Gabriel Weinberg's Blog

We have a housing shortage in the U.S., and it is arguably a major cause of long-term unrest about the economy.

Putting aside whether AI will eliminate jobs on net, it will certainly displace a lot of them. And the displaced people are unlikely to be the same people who will secure the higher-tech jobs that get created. For example, are most displaced truck drivers going to get jobs in new industries that require a lot of education?

Put these two problems together and maybe there is a solution hiding in plain sight: create millions of new jobs in housing. Someone has to build all the affordable homes we need, so why not subsidize jobs and training for those displaced by AI? These jobs will arguably offer an easier onramp and are sorely needed now (and likely for the next couple of decades as we chip away at this housing shortage).

Granted, labor may not be the primary bottleneck in the housing shortage, but it is certainly a factor and one that is seemingly being overlooked. There are many bills in Congress aimed at increasing housing supply through new financing and relaxed regulatory frameworks. A program like this would help complete the package.

None of this has been happening via market forces alone, so the government would therefore need to create a new program at a large scale, like the Works Progress Administration (WPA) at the end of the Great Depression, but this time squarely focused on affordable housing (and otherwise narrowly tailored to avoid inefficiencies).

There are a lot of ways such a program could work (or not work), including ways to maximize the long-term public benefit (and minimize its long-term public cost), but this post is just about floating the high-level idea. So there you have it. I’ll leave you though with a few more specific thought starters:

* Every state could benefit since every state has affordable housing issues. Programs become more politically viable when more states benefit from them.

* Such a program could be narrowly tailored, squarely focused on affordable housing (as mentioned above), but also keeping the jobs time-limited (the whole program could be time-limited and tied to overall housing stock), and keeping the wages slightly below local market rates (to complement rather than compete with private construction).

* It could also be tailored to those just affected by AI, but that doesn’t seem like the right approach to me. The AI job market impact timeline is unclear, but we can nevertheless start an affordable-housing jobs program now that we need today, which can also serve as a partial backstop for AI-job fallout tomorrow. It seems fine to me if some workers who join aren't directly displaced by AI, since the program still creates net new jobs we will need anyway and to some extent jobs within an education band are fungible.

* We will surely need other programs as well to help displaced workers specifically (for example, increased unemployment benefits).

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* We have hundreds of easter-egg logos (featuring our friendly mascot Dax Brown) that surface when you make certain queries on our search engine. Our subreddit is trying to catch ‘em all. They’ve certainly caught a lot, currently 504, but we keep adding more so it’s a moving target. The total as of this post is 594. I’m the one personally adding them in my spare time just for fun and I recently did a Duck Tales episode (our new podcast) with more details on the process. This incarnation of specialty logos is relatively new, so if you are a long-term user and haven’t noticed them, that’s probably why (aside from of course that you’d have to search one of these queries and notice the subtle change in logo). And, no promises, but I am taking requests.

* There is a rumor continuously circulating that we’re owned by Google, which of course couldn’t be farther from the truth. I was actually a witness in the U.S. v. Google trial for the DOJ. I think this rumor started because Google used to own the domain duck.com and was pointing it at Google search for several years. After my public and private complaining for those same years, in 2018 we finally convinced Google to give us the duck.com domain, which we now use for our email protection service, but the rumor still persists.

* We’ve been blocked in China since 2014, and are on-and-off blocked in several other countries too like Indonesia and India because we don’t censor search results.

* We’ve been an independent company since our founding in 2008 and been working on our own search indexes for as many years. For over fifteen years now (that whole time) we’ve been doing our own knowledge graph index (like answers from Wikipedia), over ten years for local and other instant-answer indexes (like businesses), and in the past few years we’ve been ramping up our wider web index to support our Search Assist and Duck.ai features. DuckDuckGo began with me crawling the web in my basement, and in the early days, the FBI actually showed up at my front door since I had crawled one of their honeypots.

* The plurality of our search traffic now comes from our own browsers. Yes, we have our own browsers with our search engine built in along with a ton of other protections. How do they compare to other popular browsers and extensions, you ask? We made a comparison page so you can see the differences. Our mobile browsers on iOS & Android launched back in 2018 (wow, that’s seven years ago), and our desktop browsers on Mac and Windows in 2022/23. Our iOS browser market share continues to climb and we’re now #3 in the U.S. (behind Safari and Chrome) and #4 on Android (behind Chrome, Samsung, and Firefox). People appreciate all the protections and the front-and-center (now customizable) fire button that quickly clears tabs and data in an (also customizable) animation of fire.

