Planet Mozilla

Agentic AI, according to Teaganne Finn and Amanda Downie at IBM, is some kind of an AI system that can go do a task for you.

Agentic AI describes AI systems that are designed to autonomously make decisions and act, with the ability to pursue complex goals with limited supervision…. This type of AI acts autonomously to achieve a goal by using technologies like natural language processing (NLPs), machine learning, reinforcement learning and knowledge representation…. Agentic AI can adapt to different or changing situations and has “agency” to make decisions based on context. It is used in various applications that can benefit from independent operation, such as robotics, complex analysis, and virtual assistants.

Sounds like it has a lot of potential. Lots of complex goals out there that it would be great to have the AI go try to achieve. Tag this one #hugeIfTrue. But how do you figure out if an agentic AI stack is ready to depend on? Some suggested uses for agentic AI are customer support, health care, and supply chain operations. Testing the AI on that stuff means either you have to make a simulated environment and the quality of the results are only as good as the simulation that the AI is working in, or you test on real work with all the risks that come with that.

What we need, in order to figure out if an agentic AI is ready to start working on high-stakes tasks, is some kind of a task that has

• realistic level of complexity: require a mix of information processing tasks such as completing forms and attaching appropriate files.

• low cost of failure: expect that early agentic AI will fail, and that should be fine. Don’t wait to test new systems that are promising but lack a record of success, or require someone to do constant manual checks of the AI’s work.

• easy to measure success: some tasks are harder to evaluate than to do. In order to make an agentic AI benchmark practical, we should be able to write a simple pass/fail script.

What kind of task is that? Most complicated tasks are too high-stakes to make a good benchmark, and most low-stakes tasks are too simple to make a good benchmark. And complex tasks tend to be hard to evaluate. Where should an agentic AI evaluation project look?

Good news. Such a task does exist. As the California Privacy Protection Agency decision in a recent online retailer case makes clear, the process for people to do a Right to Know (RtK) under California law is often over-complicated. Companies tend to add extra steps. All that malarkey is a big hassle from the human user point of view, but considered as an agentic AI test suite, it’s buried treasure. If you want to test agentic AI systems you don’t need to go write a bunch of weird corner-case web forms and bureaucratic processes—the test suite has been written for you by all the developers of weird and wonderful RtK mazes. And the results of an RtK are straightforward to check with a script. Yes, the data is largely crap but the test harness can do some pattern matching and spot that you’re getting about the crap that you expect.

The end result will probably be that companies make RtK processes more straightforward, to avoid enforcement actions and the costs of processing agentic AI traffic, but for now we have a free test suite for agentic AI. Let’s see how well the different agentic AI tools do. Watch this space.

Related

personal AI in the rugpull economy

Bonus links

German court rules cookie banners must offer “reject all” button by Skye Jacobs on TechSpot. The Hanover Administrative Court ruled that websites must display a clearly visible “reject all” button on cookie banners if they offer an “accept all” option.

Vermont state legislators: Big Tech doesn’t want you to know what’s happening to your kids online. We do. The truth is, we built this bill to withstand court scrutiny. We studied rulings in other states. We tailored the language. We removed any provision that regulated speech and focused only on product design and data processing. We listened to civil liberties groups, constitutional scholars and children’s advocates.

Priced Out: How Surveillance Pricing Leaves People with Disabilities At Risk by Ariana Aboulafia and Nina DiSalvo. Many people with disabilities regularly purchase items related to their disability, and do not have much choice in what they need, or when they need it. They may also be limited in where they can shop due to mobility limitations, difficulty finding accessible transportation, or other factors related to their disability. This means that both the privacy and consumer protection concerns that apply to all consumers in the context of surveillance pricing are particularly worrisome for disabled people.

Trying to avoid a heart attack, Instagram attacked me with ads by Bob Sullivan. Meta/Facebook/Instagram knew exactly where I was and what I was doing. But it didn’t just show me relevant ads. It knew I’d be vulnerable. It knew I might get bad news. And then it targeted me with crazy, untested products that would probably make me sicker. It’s vile and it needs to stop. This isn’t capitalism and it isn’t free speech. It’s using technology to attack people when they are nearly defenseless.

