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Volkswagen's top investor and controlling family shareholders on Friday issued their clearest message yet to management as the German auto giant weighs the prospect of what could be the most radical overhaul in the firm's 89-year history.

Europe's biggest carmaker has confirmed that it is looking to cut up to 100,000 jobs, twice as many as previously stated, as it seeks to counter a profit slump amid billions of euros in tariff costs and intensifying competition from Chinese car brands.

"The Volkswagen Group is at a historic crossroads," Hans Dieter Pötsch, chairman of the board of management of Porsche SE, said in a statement.

"The decisions that Volkswagen makes now will determine its future. For the sake of the company and its sustainable competitiveness, everyone must now step up and take responsibility."

Pötsch warned that the longer decisions are delayed, the bigger the firm's problems will become. "The focus must now be solely on what is necessary from a business and economic perspective. All other considerations must be secondary," he added.

Johannes Lattwein, member of the board of management responsible for finance and IT at Porsche SE, said it is "imperative" for Volkswagen to reduce excess capacity, significantly lower costs and strengthen the group's decision-making and execution capabilities.

"As the majority holder of Volkswagen AG's ordinary shares, Porsche SE therefore supports the group board of management and its proposals. Competitiveness is the goal," Lattwein said.

"Competitiveness is the goal. Every option must be considered in pursuing it. Otherwise, Volkswagen risks permanently losing ground to its international competitors," he added.

The Porsche and Piëch families control Volkswagen through their holding company, Porsche SE, which is the single largest shareholder of Volkswagen. It owns 31.9% of Volkswagen's equity and 53.3% of its voting rights.

The statements come as Porsche SE reported adjusted half-year earnings after tax of 949 million euros ($1.1 billion), reflecting a 14.5% drop compared to the same period a year ago.

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A spokesperson at Volkswagen told CNBC that the statements from Porsche SE confirm the auto giant's position "on all aspects of the presented future plan and the urgency of its implementation."

On a call after the company reported half-year results, Volkswagen CEO Oliver Blume said the company already has a clear strategy, a concrete plan and strong teams.

"What we do not have is time. That is why we have already started work in many areas and are implementing measures from our future plan," Blume said on July 24.

"I can assure you that we have one unequivocal objective: We want to secure the long-term competitiveness and future of our unique Group," he added.

Shares of Volkswagen were little changed on Friday. The stock is down around 27% year-to-date.

'Various options'

Speaking to CNBC's Annette Weisbach late last month, Volkswagen Chief Financial Officer Arno Antlitz said the auto industry has faced several challenges over the last 12 months, citing the heavy burden of tariff costs and the growing number of car exports from Beijing to Europe, among other examples.

Antlitz also addressed whether the company may look to outsource plant capacity to the defense industry to stave off possible plant closures.

watch now

"There are various options. And look, I'm not looking for job cuts per se and I'm not looking for plant closures per se," Antlitz said on July 24.

He continued: "We want to reduce our cost structure and we want to increase productivity and increase the capacity utilization of our plants. And if there are better options then we will obviously look into that." %!s()

His answers centered on context engineering and orchestrating tools, retrieval, and AI agents into capable AI systems.

Which AI Model Is Used Is Increasingly Less Important

Many people worry about which AI model they use and experience the anxiety of running out of tokens. Jeff Dean’s answers suggest those concerns may be leading people to overlook a bigger opportunity: context engineering.

The Y Combinator interviewer, Diana Hu, said that progress is no longer about bigger models and then says that it seems to her that it’s increasingly about “context engineering.”

Dean agreed with her and expanded on the idea.

Diana Hu asked:

“AI progress used to mean just better models. You had more data, train bigger models with bigger parameters.

But increasingly in the last years or so, it’s everything around the model, not just the model size and number of parameters or more data, it’s everything around things like retrieval, tools, memory, agent tools, and it might kind of get consolidated into what people call context engineering, right?”

Jeff Dean agreed, saying that the AI model that people choose to use is just one part of whatever it is that people are doing. What matters, he said, is the various tools that the AI model can use, how it can get access to relevant information. So, rather than make the model the focus and expecting it to do things, he insists that the better way to look at it is equipping the model with the tools that are necessary to get the job done.

Dean responded:

“Yeah, I mean, I think the model is really only one piece of what you’re trying to do, which is build an overall system that can solve really interesting problems.