* About 13% of U.S. adults self-report as a “current user” of DuckDuckGo. That’s way more than most people think. Our search market share is lower since all of those users don’t use us on all of their devices, especially on Android where Google makes it especially hard. Once you realize that then it is less surprising that we have the highest search market share on Mac at about 4% in the U.S., followed by iOS at about 3%. I’m talking about the U.S. here since about 44% of our searches are from the U.S., and no other country is double digits, but rounding out the top ten countries are Germany, the United Kingdom, France, Canada, India, the Netherlands, Indonesia, Australia, and Japan.

* Our approach to AI differs from most other companies trying to shove it down your throat in that we are dedicated to making all AI features private, useful, and optional. If you like AI, we offer private AI search answers at duckduckgo.com and private chat at duck.ai, which are built-into our browsers. If you don’t like or don’t want AI, that’s cool with us too. You can easily turn all of these features off. In fact, we made a noai.duckduckgo.com search domain that automatically sets those settings for you, including a recent setting we added that allows you to hide many AI-generated images within image search. Another related thing you might find surprising is search traffic has continued to grow steadily even since the rise of ChatGPT (with Duck.ai traffic growing even faster).

* If you didn’t know we have a browser, you probably also don’t know we have a DuckDuckGo Subscription (launched last year), that includes our VPN, more advanced AI models in Duck.ai, and in the U.S., Personal Information Removal and Identity Theft Restoration. It’s now available in 30 countries with a similar VPN footprint and our VPN is run by us (see latest security audit and free trials).

* Speaking of lots of countries, our team has been completely distributed from the beginning, now at over 300 across about 30 countries as well, with less than half in the U.S. And we’re still hiring. We have a unique work culture that, among other things, avoids standing meetings on Wednesdays and Thursdays. We get the whole company together for a week once a year.

* We played a critical role in the Global Privacy Control standard and the creation of search preference menus. I have a graduate degree in Technology and Public Policy and so we’ve done more of this kind of thing than one might expect, even going so far to draft our own Do Not Track legislation before we got GPC going. We also donate yearly to like-minded organizations (here’s our 2025 announcement), with our cumulative donations now at over $8 million. Check our donations page for details going back to 2011. We can do this since we’ve been profitable for about that long, and more recently have even started investing in related startups as well.

If this hodge-podge of stuff makes you think of anything, please let me know. I’m not only taking requests for easter-egg logo ideas, but also for stuff to write about.

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As GLP-1s are studied more, their benefit profile is expanding rapidly. Acknowledging that many questions remain, a recent journal article titled The expanding benefits of GLP-1 medicines puts it like this:

GLP-1 medicines, initially developed for blood glucose and weight control, improve outcomes in people with cardiovascular, kidney, liver, arthritis, and sleep apnea disorders, actions mediated in part through anti-inflammatory and metabolic pathways, with some benefits partly independent of the degree of weight loss achieved.

Many millions of Americans would benefit from taking these drugs, but limited insurance coverage and high out-of-pocket costs limit their use. However, if the price was low enough to match their cost savings, then wider coverage could be justified.

What price would that need to be?

How can a drug be cost neutral (pay for itself)?

If a drug reduces future care expenditures by more than it costs, then it pays for itself (is cost neutral). Modeling this out can get complicated, especially for drugs whose benefits accrue over many years. That’s because you need to at least consider how those cost savings unfold as well as people who stop taking the drug (adherence rate).

What about GLP-1s?

The Congressional Budget Office (CBO) looked into this question in detail in 2024, using these approximate assumptions:

* 9-year time horizon (2026-2034)

* 35% adherence (continuation) in first year, ramping up to 50% by year 9

* 80% yearly continuation rate after first year of continuous use

* Available to Medicare patients who are classified as obese or overweight with at least one weight-related comorbidity

* $5,600/year cost (implying about ~$625/month cost if you assume a 75% reimbursement)

* Savings from reduced care of $50/year in 2026, reaching $650/year in 2034

CBO concludes in their report that these assumptions lead to expanding GLP-1 coverage to be very costly to the Federal government.