European Defense Tech brought engineers together for the first time for a hackathon for the front lines in Ukraine by Mykhailo Liuksikov. When you don’t understand the issues, when you don’t talk to people who have been on the front lines and understand the problems, that is, what is really important, that is why all these hackers need feedback on what they are doing. — Larysa Visengeriyeva, co-organizer of the European Defense Tech Hackathon.

previously: advertising personalization: good for you?

Some sources have suggested that privacy laws and regulations should be adapted to accommodate privacy-enhancing advertising technologies. Unfortunately, these technologies tend to solve specific problems for large companies while either not addressing the reasons we have privacy laws in the first place, or making the situation worse. It is important not to get real privacy, which policymakers should try to protect, mixed up with the mathematical properties of PETs. Some of the issues include:

discrimination Large ad platforms have a lot of different kinds of advertisers, from actual malware up to legit brands, and every level of honesty and quality in between. One persistent category of problem advertisers has been those who choose to target ads in a way that violates fair housing or equal opportunity employment laws. Big Tech companies have gotten a lot of grief over the years for enabling advertisers to target ads based on membership in a legally protected group. PETs address Big Tech’s problem by processing information in a way that hides discrimination, but they would make the problem worse for everyone else by making it harder for NGOs, academics, and regulators to detect and report on.

Right to Know and disclosing inferences: In California and some other jurisdictions, the right to know includes internally generated “inferences”. For some kinds of inferences, the person being inferred about has a strong interest in learning what the inference is. A good current example is when a platform identifies you as a potential future problem gambler. PETs currently do not have a good inference RtK story, which means (1) individuals don’t get enough info to make a sensible choice about advertising personalization settings and (2) research projects that compare RtK data from many volunteers would have less ability to detect problems.

fraud: A lot of decision-makers in advertising’s buy side treat adfraud as a victimless crime, because the victims are legit publisher sites. Adfraud steals attribution from legit sites and pushes ad rates down in general, so there’s no real buy-side motivation to address it. But adfraud does create problems for users, by giving fraud operators an incentive to surveil people and front-run a sale that’s about to happen. Paradoxically, the presence of PETs increases incentives for collecting surveillance data, so would likely put users at greater risk. More: why I’m turning off Firefox ad tracking: the PPA paradox

scale: The mathematical properties of PETs tend to rely on adding noise in order to create deniability or limit cross-site identification of an individual. PETs could be designed to add just the right amount of noise to tracking data, to make them effectively usable for tracking by a few large companies, or one. The Debian project has the concept of a DesertIslandTest: can one software developer on a desert island license their work to the other inhabitants without relying on an some off-island party? Third-party cookies, despite their other problems, pass the test, PETs don’t. Adopting PETs would tend to hard-code some problematically large company sizes.

Conclusion

Legislators and regulators need to apply the same considerations to PETs as to any other surveillance/personalized advertising practice. It’s important not to confuse the mathematical magic of PETs with the real privacy concerns of real people. For a start, I personally believe that it would be appropriate to apply a state-level surveillance licensing system to PETs. More: Sunday Internet optimism

Related

Topics API notes and links specific issues with one of the frequently discussed PETs

Bonus links

Kohler Doing Damage Control After CEO Admits He Never Uses Toilets (this is a funny Onion article when it’s about a toilet manufacturer, but the ad blocker version, not so much)

Google is using AI to censor independent websites like mine by Nate Hake (more on centralization risks)

Why Meta is in trouble in Nigeria and what this means for Facebook, Instagram and WhatsApp users by Tolu Olarewaju and Jagannadha Pawan Tamvada. The Federal Competition and Consumer Protection Commission has said quitting Nigeria won’t absolve Meta of its liability.

It Isn’t a Replication Crisis. It’s a Replication Opportunity by Rory Sutherland. In marketing, and hence in business, the opposite of a good idea can be another good idea.

