They killed our Jesus: A Lament for Generation Jones
Two things happened in 1980 that would ensure the iron grip of the fascist state would (first slowly, then quickly), tighten on the entirety of the nation's populace from that moment forward: Ronald fucking Reagan was installed as president, and a CIA-psyop'd Christian Nationalist shot and killed John Lennon.
Those two things are connected.
First let's look at exactly who "Generation Jones" encompasses, and specific moments in the generational timeline that defined our future. The wiki page is actually quite good. Here's an excerpt that really hits it on the head:
"The name "Generation Jones" has several connotations, including a large anonymous generation, a "keeping up with the Joneses" competitiveness and the slang word "jones" or "jonesing", meaning a yearning or craving.[17][18][19] Pontell suggests that Jonesers inherited an optimistic outlook as children in the 1960s, but were then confronted with a different reality as they entered the workforce during Reaganomics and the shift from a manufacturing to a service economy, which ushered in a long period of mass unemployment. Mortgage interest rates increased to above 12 percent in the mid-eighties,[20] making it virtually impossible to buy a house on a single income. De-industrialization arrived in full force in the mid-late 1970s and 1980s; wages would be stagnant for decades, and 401Ks replaced pensions, leaving them with a certain abiding "jonesing" quality for the more prosperous days of the past.
Generation Jones is noted for coming of age after a huge swath of their older brothers and sisters in the earlier portion of the Baby Boomer population had; thus, many note that there was a paucity of resources and privileges available to them that were seemingly abundant to older Boomers. Therefore, there is a certain level of bitterness and "jonesing" for the level of doting and affluence granted to older Boomers but denied to them.[21]"
That sets the stage, for the most part. I was four when JFK was shot on TV. I was a wide-eyed, open-eared five year old when The Beatles were on Ed Sullivan and The Supremes were on the radio. I was ten when we landed on the moon, and I wanted to be a hippie at Woodstock at eleven. "Basketball Jones" came out when I was 13...I jonesed for a telescope because SPACE and got one from that great maker of fine telescopes, KMart.
Generationally, we jonesed to be ten years older, so we could have had all the cool shit THEY had. They had The Beatles, and we had the solo Beatles, they had Hendrix, Cream, Jefferson Airplane, and we had the fucking BeeGees and disco. It's like we, as a generation, were fated to live The K-Mart Knockoff of Life, instead of the bright, shiny Brand Name One all our older brothers and sisters got.
MUSIC and SCIENCE were EVERYTHING to us as kids/teens...the Enshittification Of Music truly began in 1973, and proceeded through SynthPop Hell in the '80s. Rock and Roll heroes became hairdos with guitars. The rock heroes of the '60s were getting married and having kids and baking bread. AM Radio ceased to be something you listened to for music...it began to replace music with strident, screaming hate voices that would eventually engulf all of AM Radio 24/7/365.
We were continually thwarted most of the way from our young adulthood on, blatantly from the moments in 1980 that the vile Ronald Reagan and the core operatives of evil for the next 50 years took over, and then the moment of what I call "Our Generational Wounding", the murder of John Lennon.
Back in '66, John had inflamed all the grandpas of todays magats by saying (truthfully) that with teens, The Beatles were more popular than Jesus. Beatle hate became a Very Big Thing in Bumfuck South Texas. Record burnings, merchandise burnings, book burnings, all were commonplace. A very palpable, and very specifically "Anti-Beatle" hate got instilled in a lot of kids/teens at that point, so anything to do with the Beatles was taboo for "good people" (read Southern Baptists) to like.
That, of course, made me love them that much more, and to follow their paths from their breakup forward with 'bated breath, buying every 45 they put out, trying to save pennies up to buy their albums.
John was the radical hippie, the one who wanted peace, the one with the weirdo wife, the one who held a "Bed-In" for peace. In a very fundamental-to-our-generation way, John Lennon was OUR "Jesus".
Richard Nixon (president from '68 to '74) HATED him.