Doesn’t Medicare prescribe GLP-1s now?

Yes, but not for obesity writ large, which about doubles the qualified population. From the CBO report:

In 2026, in CBO’s estimation, 29 million beneficiaries would qualify for coverage under the illustrative policy. About half of that group, or 16 million people, would have access to those medications under current law for indications such as diabetes, cardiovascular coverage, and other indications approved by the FDA in the interim.

Still, CBO only expects a small percentage of eligible patients to use the drugs, due to activation and adherence. In the final year of their model (2034) they predict “about 1.6 million (or 14 percent) of the newly eligible beneficiaries would use an AOM [anti-obesity medication].”

What break-even price does the CBO report imply?

CBO doesn’t calculate a break-even price. They just say they expect $50 in average savings in year 1, rising to $650 in year 9, implying a 9% offset rate overall. If we assume a progression of increasing yearly savings to match these assumptions, you get a cumulative savings of about $4,000, or about $445 per year. If you assume on average the government picks up 75% of the bill, that implies a break-even drug price of about $50/month.

What has changed since 2024 that would modify this CBO estimate?

* Time Horizon. The CBO time horizon of 9 years is too low. They acknowledge that “from 2035 to 2044…the savings from improved health would be larger than they would be from 2026 to 2034”. So, let’s add 10 years (for a total of 19), and stipulate the last ten years average $800 in savings, rising from the year 9 savings of $650. That implies an increased average savings per year of about 1.4x.

* Emerging Benefits. The CBO only accounted for weight-loss benefits, using comparisons to Bariatric surgery and other weight-loss evidence, noting that “CBO is not aware of any direct evidence showing that treatment of obesity with GLP-1-based products reduces spending on other medical services.” However, the other emerging benefits reduce conditions that are very costly to Medicare like kidney, heart, and sleep apnea complications (e.g., dialysis, heart surgery, CPAP, etc.). I think we can speculatively call this a 2x multiplier.

So, then what break-even price does that imply today?

$50/month (CBO original estimate) x 1.4 (for increased time horizon) x 2 (for increased benefits) =~ $140/month.

That is, at $140/month, we would expect the Medicare costs to roughly equal the cost savings, and net out to 0 (be cost neutral). That’s still well below the recently negotiated prices starting in 2027 (for example, Ozempic at $274).

Why are you thinking about this again?

I’m seeing the expanding benefit profile and thinking we have to find a way to get these benefits to more people, as a way to generally increase our average standard of living (in this case by greatly increasing health-span/quality of life).

The best way I can see to get the benefits to the most people is if it were government subsidized/provided. But obviously health care costs are a major barrier to that method, and so framing expanding benefits as cost neutral seems most politically viable.

What if the price were $100/month?

At $100/month, then it would be a no-brainer (assuming the above math is correct) to make available to qualified Medicare patients (say, using at least the CBO obesity criteria) since it would then be clearly making the government money.

Additionally, at that price, I think you could start expanding it well beyond Medicare in waves, monitoring outcomes and cost savings. For example, you could start with programs where the government similarly runs both the cost and benefits like Medicare, such as for the military and other federal workers. Then you could expand to Medicaid / disability (with cross-state subsidies). Ultimately there could be justification to subsidize a subset of the public at large, for example people aged 55+ who will be on Medicare within the next ten years, such that the savings will be realized by the federal government and the whole program could still be cost neutral.

OK great, but how to you get GLP-1s at $100/month?

This may be a half-baked idea, but one approach is to offer up the market a yearly contract for expanded Medicare, and whoever shows up first gets it (to be renegotiated yearly).

I don’t think this is that crazy because the manufacturing cost is estimated to be a small fraction of the list price, and the UK previously had negotiated pricing in this ballpark. The volumes would be huge, and as more companies enter the market, I imagine eventually one of them would take the offer.

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Disclaimer: This approach works for me. It may not work for you, but also maybe gives you some ideas.

I find the signal to noise ratio on social media and news sites/apps too low for me to have a consistently good experience on them. So, I developed an alternative, heavily curated approach to an information diet that I’m laying out here in hope people will give me suggesions over time to improve it.

It involves four main inputs:

* RSS, skewed towards “most upvoted” feeds

I use Reeder because it has a polished user interface, formats the feed in an aggregated, chronological timeline across devices, and has native reddit and filtering support, but there are many other RSS readers too.