Ukraine’s AI-powered ‘mother drone’ sees first combat use, minister says by Anna Fratsyvir. (Good example of why the EU wants to restrict the use of AI for marketing—the AI Act looks intended to nudge developers and entrepreneurs to focus on the problem of how to defend Europe without expanding military conscription in the countries that still have it, or reinstating it in the countries that don’t. Don’t start yet another annoying AI ad startup, go fill out the international partner form on Brave1.)

I was going to call this how to prosper in the coming de-enshittification boom but I’m still trying to figure that out.

David Gerard has a pretty good summary of today’s Internet business situation, in In 2025, venture capital can’t pretend everything is fine any more. This year, the investors are all-in on AI. Crypto’s still dead, quantum isn’t taking off—AI’s the last game in the casino. And the Big AI companies are having trouble making anything useful even for stuff like customer support where corporate decision-makers are already willing to tolerate low quality. So far, generative AI does have two winning use cases: slop and scams. Widely available generative AI tools are able to generate content that’s similar enough to real content to be plausible, but different enough that it can beat the algorithms used for detecting infringement and fraud. The AI/crime mess is most obvious at the Big Tech companies.

• Meta Battles an ‘Epidemic of Scams’ as Criminals Flood Instagram and Facebook by Jeff Horwitz and Angel Au-Yeung at The Wall Street Journal

• Google Ads Shitshow Report 2024 (You can almost stop reading this report at the release date. A report on the crime level of Google ads that got released on the same day as huge antitrust news isn’t going to be good.)

And it’s not just those companies. Sometimes it looks like we’re in a doom spiral of fake research, bogus articles and legal briefs, auto-generated accessibility problems, AI slop in educational apps and even classrooms, and, worst of all, as Asma Derja covers in The Weakest Link in AI, an enforcement of Big Tech points of view. Before AI can understand the world, someone has to tell it what the world means….Labelers in Kenya, the Philippines, and Venezuela are handed instruction sets written in California, reviewed in Dublin, and told to mark what counts as toxic, hateful, threatening, or real. They don’t decide. They execute. Rands writes, I am deeply suspicious of AI, especially after watching decades of social networks monetize our attention while teaching us to ignore facts and truth, minimizing our desire to understand….AI does an incredible job of confidently sounding like it knows what it’s talking about, so it’s easy to imagine what it will do in the hands of those who want to manipulate you.

Soatok writes, in Tech Companies Apparently Do Not Understand Why We Dislike AI. What concerns me about AI has very little to do with the technology in and of itself. I’m concerned about the kind of antisocial behaviors that AI will enable. The tech oligopoly is even more out of touch now than IBM or Microsoft ever got during their peak days. Big Tech companies have enough power to get away with being mean and dishonest and half-assing things. Most people don’t. The result is that the technologies that Big Tech builds, with the assumption that a certain level of meanness, dishonesty, and half-assing are acceptable, are less and less relevant to more and more people.

Maybe we’re not in a doom spiral after all? What if we’re back to something more like the dismal Microsoft monopoly from which came the Internet and free software boom of the 1990s? The green shoots of de-enshittification are popping up in lots of places once you start to look for them. Here’s a de-enshittification sighting in a toot from Hugo-award-winning author Charles Stross) explaining his switch from Microsoft Office 356 to LibreOffice:

CoPilot in Office would open me up to accusations of breach of contract—my book contracts warrant that they’re all my own work: CoPilot brings that into question.

Generative AI slop has real consequences for authors because of reputation risks and contract terms. A Big Tech company’s AI service can output copyright-infringing slop for a user—what are they going to do about it? An individual author can’t turn in copyright-infringing slop to a big publishing company.

More sightings…

12 years of Ghost by John O’Nolan. As ever, we’re competing with prominent, VC-funded platforms with tens of millions of dollars and hundreds of employees, playing zero-sum games to try and control the entire media industry. Now, though, it feels like there’s more appetite than ever for something different.

What makes a tech gadget calm? This certification firm has an 81-point checklist by Scharon Harding. Currently, six products are Calm Tech-certified

How to Improve Your Site’s Typography by Scott Knight. (More people are making personal web sites again.)

Just a QR Code by Gabriel M. Schuyler You type something, it makes a QR code. You can also set the size, error correction, and colors. And that’s it. Just a QR code.