In 1971, there was a true mass consciousness that incorporated us along with our older siblings, a musical mass consciousness. I became aware of many things in 1969, specifically fall of '69, so I was experiencing all this in real-time, as it happened. When the news that The Beatles officially broke up came across the AM radiowaves in May of '70, it was A. Very. Big. Deal. Everyone watched everything they did from that point on with GREAT interest.
George put out "My Sweet Lord" and "What Is Life" (first record I ever bought), John put out "Instant Karma", "Mother", then "Power To The People", then "Imagine". Ringo put out "It Don't Come Easy", and Paul & Linda had "Uncle Albert/Admiral Halsey". EVERYBODY was a "post-breakup Beatle critic", panning Paul's very first solo 45 "Another Day", "Uncle Albert" was the followup. This band called Badfinger that sounded suspiciously like The Beatles appeared on American radio, and would make 1972 one of the final "Golden Years" of AM Rock Radio.
In 1970 we heard about this Elton John guy, by the end of '72, I was playing as many of his songs on the piano as I could figure out. My favorite album was (still is) "Madman Across The Water". When "Goodbye Yellow Brick Road" came out in '73, a very noticeable shift was occuring.
Pop became much less political. It softened. It mellowed. It grew its hair long and lived in the country, learned how to grow potatoes and play the mandolin, making Country Rock the one lasting "legacy" of our sad sub-generation. By the time I graduated HS in May of '77, it was all there was on the radio, besides....disco. Oof.
One of my first TV memories was JFK getting shot. That was the Generational Wounding of our older brothers and sisters. When Mark Chapman (a Christian nationalist who changed the words of "Imagine" to "Imagine there's no John Lennon") shot John in December of 1980, it was the 2 in the 1-2 PUNCH done to our OUR generation. The first, of course, being the installing of Reagan and the evil Evangelical influence beginning in earnest.
It also began the buildup of the "Holy War" radical right, and an utter denial and clampdown of "hippie", of "counterculture" in general began, ensuring that John's vision of world peace would never come true, at least not on their watch. They had, effectively, killed OUR Jesus, along with our chances of the kind of security our older sibs got in spades. It also marked the unholy marriage of the evangelicals and the republican apparatus.
When Reagan got elected by virtue of the vile Newt Gingrich's 'Southern Strategy', a clampdown in earnest on the very SPIRITUAL EXISTENCE of our generation's incredible want and need, our collective JONESING for world peace began. Richard Nixon had planted the seeds. Nixon hated John Lennon with a passion. After Reagan was elected, I firmly believe Chapman was "activated" and they killed John as a Christmas present to Nixon.
It was after that, when the dream of a scientific future began to die, as well. When we were in high school, SCIENCE WAS EVERYTHING, so we wanted to be some kind of scientist "when we grew up".
I dealt with four years of college, majored in Biology, and in early 1981 realized my dream of being a Forest Ranger in Yosemite or some other national park somewhere, living in a cabin, giving talks to visitors about the biology aspects of the park....all that went POOF, almost instantaneously. My degree would get me nowhere, so I left before the end of that year and started working in record stores.
I was effectively the Cusack character in the movie about record stores, but it led to a dead end. Record stores weren't all that glamorous, and yes, the pay was dogshit. I tried working in record stores for the love of the music, while trying to BE a musician in a town FILLED OVER FLOWING with musicians, but that was quickly shat on by the beginning shrieks of late-stage capitalism.
It was like working in the record stores was my trying to keep holding onto the dream, our generation's dream...John's dream of world peace (along with my dream of being a working musician) died a pitiful death by the end of 1986.
What followed was nothing but a series of Jobs I Hated, and the beginnings of the true Jonesing for the life we'd been promised, because we didn't get the raises, the pensions, the house, the car, boat and camper, none of that shit for us. A life of being a low-paid, no-insurance drub, destined to be a life-long renter, unless a financial miracle happens.
So when people ask why we (as a generation) hate Ronald Reagan so much, let's just say I'm with Bugs on this one.