I subscribe to around 25 feeds and 25 subreddits through Reeder. To increase the signal to noise, I try to find “most upvoted” feeds where possible. For example, for subreddits, I usually use the top posts for the week, which you can get for any subreddit like this: https://www.reddit.com/r/economics/top.rss?t=week (just replace ‘economics’ with the subreddit of your choice). Doing so will get you on the order of five top posts per day, but you can also change ‘week’ to ‘day’ to increase that number to about twenty or to ‘month’ to decrease it to about one, which I do for some feeds. To find generally interesting subreddits I looked through the top few hundred subreddits, and then I also added some niche subreddits for specific interests I have.

Below is part of my reddit list (alphabetical). You can see I have some really large subreddits (technology, science, todayilearned) mixed in with more niche ones (singularity, truereddit), as well as communities (rootsofprogress, slatestarcodex) and hobbies (phillyunion, usmnt). Getting about twenty five across a range of your interests makes a good base feed.

Many publications still have RSS feeds if you search for publication name+RSS. If they don’t, it’s likely RSS.app or Feedspot has made one you can use instead. There is usually support through one of these methods for sub-section publication feeds, for example the tech section.

Here are some other examples of non-reddit “most upvoted” feeds that might be more widely appealing:

* Hacker News RSS - for example, I added the 300 and 600 points feeds, meaning you get notified when a story hits 300 or 600 points (you can pick any number).

* NYT RSS - they have most emailed/shared/viewed

* Techmeme RSS - curated by the techmeme team

* LessWrong RSS - they have a curated feed

Then I also just consume the main RSS feeds of some really high signal publications like Ars Technica (full articles come through for subscribers), The Information, Quanta, etc.

Even with all this curation, the signal to noise for me isn’t that great. I skim through the timeline mostly, but I do end up getting a bunch of interesting articles this way every day. I do use the filtering feature of Reeder to drop out some really low hit keywords.

* Podcasts

I subscribe to about 20 podcasts via Overcast. I like the Overcast “Voice Boost (clear, consistent volume)” and “Smart Speed (shorter silences)” features as well as the ability to do a custom playback speed for each podcast.

The signal to noise ratio is better here than the RSS feeds, but I still don’t listen to every episode, and for ones I do I often skip around. I like having a queue to listen to in the car and at the gym.

I find new podcast discovery pretty hard. I’ve looked through the Overcast top podcasts lists in all the different categories, and tried lots of them, but not many stick for me.

* Email newsletters

I subscribe to about the same amount (20-25) of email newsletters, some daily but most weekly or less. Signal/noise is less than podcasts, but greater than the RSS feeds. I’d guess my hit rate is about 20% in terms of reading them through vs. maybe 50% for podcasts listening through and 5% for the full RSS amalgamation reading through.

About half of the email newsletters I subscribe to are through Substack and half are direct from websites/organizations.

* People sending me links

I really appreciate when people send me curated links, which happens less than I’d like but I can’t complain because the signal to noise here is the highest with a hit ratio maybe 80%. I try to encourage it by saying thank you and responding when I have thoughts.

With those four inputs, I feel decently covered, but sometimes I do wonder what I’m missing out on and occasionally relapse back to going directly to a news or social media app and skimming the front page. This method of course depends on having a good list of feeds, podcasts, and newsletters. But in general, I’m personally happier with this approach, though of course your mileage my vary. If you’re doing something similar and have any ideas on process tweaks or specific recommendations for feeds, podcasts, or newsletters, I’d love to hear them.

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In response to “A U.S.-China tech tie is a big win for China because of its population advantage,” I received feedback along the lines of shouldn’t we be looking at China’s working-age population and not their overall population? I was trying to keep it simple in that post, but yes, we should, and when we do, we find, unfortunately, that China’s population advantage still persists. Here’s the data:

Currently, China’s working-age population is over 4 times the U.S.

According to Our World in Data, China’s working-age population is 983 million to the U.S.’s 223 million, or 4.4x.

In 2050, despite being in rapid decline, China’s working-age population is still projected to be over 3 times the U.S.’s

The projections put China’s 2050 working-age population at 745 million to the U.S.’s 232 million, or 3.2x.