Slop, surveillance, and extreme right politics are all part of the same big movement, so political concerns are a big motivator to avoid the first two. The networker: Microsoft shutting down email accounts of Trump’s foes should be worrying to us all by John Naughton on The Observer. Khan, who is a British subject, has already lost access to his bank accounts, which is what happens when you’re sanctioned. More interestingly, though, Microsoft has also blocked his email account….The reality is that it’s a global company based in the US. More: Trump’s trade war risks splintering the Internet, experts warn by Ashley Belanger. the #Eurostack, so hot right now

Enterprise Policies for Firefox Customization: A Beginner’s Guide by Asif Youssuff. I wanted to post something to enable people who are currently using dodgy user.js files and forks without security teams to use the mainline version of Firefox, just tweaked in known safe ways. (See Data and Firefox Suggest for info from Mozilla on one of their ad features. Firefox is still good, but they do end up in weird risky advertising rat holes sometimes, so it helps to have an independent tool to fix the settings. Bonus link: When Your Webcam Doesn’t Work: Solving Firefox and PipeWire Issues by Jan Grulich)

Related

Return of the power user The small-business-facing Internet is a more or less wretched hive of scum and villainy, from fairly mild shelfware without the shelf schemes, all the way to actively heinous stuff like sending your marketing budget to terrorists. But the underlying PC hardware is still getting way better…

Time for some horseshoe theory. Right-wing surveillance oligarchy has looped all the way back around to left-wing central economic planning.

Cory Doctorow sums up some recent news from Meta, in Pluralistic: Mark Zuckerberg announces mind-control ray (again). Zuck has finally described how he’s going to turn AI’s terrible economics around: he’s going to ask AI to design his advertisers’ campaigns, and these will be so devastatingly effective that advertisers will pay a huge premium to advertise on Meta.

Or, as Nilay Patel at The Verge put it, Mark Zuckerberg just declared war on the entire advertising industry. What Mark is describing here is a vision where a client comes to Meta and says I want customers for my product, and Meta does everything else. It generates photos and videos of those products using AI, writes copy about those products with AI, assembles that into an infinite number of ads with AI, targets those ads to all the people on its platforms with AI, measures which ads perform best and iterates on them with AI, and then has those customers buy the actual products on its platforms using its systems.

But the mind-control ray story, if true, would affect more companies, and functions within companies, than just advertising. Myles Younger writes, Zuck Says AI Will Make Advertising So Good Its Share of GDP Will Grow. Is That Really Possible? In the Meta version of the future, somehow the advertising share of the economy grows to include media, sales, and customer service. And a business that wants to sell a product or service would be able to change the number of units sold with one setting—the amount of money sent to Meta. That means the marketing department within the business can also be dramatically reduced. Or do you even need a marketing department when the one decision it has to make is how much money to send to Meta to move how many units? That could be handled as part of some other job.

TheZvi, in Zuckerberg’s Dystopian AI Vision, writes,

When asked what he wants to use AI for, Zuckerberg’s primary answer is advertising, in particular an ultimate black box where you ask for a business outcome and the AI does what it takes to make that outcome happen. I leave all the do not want and misalignment maximalist goal out of what you are literally calling a black box, film at 11 if you need to watch it again and general dystopian nightmare details as an exercise to the reader.

Such rightsizing, much futuristic! But wait a minute, this has been done. Centrally planned economies are already a thing, and have had well-known challenges for a while. On paper, the central planers decide up front how much of each product or service will be produced and consumed, but in reality the system ends up with everyone faking their numbers and gaming the system. For surveillance capitalism, that’s already happening. A majority of US teens have lost trust in Big Tech, and advertisers are starting to walk away from platforms’ AI solutions that once promised them everything. The top-down AI-driven social media story is less of a nightmare and more just more slop.

Related

Antitrust Policy for the Conservative by FTC Commissioner Mark R. Meador. (This is basically a good memo but is not going to have much impact in a political environment where a powerful monopoly can avoid government action by showing up at Mar-A-Lago to invest in a memecoin or settle a lawsuit. If we get to the point where there is a reasonably powerful honest conservative movement in the USA, then Meador’s work will be useful, probably with not too many updates.)