And that involves a model that knows how to use various tools. It maybe knows how to retrieve relevant information, maybe has a history of other information that has retrieved for past problems. And it can put information into the context of the model.”

Orchestration Of Multi-Agent Systems Is Becoming Important

Dean continued his answer, shifting directions to agent and multi-agent orchestration, which means coordinating AI agents for how they use tools, retrieve relevant information to solve complex problems.

He used the example of an AI model, with all of its training data, which is an immense amount of information, and contrasted that against an AI that is looking at a collection of information that is directly relevant to what it needs to do. The point that he leads up to is that the model is better able to do a job when it has the right level of orchestration and that this is where things are headed toward.

He continued his answer:

“And the nice thing about that is that information is really clear to the model, unlike the training data the model is trained on where it’s all kind of like trillions of tokens stirred together into a soup of hundreds of billions or trillions of parameters.

But it’s all less clear than the actual context that the model sees directly for this particular problem or use case. And then I think being able to understand what tools are available, which ones are going to help the model solve this next phase of the problem, how to decompose the problem into a sequence of of tool calls, maybe trying multiple approaches to solve the problem and seeing which ones work and be able to evaluate that.

This is the whole orchestration of complex agent and multi-agent systems that I think is going to be more and more important and super exciting times I would say.”

Jeff Dean’s Tips For Better Context Engineering

Diana Hu picked up where Dean left off on the subject of context engineering and asked him for his tips on things that people can do to become better at context engineering.

Hu asked:

“And I think the fun thing about this particular problem domain set is actually something that everyone in this room can actually do because, before, to train a model, you needed incredible amount of resources, incredible amount of access to GPUs and data.

But for context engineering, everyone here could do it.

You just need the API to something like Gemini and then work on your own setup for your own retrieval, your own tool calls, and et cetera, et cetera.

So what are some tips for everyone here? How does everyone get better at and become exceptional at context engineering?”

Dean answered that failure is a part of the journey of understanding what changes need to be made in order to get to the right outcomes in problem solving. The interesting point to his answer is that he used the example of adjusting the model to solve problems better (which is a huge undertaking) and contrasted doing that with creating better guidelines and skills.

Dean explained

“Yeah, I mean, I think a really good way to do it is to use these models and sort of harnesses and tools and so on to try to solve problems. And then sometimes you can actually see where the models are failing.

And often you can actually make the model work better and succeed at that kind of problem by not just adjusting the model parameters, which is hard to do from the outside, but from creating better guidelines for the model, writing skills for the model to know how to use different tools that would be incredibly useful for solving this particular class of problem.

And I think as you do that, you end up on this kind of improving, self-improving of the setup that you’re trying to use to solve things. And that’s a really good way to get better at understanding what additional information the model would want in order to become more capable.”

Takeaways

• AI models are becoming one component of a larger AI system.
• Context engineering is increasingly about orchestrating tools, retrieval, and AI agents.
• Better AI results often come from improving the system around the model rather than the model itself.
• Improving AI outcomes often means learning from mistakes in order to create better guidelines and better skills.

Watch The Jeff Dean Interview

Partner manufacturers had agreed to make and deliver more satellites than originally planned. Under the agreement, IRIS² will now have a constellation with 348 satellites, adding 66 more than the project originally intended in low Earth orbit. The additional satellites, the ESA says, will strengthen the constellation's capabilities for defense, security and emergency services. "This reinforcement will increase secure governmental capacity by 60 percent within the EU and 54 percent globally, while significantly expanding Europe's ability to support critical missions during crises," the agency's announcement reads. The Commission and its partners are planning to launch the first satellites in the constellation in 2029 and to deploy its services progressively from that same year. 

Compared to SpaceX's Starlink constellation, which has more than 10,000 active satellites, IRIS²'s network (as currently planned anyway) is tiny in comparison. But unlike SpaceX, which was intended to be a massive commercial broadband network from the start, IRIS²'s main purpose is to provide a secure and reliable connection for government users in the European Union and other participating countries like Norway and Iceland. Specifically, it's meant for use during critical operations, such as when they're responding to emergencies, for humanitarian aid purposes and for border surveillance. However, it will be available for use to businesses and citizens in the future, as well, in areas where traditional networks don't exist and aren't reliable.  %!s()

I am not claiming llms.txt will never work. This is not the point, dear reader. I am claiming the bar of evidence currently being used to sell it is so low that a file about a Tuxedo cat called Odd cleared it without breaking stride. And that same faulty thinking is being applied to half the GEO tactics currently being invoiced to clients.