In 2075, noting projections are more speculative, China’s working-age population is still projected to be about double the U.S.’s

The projections put China’s 2075 working-age population at 468 million to the U.S.’s 235 million, or 2.0x.

Noah Smith recently delved into this rather deeply in his post “China’s demographics will be fine through mid-century” noting:

China’s economic might is not going to go “poof” and disappear from population aging; in fact, as I’ll explain, it probably won’t suffer significant problems from aging until the second half of this century.

And even in the second half, you can’t count on their demographic decline then either, both because even by 2075 their working-age population is still projected to be double the U.S.’s under current conditions, but also because those conditions are unlikely to hold. As Noah also notes:

Meanwhile, there’s an even greater danger that China’s leaders will panic over the country’s demographics and do something very rash…All in all, the narrative that demographics will tip the balance of economic and geopolitical power away from China in the next few decades seems overblown and unrealistic.

OK, why does this demographic stuff matter again?

Check out my earlier article for details, but here’s a summary.

[A] U.S.-China tech tie is a big win for China because of its population advantage. China doesn’t need to surpass us technologically; it just needs to implement what already exists across its massive workforce. Matching us is enough for its economy to dwarf ours. If per person output were equal today, China’s economy would be over 4× America’s because China’s population is over 4× the U.S. That exact 4× outcome is unlikely given China’s declining population and the time it takes to diffuse technology, but 2 to 3× is not out of the question. China doesn’t even need to match our per-person output: their population will be over 3× ours for decades, so reaching ⅔ would still give them an economy twice our size since 3 × ⅔ = 2.

…With an economy a multiple of the U.S., it’s much easier to outspend us on defense and R&D, since budgets are typically set as a share of GDP.

…What if China then starts vastly outspending us on science and technology and becomes many years ahead of us in future critical technologies, such as artificial superintelligence, energy, quantum computing, humanoid robots, and space technology? That’s what the U.S. was to China just a few decades ago, and China runs five-year plans that prioritize science and technology.

…Our current per person output advantage is not sustainable unless we regain technological dominance.

…[W]e should materially increase effective research funding and focus on our own technology diffusion plans to upgrade our jobs and raise our living standards.

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If you don’t know about Total Factor Productivity (TFP), you probably should. It’s an economic concept that is arguably the most important driver of long-term economic prosperity. An International Monetary Fund (IMF) primer on TFP explains it like this (emphasis added):

It’s a measure of an economy’s ability to generate income from inputs—to do more with less…If an economy increases its total income without using more inputs…it is said to enjoy higher TFP [Total Factor Productivity].

TFP is an important macroeconomic statistic [because] improvements in living standards must come from growth in TFP over the long run. This is because living standards are measured as income per person—so an economy cannot raise them simply by adding more and more people to its workforce.

Meanwhile, economists have amassed lots of evidence that investments in capital have diminishing returns. This leaves TFP advancement as the only possible source of sustained growth in income per person, as Robert Solow, the late Nobel laureate, first showed in a 1957 paper.

So, it’s important. Critically important to long-term progress. To learn more about TFP, check out the full IMF primer referenced above and then this post I wrote about TFP titled “The key to increasing standard of living is increasing labor productivity,” which also has more links embedded in it. It explains how the only sustainable way to increase TFP is to “to invent new technology that enables workers to do more per hour.” And this is why I’m always going on and on about increasing research funding.

Let’s assume for a second that most people want more prosperity and that long-term prosperity does indeed primarily flow through Total Factor Productivity. Then why aren’t we talking about TFP a lot more? Why isn’t Total Factor Productivity front and center in our political agendas?

I think there are a host of reasons for that, including those I outlined in the paradox of progress. But another even simpler reason has to be that Total Factor Productivity is a terrible, inscrutable name, at least from the perspective of selling the concept to the mainstream public.

Every word of it isn’t great. It starts with “total,” which isn’t as off-putting as the other words, but doesn’t add much especially as the first word, let alone the fact that economists quibble that it isn’t an actual total. “Factor” seems like a math word and doesn’t add much either. And then you have “productivity,” which is confusing to most people because it has an unrelated colloquial meaning, and from a political perspective it also codes as job-cutting which is inherently unappealing.