Bonus links

Even a Broken Clock Can Lower Drug Prices by Joan Westenberg. The CBO has repeatedly found that negotiated drug pricing—including international benchmarking—can save significant amounts of public money.

Crypto Is Still for Criming by Paul Krugman. (Will cryptocurrencies go mainstream, or will they be stuck as just a crime thing? Turns out the answer is both because crime is going mainstream.)

The AI Slop Presidency by Matthew Gault. (This kind of thing is a good reason to avoid generative AI header images in blog posts. The AI look has become the signature style of the pro-oligarch, pro-surveillance side. This is particularly obvious on LinkedIn. An AI-look image tends to mean a growth hacking or pro-Big Tech post, while pro-human-rights or pro-decentralization posters tend to use original graphics or stock photos.)

Monopoly Round-Up: China Is Not Why America Is Sputtering by Matt Stoller. Simply put, modern American law is oriented towards ensuring very high returns on capital to benefit Wall Street and hinder the ability to make things. (fwiw, surveillance capitalism is probably part of the problem too. Creepy negative-sum games to move more units of existing products have higher and more predictable ROI than product innovation does.)

Industry groups are not happy about the imminent demise of Energy Star by Marianne Lavelle The nonprofit Alliance to Save Energy has estimated that the Energy Star program costs the government about $32 million per year, while saving families more than $40 billion in annual energy costs.

The Mozilla Firefox New Terms of Use Disaster: What Actually Happened? by Youssuff Quips. It is clear is that Mozilla wants to be able to unambiguously claim to regulators that people agreed to have their data sold – they want that permission to be persistent, and they want it to be modifiable in perpetuity. That changes what Firefox has been, and the Firefox I loved is gone. (For what it’s worth I don’t think it’s as bad as all that. In a seemingly never-ending quest to get extra income that’s not tied to the Google search deal, Mozilla management has done a variety of stupid shit but they always learn from it and move on. They’ll drop their risky adfraud in the browser thing too at some point. More: why privacy-enhancing advertising technologies failed)

Previously: PET projects or real privacy?, stop putting privacy-enhancing advertising technologies in web browsers

From recent news: Google is reportedly wrapping up work on in-browser privacy-enhancing advertising features. Instead, they’re keeping third-party cookies and even encouraging going back to older user tracking methods like fingerprinting.

Google’s Privacy Sandbox projects were their own special case, and it certainly looks possible that their continued struggles were mostly because of trying to replicate a bunch of anticompetitive tricks from Google’s old ad stack inside the browser. Privacy-enhancing technologies are hard enough without adding in all the anticompetitive stuff too. But by now it looks more and more clearn that it wasn’t just a problem with Privacy Sandbox trying to do too much. Most of the hard problems of PETs for advertising are more general. Although in-browser advertising features persist, for practical purposes they’re already dead code. Right now we’re in a period of adjustment, and some of the interesting protocols and code will probably end up being adaptable to other areas, just not advertising.

While PETs for advertising were a bad idea for a lot of reasons, all I’m going to list here are the big problems they couldn’t get over.

PETs without consent didn’t work. The original plan in the early days of Privacy Sandbox was to deploy to users with a simple Got it! dialog. That didn’t work. Regulators in the UK wrote (PDF),

We believe that further user research and testing of the dialogue box, using robust methodologies and a representative sample of users, is critical to resolve these concerns. Also, it is not clear if users will be prompted to revisit their choices, and the frequency of this.

In the real world, PETs will be required to get the same kind of consent that other adtech is. Buried in a clickwrap agreement isn’t going to pass inspection. PETs are catching the same kinds of complaints over lack of consent as any other adtech. And getting consent will be hard, because…

Users are about as creeped out by PETs as by other kinds of tracking. Jereth et al. find that perceived privacy violations for a browser-based system that does not target people individually are similar to the perceived violations for conventional third-party cookies. Co-author Klaus M. Miller presented the research at FTC PrivacyCon (PDF):

So keeping your data safer on your device seems to help in terms of consumer perceptions, but it doesn’t make any difference whether the firm is targeting the consumer at the individual or group level in the perceived privacy perceptions.