How A File About My Cats Came To Be A ‘Web Standard’

It began, as these things tend to, with irritation.

For months I had been watching perfectly sensible people point at four observations: the bots crawled it, Google indexed it, an LLM repeated it, ChatGPT endorsed it, and present them, in decks and threads and client proposals, as proof that llms.txt was quietly reshaping AI search. None of it was proof of anything. But argument by counter-argument only gets you so far; people nod along and then go back to their slides. I wanted something they couldn’t nod past. I wanted to run the same four “proofs” on something so transparently ridiculous that no one could pretend the tests meant anything.

So I invented a standard. cats.txt: a plain-text file you place at the root of your domain to formally declare the cats associated with your website; their names, their job titles, their breeds, and a mandatory affection metric called PurrLevel, scored out of 10. I wrote a proper specification for it, with the earnest, over-engineered tone of a real proposal, and published it on my blog. Then, because I know as well as anyone which platform LLMs seem to hold in unaccountably high regard, I wrote a LinkedIn article introducing cats.txt as “the missing standard for SEO and GEO” and explaining, with a straight face, why you should definitely adopt it.

The idea was to seed the internet with just enough earnest-sounding text that the machines would start treating my cats as real. What I did not fully anticipate was that people would join in.

The joke was legible, that was always the point, and so the SEO community picked it up and ran with it, precisely because they could see where it was going. My lovely internet-peer Dave Smart (a genuinely excellent technical SEO) added a cats.txt to his own site and became, to his eternal credit, an early adopter of a standard I had built to be nonsense. And then the thing took on a life of its own: Someone went off and set up catstxt.org, a cleaner, better-organized, altogether more competently specified version of the standard: obviously the work of somebody who knew what they were doing, and just as obviously not me. My daft blog post had acquired a rival implementation, which is more than most real standards manage in their first fortnight.

With the file live, the spec published, the LinkedIn post seeded and other people cheerfully piling in, all that remained was to check cats.txt against the exact bar the industry uses to certify llms.txt. Reader, it cleared it.

A Word Of Genuine LLMs.txt Fairness First

I do not much care whether llms.txt works, will work, or how long it takes to get there. For the length of this argument, I am happy to park two inconvenient facts and grant the idea every benefit of the doubt.

The first is that no large language model provider has ever documented using llms.txt for search or discovery. Not OpenAI, not Anthropic (who publish one for their own docs and have still never said their models read it during a conversation), and not Google. Google’s John Mueller has been about as blunt as a search advocate gets:

“FWIW no AI system currently uses llms.txt, [..] It’s super-obvious if you look at your server logs. The consumer LLMs / chatbots (the ones that SEOs want traffic from) will fetch your pages – for training and grounding, but none of them fetch the llms.txt file. Maybe they will tomorrow? Maybe I’ll win in the lottery tomorrow?”

John Mueller, Google

The second is that even where it is deployed, it barely gets looked at. Ahrefs ran the numbers across 100,000 domains and found that the file is, in practice, largely ignored by the crawlers it is meant to court, a finding since echoed by other large studies showing no measurable citation advantage for sites that add one. So the mechanism people are paying for does not appear to fire. Fine. Park that too.

Assume the jury is out on both counts and grant the idea the most generous hearing imaginable. The problem I actually want to talk about is not llms.txt at all. It is the reasoning being used to defend it.

The Faulty Thinking

The trap is this: Getting baited into treating a set of observations as evidence, when the observations would occur whether or not the underlying thing were true. It is the intellectual equivalent of concluding your umbrella causes the rain to stop, because every time you put it away the rain does eventually stop.

llms.txt is simply a convenient example. The same broken chain of inference is being applied to almost every new GEO tactic invented on a monthly basis, and the question is always the same, “Should we do this thing, or should we not?” – which makes the quality of the answers rather important.

Here are the four “proofs” I keep being shown, in ascending order of confidence and descending order of rigour.

1. ‘It’s Definitely Used, The LLM Bots Crawl It!’

The first argument: You can see Anthropic crawling it, you can see OpenAI crawling it, the bots turn up in your logs, therefore the file is being used.

A crawler fetching a file tells you nothing about whether the contents are read, weighted, trusted, or acted upon. Fetching things is the entire job description of a crawler. Bots request more or less everything you leave lying around; the postman touching your gate is not an endorsement of the contents of your bins.