Now, lots of economics jargon has similar problems, case in point “Gross Domestic Product” (GDP). Given GDP hasn’t been rebranded, I doubt TFP will either. That said, I think for anyone trying to communicate this concept to the public, we shouldn’t take the TFP name or acronym as a given, but try to use something more appealing and inherently understandable.

I’m looking to switch to something else but not sure to exactly what. My thinking so far has led me to work in the words “prosperity” or “innovation” directly like:

* Prosperity Driver

* Prosperity Component

* Innovation Multiplier

Do you have any other suggestions?

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Fifteen years ago Google started using their search monopoly to create a browser monopoly by pushing people to use Chrome through in-product promotions in Google search. It worked. Now they’re repeating that same playbook for consumer AI with Gemini and it’s working again.

In the last 30 days, Gemini has been downloaded about the same amount of times as ChatGPT, and nothing else is even close.

While ChatGPT had a massive head start, Google is rapidly turning consumer AI into a duopoly. Despite endless headlines mentioning Anthropic, Perplexity, and others, none of the alternatives seem to be meaningfully gaining market share right now relative to ChatGPT, except Gemini. The reason is simple: the others don’t have the distribution channels to match Google’s.

The next phase of consumer AI competition will favor Google even more. As I recently noted, consumer internet workflows increasingly span across search, browsing, and AI. Who has the most entrenched position in search and browsing to complement consumer AI? Google. For example, their monopoly browser (Chrome) can get AI features to most consumers the fastest.

Google’s ability to leverage its market position to distribute its own AI products continues unabated, and U.S. v. Google made clear that distribution powers a scale advantage. That is, Google’s search assets are not easily replicable because of the vast user engagement data Google alone possesses. And an increasing number of sites don’t even allow web crawlers or access to their content except for Google.

We shouldn’t settle for a shift from Google’s search monopoly to an AI duopoly. Thus far, regulators have only addressed Google’s advantages at the margins. There remains time to address these dynamics and unlock innovation.

One possible (non-regulatory) response is deeper partnerships and consolidation between the other AI companies, search engines, and browsers in an effort to compete more with more scale in this new market. This has already started around the edges, for example we (at DuckDuckGo) have partnered with You.com to develop a better news search index and are looking to partner with others to advance the web index we’ve been working on, as well as to enhance our browser and AI features. But the market is ripe for larger deals.

To see where those might come from, here’s the top mobile (consumer AI is used primarily on mobile) search engines in the U.S. according to Cloudflare, who sees the most traffic. Google is #1, followed by us (DuckDuckGo) at #2, then Yahoo and Bing. Everyone else is sub 1%.

Similarly, here’s the top mobile browsers in the U.S. Safari and Chrome dominate, followed by Samsung Internet, DuckDuckGo and Firefox above 1%.

And finally, here are the top 20 consumer web destinations in the U.S., according to SEMRush. The top ten are Google, Reddit, Facebook, Amazon, Yahoo, Wikipedia, Instagram, DuckDuckGo, and ChatGPT.

Partnerships and consolidation between these companies could produce some more effective competition. So far, consumer AI has actually driven more traditional search and browser usage, not less. We see that in our numbers, and SparkToro reports similar for others. AI is driving people to do more information seeking in general, and as mentioned those workflows increasingly span across search, browsing, and AI. The best experiences seamlessly blend all three in the browser, and so it is natural companies with assets in some of the three areas would want to partner with companies with non-overlapping assets. Additionally, a company with a large consumer user base could help directly drive distribution of consumer AI, browsers, and search engines, especially if that company has unique content assets.

A duopoly in consumer AI will not just be bad for innovation, but will further erode privacy. That’s why I believe DuckDuckGo will remain an important alternative regardless of what happens, but I’m still a little hopeful that innovative partnerships and consolidation could challenge the rapidly emerging consumer AI duopoly.