Martin et al. find substantial differences between the privacy that users expect and the privacy (ish) features of PETs. In fact, users might actually feel better about old-fashioned web tracking than about the PET kind.

In sum, the use of inferences rather than raw data collected by a primary site is not a privacy solution for users. In most instances, respondents judged the use of raw data such as browsing history, location, search terms, and engagement data to be statistically the same as using inferences based on that same data. Further, for improving services across contexts, consumers judged the use of raw data as more appropriate compared to using inferences based on that same raw data.

PET developers tried to come up with solutions that would work as a default for all web users, but that’s just not realistic considering that the research consistently shows that people are different. About 30% of people prefer cross-context personalized advertising, 30% really don’t want it, and for 40% it depends how you ask. PETs are too lossy for people who want cross-context personalized ads and too creepy for people who don’t. (In addition to this published research, there is also in-house research at a variety of companies, including at some of the companies that had been most enthusiastically promoting PETs.)

PETs never had a credible anti-fraud story. One of the immutable laws of adtech is that you can take any adtech term and put fraud after it, and it’s a thing. PETs are no exception.

• Anti-fraud lesson of the 1990s: never trust the client

• Anti-fraud lesson of the 2000s: use machine learning on lots of data to spot patterns of fraud

• PETs: trust the client to obfuscate the data that your ML would have needed to spot fraud. (how was this even supposed to work?)

If PET developers could count on an overwhelming percentage of users to participate in PETs honestly, then there might not be a problem. A few people would try fraud but they would get lost in the noise created by PET math. But active spoofing of PETs, if they ever caught on, would have the same triad of user motivations that open-source software does: it feels like the right thing to do (since the PETs come from the same big evil companies that people are already protesting), you would have been able to make money doing it, and it’s fun. Any actual data collected by PETs would have been drowned out by fake data generated on principle, for money, or for lulz.

PETs didn’t change the market. The original, optimistic pitch for PETs was that they would displace other surveillance advertising technologies in marketing budgets and VC portfolios. That didn’t happen. The five-year publicity frenzy around Google’s Privacy Sandbox might actually have had the opposite effect. The project’s limitations, well understood by adtech developers and later summarized in an IAB Tech Lab report, encouraged more investment in the kinds of non-cookie, non-PET tracking methods that Mozilla calls unintended identification techniques.

Just as we didn’t see articles written for end users recommending PETs as a privacy tip—because the privacy they provide isn’t the privacy that users want—we also didn’t see anyone in the advertising business saying they were cutting back on other tracking to do PETs instead. Even Google, which was the biggest proponent of PETs for a while, lifted its 2019 ban on fingerprinting as Privacy Sandbox failed to take off. And Google was not just a technology provider for PETs, but also a possible client, with consumer phone and thermostat brands, among others. The most informative report about Privacy Sandbox was the absence of a report: no case study was ever released about Google’s own results using it for the Pixel and Nest brands.

PETs would create hard-to-predict antitrust issues. If users are still creeped out by PETs, and advertisers find PET features too limiting, then the designers of PETs must be splitting the difference and doing something right, right? Well, no. PETs aren’t just about users vs. advertisers, they’re about large-scale platforms vs. smaller companies. PETs introduce noise and obfuscation, which tend to make data interpretation only practical above a certain data set size—for a few large companies, or one. Designers of PETs can tune the level of obfuscation introduced to make their systems practical for any desired minimum size of company.

The math is complicated enough, and competition regulators have enough on their to-do lists, to make it hard to tell when PET competition issues will come up. But they will eventually.

PETs would have made privacy enforcement harder. This year’s most promising development in privacy news in the USA is the Honda case. Companies that had been getting by with non-compliant opt outs and Right to Know forms are finally fixing their stuff. CCPA+CPRA are progressing to their (intended?) true form, as a kind of RCRA for PII. Back in the 1980s, companies that had a bunch of random hazardous materials around decided that it was easier to safely get rid of them than to deal with RCRA paperwork, and something similar is happening for surveillance marketing today.

PETs would have interfered with this trend by making it harder for researchers to spot problematic data usage practices, and helping algorithmic discrimination to persist.