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If you'd like an essay-formatted version of this post to read or share, here's a link to it on pluralistic.net, my surveillance-free, ad-free, tracker-free blog:
One of my favorite rhetorical and analytical moves is joining things together (showing that two different, seemingly unrelated ideas are aspects of the same phenomenon) and taking them apart (resolving a paradox by demonstrating that what appears to be one, contradictory thing is actually two different things that have been lumped together).
"Taking things apart" is a very useful framework for understanding AI. How do we resolve the (seeming) paradox that some skilled workers report wonderful results from their work with AI, while others are full of dire warnings about the lurking defects in their AI-assisted outputs? Simple: the first group are "centaurs" (humans who are assisted by machines) and the second are "reverse centaurs" (humans who have been pressed into service as peripherals for machines):
What are we to make of the people who've been fired by bosses who replaced them with AI, in light of the fact that AI is demonstrably not able to do their (former) jobs? Again, it's simple if you separate out two distinct phenomena: "AI can do your job" is the first. The second is: "Your boss is a credulous dolt who is infinitely horny for replacing lippy workers with pliable machines, which made him an easy mark for an AI salesman who convinced him to fire you and replace you with an AI that can't do your job":
This is also a useful move for understanding the AI investment bubble. It's not just billionaires who don't think other people are as real as they are and consequently their jobs can be done by chatbots. It's also billionaires who believe that bosses can be sold AI and don't care if the AI is defective, because that's your boss's problem after he buys the AI and fires you. They don't have to believe in AI in order to think it's a good investment: like an investor betting that Joe Rogan can sell millions of dollars' worth of peptides to desperate young men, they are assessing the sales potential, not the merits of the thing for sale:
https://pluralistic.net/2026/08/03/andor/#either
As useful as "taking things apart" is, "putting things together" is also a very important technique for assessing, critiquing and improving AI. In a stellar essay entitled "Temperature Zero for Culture: Why Everything Is Starting to Look the Same" by the data scientist Lauren Leek, we get a top-notch example of "putting things together":
Leek's essay is one of those fabulous, wide-ranging, cross-disciplinary pieces, touching on urban design, music trends, synthetic LLM crowds, Netflix recommendation algorithms, and several other subjects, all seeking to resolve a(nother) (seeming) paradox: how is it that we have so much potential variety, but everything is so manifestly the same?
The answer is complicated and nuanced, but Leek's foundational point is that in a data-driven society, "predictions" are self-fulfilling prophecies. As Leek puts it: "Once prediction shapes the choices in front of us, we lose the ability to tell the difference between what people wanted and what the system made easy to want."
This is a pervasive issue across many domains. Leek says that economists call it "performativity," while machine learning researchers call it "model collapse" and urbanists call it "placelessness."
"Performativity" describes how, once a market has been modeled by economists, that model becomes the foundation for economic policy, which pushes the market to conform to the model:
"Model collapse" describes how machine learning models that are trained on their own predictions become incredibly bland, with all variety disappearing from the system's predictions:
This is hugely consequential: it's why bias proliferates through predictive policing algorithms: train a model with data from racist stop-and-frisks and it will predict that all the weapons and drugs in a city are to be found in Black and brown peoples' pockets. Turn those predictions into recommendations telling cops where to go look for weapons and drugs and they will double down on racist stops, producing even more biased training data, which turns into still more bias in the predictions:
"Placelessness" is the urbanist's name for "when everywhere optimises toward the same template." I think of it as Flinstones Syndrome, where the same background is looped behind Fred and Barney as they drive through Bedrock. In New York City, it's Citibank-bodega-Chipotle-Walgreens; in the Chicago suburbs, it's the strip malls with a Chili's, a gas station, and a big box store.
Leek proposes that these are all expressions of the same underlying phenomenon, a failure mode of data science that takes a world of "granular personal data" and arrives at a world where "personalisation produc[es] more sameness."
To these excellent examples, I'd add another one, from the world of monetary policy: Goodhart's Law, which holds that "When a measure becomes a target, it ceases to be a good measure":
https://en.wikipedia.org/wiki/Goodhart%27s_law
Goodhart's Law captures a wide variety of phenomena. When Google first deployed Pagerank, they showed that by counting the inbound links to all the pages on the web, you could extract a signal about which pages were most important (because there was no reason to link to a page unless you found it noteworthy).