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Progress doesn’t have a single agreed-upon definition, but for the sake of anchoring, let’s say progress is rising living standards. While this definition seems unambiguously good, deserving of top billing on policy agendas for both major parties, the paradox is that long-term progress agendas rarely get top billing. Why? Here are three underlying reasons I’ve noticed in thinking about advocating for a lot more basic research funding, which I think needs to be the cornerstone of any credible progress agenda:

* Timescale mismatch. People want benefits now, not decades from now. U.S. politics runs on two-year cycles, while progress policies need decades to compound into large increases in living standards. For example, 2% vs. 3% growth, which would be a great outcome for a progress agenda, seems like a rounding error to most people even though it is meaningful when compounded. And the politicians championing these policies won’t be around to claim credit when they pay off decades later.Resolving this part of the paradox would involve articulating short-term benefits in some manner, for example that research funding is a jobs engine in the short term. It could also involve bundling longer-term investments like research funding in a particular field with shorter-term concrete results like rollout projects in that same field, which people can start seeing the phsyical results from within a couple of years.

* Change aversion. Advocating for far-future progress is selling a sci-fi world, which a lot of people take (and creative media often depict) as dystopian, not utopian. True progress means society changes for the better, delivering better-paid jobs using more advanced technology, and products that bring new conveniences and experiences. But change also means at least some disruption of current ways of life and thinking, and that creates winners and losers in the short term, which in turn creates reasonable anxiety.Resolving this part of the paradox would involve painting a clearer picture of what exactly will change in the short term, paired with explicit transition support for people most directly affected. It could also involve less focus on the far-future altogether, focusing instead on shorter timeframes that could be more easily contextualized.

* Lacking urgency. Not only is there not a clear picture, but progress agenda framing lacks urgency, emphasizing future opportunities rather than short-term crisis. Crisis framing comes with inherent urgency that opportunity framing lacks.Resolving this part of the paradox would involve reframing progress agendas as a response to crisis, such as the risk of China leapfrogging us in critical technology and the military and economic consequences that brings.

All these resolutions share a common thread: making distant abstractions concrete. I’m increasingly convinced that advocating for progress, whether it be basic research funding or otherwise, requires bundling long-term promises with near-term demonstrations, including explicit workforce transition plans, and framing progress as helping to address competitive threats we’re already facing.

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The browser wars are back. Agents are one front; the overlooked front is reducing switching costs in workflows that bridge across search, browse, and AI.

Overlapping search, browse, and AI in the browser

Traditional web search, browsing, and AI chatting aren’t going away, even as many people will eventually start interacting more with agents that automate tasks. And all three of these core Internet activities increasingly overlap into complex workflows. Many queries you could start either with a web search or an AI chat, and many of those end with wanting to browse to a website. Sometimes you’re on a website and want to ask a question about it. Sometimes you are in a chat and want to run a related web search, and vice versa.

The best product lets you complete whole tasks with the least friction. Agents are one approach. The other approach, which keeps users in the driver seat, is to seamlessly integrate search, browse, and AI into one interface. Both are important fronts in the new browser wars.

The DuckDuckGo Browser

We’ve been working on AI at DuckDuckGo for several years now, with an overall approach to provide private, useful, and optional AI features—including chat and search instant answers—to people who want the productivity benefits of AI without the privacy risks. Our chat service at duck.ai, which allows you to chat privately with popular chatbots and get real-time answers from the web, has the highest satisfaction ratings we’ve ever seen in a new service, and Search Assist, our take on Google’s AI Overviews, is currently our highest-rated search feature.

While we’re the second largest search engine on mobile in the U.S., we’re also the 4th largest mobile browser (and #3 on iOS specifically). In fact, we think of ourselves as a browser company at this point. I believe the best search, AI chat, and web user experience is where they are all integrated deeply in the browser (vs. in separate apps or services), supporting workflows that allow you to seamlessly move between modes as needed.

Seamless workflows: what we’re creating at DuckDuckGo

I believe our browser is so popular because we focus not just on protection but on also continually refining the user experience, aiming for both dependability and delight. That’s why we have been actively working on creating such seamless workflows across search, browse, and AI for some time now. Here are a few examples.

Easily accessible AI chat sidebar

We have a sidebar in our desktop browser that is easily accessible from any webpage to ask an AI chat question, optionally using page context. From there, you can ask follow-ups, pop it out into a new tab if you need more space, or hide it to return to give more room to the website without losing your place.

Toggle between Duck.ai and search via address bar / homepage

We now have an optional address bar mode on mobile (coming to desktop next) that allows you to easily toggle between private web search and private AI chat when starting queries. Ideally this makes it just as easy to start a web search or an AI chat as in a standalone search or chat app, and further allows you to switch modes mid-query without re-typing.