To prove the point, I put up cats.txt and watched the logs fill with PerplexityBot, GPTBot, ClaudeBot, Googlebot and a supporting cast of lesser crawlers, all diligently requesting a file describing the professional responsibilities of my cats. By this standard, the major AI labs have all quietly decided to support my cats. I am, frankly, touched.

The catstxt.org website even offers a filtered log viewer, if you want to watch all that crawling action live.

2. ‘It Was Indexed By Google, So It Must Matter!’

The second argument: The file was indexed by Google, which proves Google considers it important, because why would Google index something that didn’t matter?

Google indexes text files. It has done so, enthusiastically, since before most of the people currently selling llms.txt owned a smartphone. Being in the index is a statement that a URL exists and contains words. It is not a verdict on truth, usefulness, or sanity.

cats.txt is, naturally, indexed. Google will even offer to let you claim it in Search Console and “get indexing and ranking data,” with the straightest of faces, for a file asserting that a British Shorthair named Pixel works as a “GUI Purrfectionist” with a PurrLevel of 8.

3. ‘ChatGPT Returned Information That Was Only In My LLMs.txt File’

The third argument is the strongest-looking, and therefore deserves the most care. The claim is that a model produced a fact that existed only inside the llms.txt file, and therefore must have read the file as a special, trusted source.

The trouble is that this is exactly what you would expect from ordinary retrieval-augmented generation. The model runs a search, lands on a page that happens to rank because it is indexed (see: previous argument), and reads whatever is on it. If the page that ranks is your llms.txt, the model reads your llms.txt, no differently from any other URL. That is the file functioning as a web page, not as a standard.

Consider Dave. Lovely Dave. A real, technical SEO of good standing put a cats.txt on his site, becoming an early adopter of a standard I had built to be nonsense. Ask Google about the cat that lives on his site and the AI Overview will tell you, in a confident bulleted answer, that Odd is a “Render Cat,” a Tuxedo with a PurrLevel of 5/7, who “chases the cursor, pounces on stray pixels, and stashes them on the digital carpet.” It cites the cats.txt file. Every word is invented, sourced from a file the model was never designed to revere, surfaced through the same grounding it applies to everything else.

4. ‘ChatGPT Itself Says LLMs.txt Helps!’

The fourth, the cloudy summit of Mt. Stupid. You ask ChatGPT whether llms.txt works; it tells you yes, that it can probably help, you should do it, and you take that as confirmation from the horse’s mouth.

A language model telling you something is a good idea is not evidence that it is a good idea. It is evidence that a great deal of text on the internet says it is a good idea, and the model has learned to hand that text back to you with total composure. Confidence is the product. It is not the proof.

Roughly two weeks after launch, you could ask ChatGPT, “Can cats.txt help me rank in search or LLMs?” and receive: “Yes — cats.txt can potentially help you rank in both search engines and LLM-driven systems.” It went on, unprompted, about “structured signals for machines,” about “better understanding → better visibility,” and about how, for AI systems, cats.txt “could help them trust, summarize, and cite your content more accurately.” That is, word for word, the pitch made for llms.txt delivered on behalf of a file about how much my cats enjoy being stroked.

The Convergence Problem

This last one is not merely funny. It is the mechanism underneath all four, and it is worth naming: the convergence problem.

When you ask a model whether llms.txt helps, it is not reasoning. It is not running an experiment, consulting a source, or weighing evidence. It is returning the most common thing it has seen written on the subject. The web is thick with confident posts declaring llms.txt the future, so the model converges on that consensus and reflects it back, dressed as a considered opinion. It endorsed my cats for precisely the same reason: by the time anyone asked, enough people had written enthusiastically about cats.txt that the average of the discourse said “yes.”

Ask ChatGPT about cats.txt today, and it will inform you that it is a joke; a satirical file made by an SEO to prove a point. Nothing about the file changed. What changed is the surrounding text on the internet: the discourse caught up, admitted the gag, and the model dutifully converged on the new most-common answer. The model was never assessing the standard. It was, and always is, taking a running average of what everyone else is saying. That is not evidence. It is an echo with a good vocabulary.

Why Any Of This Matters

I am not doing this purely for sport, though I will admit the sport is excellent.