But once Pagerank became the dominant means by which web users found pages, counting links stopped being useful: first, because people used Pagerank to find the best pages and link to them, making it impossible for new pages to get the inbound links needed to supersede incumbent pages; and second, because it's easy for fraudsters to create inbound links for low-quality pages in bulk, once there's a reason to do so.
Counting inbound links was a world-beating retrospective way of predicting which page would best match a searcher's query, but once it shaped the world it sought to analyze, it ceased to be a good prospective way to predict which page would best match your queries.
Leek is a brilliant data scientist and an even better science communicator, with a knack for crisp, readily understood explanations. How can a world of granular, highly varied data turn into a world of homogeneous choices? Simple: start with a set of items ("cuisines, genres, shop types") and a standard algorithm for sorting them. Let users choose from those recommendations. The mode (average) of those choices "gets shown more, so it gets picked more, so the model grows more confident the mode is what people want, and the tails starve." Run this for a few rounds and the evenly distributed catalog of choices "collapses onto one dominant option."
This is intrinsic in the choices we make in designing recommendation algorithms, tilting them towards the likelihood of a successful recommendation. A recommender that wants to succeed every time will make the safest possible recommendations, "so an algorithm that is uncertain about you, and it is always at least a little uncertain, hedges toward the average."
Then she busts out a beautiful, perfect little statistics aphorism: "Personalisation under a standard loss function is regression to the collective mean with extra steps." That is to say, "regression to the mean" (the tendency of varied things to become more standardized) cannot be avoided with the standard personalization algorithm. That algorithm is going to play it safe, showing you things that are broadly palatable, and because your choices are constrained to the average, you will choose average things.
This is how recommendation systems – and other analytical tools that produce predictions that are then turned into action – force so many diverse phenomena (streets, markets, media recommendations) into sameness. The fact that these recommenders are self-fulfilling prophecies means that "they don't have to be right," only "listened to."
This explains the sameness of so many of London's high streets. Leek examines 640 shopping streets, characterizing 18,000 food places spread out across them, flagging all the chain restaurants. Her analysis shows that any two London streets will, on average, share about half of their "food profile."
Obviously, this is most pronounced on streets with chain outlets, and it doesn't take that many chain outlets before a street's sameness shoots up: "A relatively small number of repeated names is enough to make otherwise different streets resemble one another more." So why do streets with chains resemble one another so much? Because the chains use an algorithm (weighting footfall, proximity to train stations, demographics, and competitors) to decide where to put their restaurants. If a street with a Gail's Bakery on it feels like every other street with a Gail's Bakery, that's because Gail's only puts its restaurants in places that have highly similar characteristics, measured to a high degree of accuracy and controlled by a narrow set of tolerances.
In other words, every street that feels like it should have a Gail's will eventually get a Gail's, whereupon that street will feel even more like all the other streets that have a Gail's, because it will share one more common factor with those other streets (a Gail's).
Leek points here to her earlier work on pub closures in the UK. The UK has experienced an epidemic of pub closures, with thousands of pubs disappearing since 2016:
Her research found that the biggest predictor of a pub surviving was its similarity to the median pub; which is to say that the more distinctive a pub was, the more "character" it had, the more likely it was to close. Pubs that are different from the average pub are harder to categorize, which means they're harder for a bank manager to assess for creditworthiness or for a landlord to justify extending a long-term lease to. The algorithms used to allocate capital and real estate are also recommenders, and they also drive variety out of the system.
This same phenomenon acts on culture. In an age of music recommendation algorithms, hit songs are changing; today's songs use a smaller vocabulary of unique words and repeat those words more often:
Vocabulary richness, distinct words relative to length, has fallen by more than a quarter since the early 1960s, while the share of repeated lines has climbed by nearly a third. The modern hit says less and says it more often, because the hook that works gets repeated.