The toggle is also available on (or coming soon to) our homepage and new tab page.

Handoff between search results and chat

If you start a web search, we’ve built in ways to more easily jump into chat mode if desired. You can click Duck.ai to go into AI mode, or the chat icon from within Search Assist to ask a specific follow-up question related to the answer, which will carry over the answer and sources as context.

Integrating traditional search into Duck.ai

From within Duck.ai chat conversations, we automatically search the web for you and provide links to related searches or websites when appropriate, allowing you to jump back into search or browse mode if desired.

We’re working on refining these features based on user feedback as well as designing more. The best user experience will improve the workflows of the many people that are bouncing between these modes dozens of times a day. Your thoughts are welcomed!

Keeping AI private and optional

As mentioned, our overall approach to AI features is to keep them useful, private, and optional. The above illustrates usefulness, so now a few closing words on private and optional. Similar to our search engine, our AI chats are anonymized. In addition, chats are not used to train AI models. There’s a lot more about private AI chats on our help pages.

We also make sure everything we do with AI is optional, since we know not all of our users want to use AI for a variety of reasons, and that is fine with us. All AI features can be turned off, in both our browser and our search engine.

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I previously called on Congress to ban AI surveillance because of its heightened potential to easily manipulate people, both for commercial and ideological ends. Essentially, we need an AI privacy law.

Yet Congress has stalled on general privacy legislation for decades, even in moments of broad public privacy focus, like after the Snowden revelations and the Cambridge Analytica scandal. So, instead of calling for another general privacy bill that would encompass AI, I believe we should focus on an AI-specific privacy bill.

Many of the privacy frameworks floated over the years for general privacy regulation could essentially be repurposed to apply more narrowly to AI. For example, one approach is to enumerate broad consumer AI rights, such as rights of access, correction, deletion, portability, notice, transparency, opt-out, human review, etc., with clear processes to exercise those rights. Another approach is to create legally binding duties of care and/or loyalty on organizations that hold AI data, requiring them to protect consumers' interests regarding this data, such as to minimize it, avoid foreseeable harm, prohibit secondary use absent consent or necessity, etc.

There are more approaches out there and they are not mutually exclusive. While I have personal thoughts on some of them, my overriding goal is to get something, anything useful passed, and so I remain framework-agnostic. However, I believe within whatever framework Congress adopts, certain fundamentals are non-negotiable:

* Ban a set of clearly harmful practices. Start with what (I hope are) universal agreement items, like identity theft, deceptive impersonation, unauthorized deepfakes, etc. The key is explicitly defining this as a category so that we can debate politically harder cases like personalized pricing and predictive policing (both of which I think should also be banned).

* Practices near the ban threshold should face higher scrutiny. For example, if we can’t manage to outright ban using AI to assist in law enforcement decisions, at the very least this type of use should always be subject to human review, reasonable auditing procedures, etc. Using AI for consequential decisions, like for loan approvals, or for processing sensitive data, like health information, should at least be in this category. And many practices within this category, especially with regards to consumer AI, should be explicitly opt-in.

* Make everything else transparent and optional. Outside the bright-line bans and practices subject to higher scrutiny, any other AI profiling must be transparent and at least come with the ability to opt-out, with only highly limited exceptions where opt‑outs would defeat the purpose, like for legal compliance. Consumers also need meaningful transparency, including prominent disclosures that indicate clearly when you are interacting with an AI system. That means not just generic data collection notices or folding into existing privacy policies, but plain-language explanations shown (or spoken) prominently at the time of processing, which detail what AI systems are inferring and deciding.

* States must maintain authority to strengthen, not undermine, federal minimums. I wrote a whole post about why, with the gist being AI is changing rapidly, the federal government doesn’t react to these changes quickly enough, and states have shown they will act, both in AI and privacy.

Finally, these protections won't stifle progress. Some oppose any AI regulation because they believe it will hinder AI adoption or innovation. In terms of innovation, privacy makes a good analogy: Despite fears that a “patchwork” of state privacy laws would wreak havoc on innovation by going too far, they haven’t. Innovation hasn’t stalled, and neither have Big Tech privacy violations. In terms of adoption, the backlash against AI is real and rising, and smart regulation can help build the trust necessary for sustained AI adoption, not hinder it. We can get the productivity benefits of AI without the privacy harms.

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