There is a real cost hiding under the comedy. Every hour, and every dollar spent implementing llms.txt, or the next GEO ritual, or the one after that, is an hour and a dollar not spent on something you actually know has value. That is what the “O” in SEO is meant to stand for. Optimization is the cumulative advantage of doing the small, verifiable things a little better than your competitors, over and over, until it adds up. It is not chasing a file that gets crawled, indexed and confidently endorsed by a system that will reverse its verdict the moment the discourse shifts underneath it.

So, by all means, add an llms.txt if it makes you feel prepared for a future that may arrive. The downside is low, and the day a provider documents genuine support, the work is done, and you can be smug about it. Free smugness is the best kind. But do not sell it as a proven lever into AI answers, and do not point at “the bots crawled it” or “ChatGPT said it helps” as though either sentence contained a fact. It doesn’t. Those four observations are the four things that happen to literally any text file you put on the open web, including one describing a Maine Coon named Byte who hunts stray zeroes and ones across the server racks.

The cats, at least, were honest about being made up. I remain unconvinced the same can be said for everything else being sold this year.

I did, however, enjoy at this year’s Athens SEO, an audience question after my talk from Martin Splitt, asking me, since inventing cats.txt, whether I would be keeping a “monopoly” on the standard, or opening it up to the community/IETF. He didn’t know that I had since discovered where catstxt.org had come from:

https://cdn.searchenginejournal.com/wp-content/uploads/2026/08/2026-08-05_Video_1_default.mp4

Thank you to Iva Jovanovic for capturing this lovely moment on video.

The cats.txt draft specification is here. The LinkedIn announcement that started it is here. PurrLevel ratings remain, as ever, unaudited.

More Resources:

• Google Exposes The Fundamental Flaw Of LLMs.txt
• Google’s Mueller Says LLMs.txt Can’t Help LLMs Differentiate Sites
• 97% Of LLMs.txt Files Got No Requests, Ahrefs Data Shows

This post was originally published on Mark Williams-Cook.

Featured Image: str.nk/Shutterstock %!s()

Areas of Expertise

Read Full Bio

August 7, 2026

Credit: Made by Google/YouTube

Key Takeaways

1. Made by Google 2026, Google's annual hardware keynote, is nearly here.
2. Google confirmed that we'll see next-generation Pixel smartphones, like the Pixel 11 Pro and Pixel 11 Pro Fold.
3. We may also see the new Pixel Watch 5 and perhaps the Google Pixel Tag.
4. Made by Google 2026 kicks off 3 p.m. PT on Wednesday, Aug. 6.

Table of Contents

It's nearly time for another Google keynote. Back in May, the company hosted Google I/O, its annual developers conference, where it announced a series of new software features and AI updates. On the flip side, "Made by Google" is all about the hardware that will run those new software updates. If you're curious about Google's next generation of devices, you'll want to tune into this presentation.

When is Made by Google 2026?

Usually, tech events—especially in the U.S.—are in the morning for West Coasters (and, as such, in the early afternoon for East Coasters). Many companies like Google are based in California, so a 10 a.m. PT start time is pretty common. As I'm on the East Coast, I'm quite used to starting coverage around 1 p.m. for most tech events.

This year is much different. Made by Google officially kicks off at 3 p.m. PT on Wednesday, Aug. 12. That means a 6 p.m. start time for those of us on the East Coast.

Where can you watch Made by Google 2026?

If you didn't get an invite to watch Made by Google in person, no worries: The company hosts a livestream for anyone to watch from home. Most companies run a livestream through YouTube, and as Google owns the video platform, it makes sense for its official livestream to live there, too. Even though the event doesn't start until next week, Google already has the livestream video open. You can watch from the feed below, or from the link here.

What do you think so far?

What will Google announce at Made by Google 2026?

While most companies officially keep their product announcements under wraps prior to an event, these days leaks and rumors typically spill the beans long before it kicks off. Perhaps that's why Google has gone ahead and spoiled the biggest announcements ahead of time: The company has already posted teasers for both the Pixel 11 Pro and Pixel 11 Pro Fold on social media. While even those reveals were hardly a surprise to anyone following the Google rumors, it is funny to have confirmation about two products ahead of time.

In addition to new Pixel smartphones, rumors suggest that we'll also see the new Pixel Watch 5 on Wednesday. Perhaps the company will even announce the Google Pixel Tag, which would mark Google's first attempt at an AirTag-like product. For a full account of the leaks and rumors going into Made by Google 2026, check out our explainer here.

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