and requiring controls like MFA and privileged-access governance as a condition of coverage. Meanwhile, the shifting VMware landscape has introduced more complexity for IT teams tasked with planning long-term infrastructure strategies. 

Download the full article

This content was produced by Insights, the custom content arm of MIT Technology Review. It was not written by MIT Technology Review’s editorial staff.

This content was researched, designed, and written by human writers, editors, analysts, and illustrators. This includes the writing of surveys and collection of data for surveys. AI tools that may have been used were limited to secondary production processes that passed thorough human review.

The Download: a controversial proposal to solve climate change, and our future grids

This is today’s edition of The Download, our weekday newsletter that provides a daily dose of what’s going on in the world of technology.

How one controversial startup hopes to cool the planet

Stardust Solutions believes that it can solve climate change—for a price. 

The Israel-based geoengineering startup has said it expects nations will soon pay it more than a billion dollars a year to launch specially equipped aircraft into the stratosphere. Once they’ve reached the necessary altitude, those planes will disperse particles engineered to reflect away enough sunlight to cool down the planet, purportedly without causing environmental side effects. 

But numerous solar geoengineering researchers are skeptical that Stardust will line up the customers it needs to carry out a global deployment in the next decade. They’re also highly critical of the idea of a private company setting the global temperature for us. Read the full story.

—James Temple

MIT Technology Review Narrated: Is this the electric grid of the future?  

In Nebraska, a publicly owned utility company is tackling the challenges of delivering on reliability, affordability, and sustainability. It aims to reach net zero by 2040—here’s how it plans to get there.

This is our latest story to be turned into a MIT Technology Review Narrated podcast, which we’re publishing each week on Spotify and Apple Podcasts. Just navigate to MIT Technology Review Narrated on either platform, and follow us to get all our new content as it’s released.

The must-reads

I’ve combed the internet to find you today’s most fun/important/scary/fascinating stories about technology.

1 Australia’s social media ban for teens has just come into force
The whole world will be watching to see what happens next. (The Guardian)
Opinions about the law are sharply divided among Australians. (BBC)
Plenty of teens hate it, naturally. (WP $)
A third of US teens are on their phones “almost constantly.” (NYT $)

2 This has been the second-hottest year since records began
Mean temperatures approached 1.5°C above the preindustrial average. (New Scientist $)
+ Meanwhile world leaders at this year’s UN climate talks couldn’t even agree to use the phrase ‘fossil fuels’ in the final draft. (MIT Technology Review)

3 OpenAI is in trouble
It’s rapidly losing its technological edge to competitors like Google and Anthropic. (The Atlantic $)
+ Silicon Valley is working harder than ever to sell AI to us. (Wired $)
There’s a new industry-wide push to agree shared standards for AI agents. (TechCrunch)
No one can explain how AI really works—not even the experts attending AI’s biggest research gathering. (NBC)

4 MAGA influencers want Trump to kill the Netflix/Warner Bros deal
They argue Netflix is simply too woke (after all, it employs the Obamas.) (WP $)

5 AI slop videos have taken over social media
It’s now almost impossible to tell if what you’re seeing is real or not. (NYT $)

6 Trump’s system to weed out noncitizen voters is flagging US citizens 
Once alerted, people have 30 days to provide proof of citizenship before they lose their ability to vote. (NPR)
The US is planning to ask visitors to disclose five years of social media history. (WP $)
How open source voting machines could boost trust in US elections. (MIT Technology Review)

7 Virtual power plants are having a moment
Here’s why they’re poised to play a significant role in meeting energy demand over the next decade. (IEEE Spectrum)
How virtual power plants are shaping tomorrow’s energy system. (MIT Technology Review)

8 New devices are about to get (even) more expensive
You can thank AI for pushing up the price of RAM for the rest of us. (The Verge)

9 People hated the McDonald’s AI ad so much the company pulled it 
How are giant corporations still falling into this exact trap every holiday season? (Forbes)  

10 Why is ice slippery? There’s a new hypothesis 🧊
You might think you know. But it’s still fiercely debated among ice researchers! (Quanta $)

Quote of the day

“We’re pleased to be the first, we’re proud to be the first, and we stand ready to help any other jurisdiction who seeks to do these things.”

—Australia’s communications minister Anika Wells tells the BBC how she feels about her government’s decision to ban social media for under-16s. 

One more thing

MICHAEL BYERS

The entrepreneur dreaming of a factory of unlimited organs

At any given time, the US transplant waiting list is about 100,000 people long. Thousands die waiting, and many more never make the list to begin with. Entrepreneur Martine Rothblatt wants to address this by growing organs compatible with human bodies in genetically modified pigs.

In recent years, US doctors have attempted seven pig-to-human transplants, the most dramatic of which was a case where a 57-year-old man with heart failure lived two months with a pig heart supplied by Rothblatt’s company. 

The experiment demonstrated the first life-sustaining pig-to-human organ transplant—and paved the way towards an organized clinical trial to prove they save lives consistently. Read the full story.

—Antonio Regalado

We can still have nice things

A place for comfort, fun and distraction to brighten up your day. (Got any ideas? Drop me a line or skeet ’em at me.)

+ I want to eat all of these things, starting with the hot chocolate cookies. 
+ Even one minute is enough time to enjoy some of the benefits of mindfulness.
+ The Geminid meteor shower will reach its peak this weekend. Here’s how to see it
+ I really enjoy Leah Gardner’s still life paintings.