Conclusion: learning from the rise and fall of PETs. In most cases, there should be little or no shame in chasing a software fad. At their best, hyped-up technologies can open up a stale industry to new people by way of hiring frenzies, and create change that would have been harder to do otherwise.all right, the cryptocurrency and AI bubbles might be an exception because of the environmental impact, but the PET fad wasn’t that big. Having been into last year’s trendy thing can feel a little embarrassing, but really, a trend-driven industry has two advantages.

• A trend can give you a face-saving way to dig up and re-try a previous good project idea that didn’t get funded at the time. (this could still happen with prediction markets)

• Investing in a trend can be an excuse to fix your dependencies (I once got to work on fixing software builds, making RPMs, and automating a GPL corresponding source release, because Docker containers were a big thing at the time) and produce software that’s useful later (PDF-to-structured-text tools, so hot right now)

In the case of PETs there probably should have been more user research earlier, to understand that the default PETs without consent idea wouldn’t have worked and save development time, but that’s a deeper problem with the relative influence of people who write code and people who do user research within companies, and not just a PET thing.

Not all the development work that went into PETs was wasted, because PETs are still really promising in other areas, just not advertising. For example, electricity markets could benefit from being able to predict demand without revealing when individual utility customers are at home or away. PETs are already valuable for software telemetry—for example, revealing that a certain web page crashed the browser without telling the browser maintainer which users visited which pages—and could end up being more widely used for other products, where the manufacturer and user have a shared interest in facilitating maintenance and improving quality. But advertising is different, mostly because it’s unavoidably adversarial. Every market has honest and dishonest advertisers, and advertising’s main job is to build reputation by doing something that’s practical for a legit advertiser to do and as difficult as possible for a dishonest advertiser. As the shift to a low-trust economy continues, and more software companies see their reputations continue to slide, real ad reform solutions will need to come from somewhere else. More: Sunday Internet optimism

Bonus links

Time To Get Serious by Brian Jacobs. (A must-follow RSS feed for anyone interested in #adReform. We have spent decades building up thoughtful measurement systems through collaboration and compromise. And yet we are prepared to believe what suits the largest vendors without question and without any hint of criticism. Indeed, we build what suits them into our thinking, with scant regard as to whether it fits with what we know. We are in a crisis in large part of our own making.

What Do We Do With All This Consumer Rage? by Anne Helen Petersen. As consumers, the globalized marketplace (with a noted assist from venture capital) has taught us to expect and demand levels of seamless service at low prices. But the companies that provide seamless service at low prices often provide lower-quality products and service. Or, now that VC-backed enterprises like Uber and DoorDash have ceased to subsidize the on-demand lifestyle, they provide lower quality products or experiences at higher prices.

The anatomy of Anatomy of Humbug, ten years on by Paul Feldwick. The text of Anatomy emerged (over many years) as my attempt to articulate the unspoken assumptions that underlay the way we made advertising, in the thirty years I worked at a successful agency. It seemed to me that the theories we all uncritically believed fitted rather badly with the kind of advertising we produced, and, more worryingly, with the kind of advertising that we increasingly knew worked best. We agonised over single-minded propositions and consumer benefits; then we created singing polar bears, comic Yorkshiremen, and laughing aliens, and the public loved them. Something didn’t quite make sense.

Rage of the Oligarchs Naomi Klein: ’What They Want Is Absolutely Everything (I’m more worried about evil oligarchs who know how to hire and use a good PR team than I am about the guys who are willing to be the main character on Twitter or whatever, but maybe that’s just me)

Costco’s Kirkland brand is bigger than Nike—and it’s about to get even bigger by Rob Walker. Like all private labels, it competes with brand-name consumer products largely on price—an obvious advantage in belt-tightening times. But Kirkland is also the rare private label that’s developed its own powerful, and surprisingly elastic, brand identity. (Kirkland might be a success story for brand building by investing in measurable improvements. There is a content niche for posts like What to Buy from Costco & What to Avoid, which means an opportunity for Costco in offering low-overhead bargains and leaving it to independent content creators to get the word out.)