But that's not the whole story! While each song resembles itself more ("saying less more often"), within that constraint, there's far more variety today than before: a given song's (constrained) vocabulary has grown more distinct when compared to all the other songs' vocabularies. Songs repeat the words they use, but the words repeated in songs are getting more different.
For Leek, this is the key to understanding the whole phenomenon and (more importantly) doing something about it. Music recommendation systems optimized for a singable hook, but did not optimize on any of the other variables in songs, so those dimensions acquired a broader range, even as the optmized variable got flatter and narrower.
This means that the tendency of recommenders to "flatten the world" isn't a single blunt outcome: it depends on which dimension we choose to flatten through recommendation, and who chooses to flatten that dimension.
A media recommender optimizes for consumption, showing you a tractable set of things it believes you'll watch, read or listen to. When you choose from among this limited set, the recommender takes note of that fact and shows you more of the same, pushing everything to a greige median. All the movies, books and songs you might have liked that were omitted from that initial set are excluded from being recommended in the future. The features of that media that you might have appreciated "decay out of consideration." They are never tested for desirability. The model collapses.
How badly does it collapse? Leek cites Movietweetings' data on which movies people watch: out of a million public movie ratings, half relate to the top 2% of movies in the set. There's 38,000 films in the set, but just 380 titles account for 40% of the ratings. Leek argues (persuasively) that this isn't because recommenders are good at "knowing your taste" – rather, they are good at "narrowing the menu."
Leek relates this to her work on creating LLM "personas" – synthetic populations meant to mimic the tastes and proclivities of real groups of people, that you can interrogate "before you spend money asking actual humans." While this would be useful for many applications, "it fails in exactly the way this whole essay is about."
Leek went to enormous lengths to reproduce the traits that make people interesting to study in aggregate, painstakingly replicating the ways that social connections, psychological outlook and demographic factors predict people's beliefs. The result was a set of LLM personas with "elaborate stories" about how they differed from one another, but whose survey responses about planned actions were homogeneous in a way that real populations are not.
This, Leek writes, is the same force that homogenizes other data-driven predictors. Because she'd ordered her LLM to reproduce the statistically validated relationships between different factors that predict a person's beliefs, each synthetic persona was a homogenized average. It's like the paradox of "The Average Man," where military uniforms sized to the average of all service personnel fit no one, because no one is average:
https://archive.org/details/DTIC_AD0010203
The thing is (as Leek points out) the idea that synthetic personas are a good way to understand the preferences of a real population is not a harmless delusion: it's a product that's being actively sold to governments, campaigning politicians and marketers. It's a self-fulfilling prophecy that drives governance, political campaigns and product design to the same homogeneous median that is making every shopping street in London feel the same.
This matters. As Leek writes, ecologists have long understood the importance of variety for systemic resilience: they call it "the insurance value of biodiversity." A diverse system has reservoirs of species and variation that may not be optimized for how things stand now, but that can move into niches created when things change in ways that lay waste to the previously dominant organisms. As anyone whose favorite banana went extinct can tell you, homogeneity works well, but diversity fails well:
https://en.wikipedia.org/wiki/Gros_Michel
The brittleness of algorithm-induced homogeneity is compounded by the fact that recommenders obscure the true preferences of people. If you watch two Scandinavian crime dramas after Netflix recommends them to you, it will keep showing you more Scandy crime for the next decade – even if there's another kind of programming that you'd vastly prefer (if only you knew about it). This means that decision-makers who choose which shows will get made in the future will keep on funding their safe Danish detectives, to the exclusion of whatever might emerge from the same weird attractor that produced the K-Pop Demon Hunter fortune.
Transpose this failure mode onto states, bank managers and landlords, and we see whole ranges of policies, businesses and activities that never come into existence, despite the popularity, prosperity and joy they might bring us.
But Leek doesn't end with this worrisome note. Instead, she identifies this whole thing – model collapse, placelessness, performativity, even Goodhart's Law – as an expression of one of the best-understood tradeoffs in computer science: "exploration vs exploitation":
Any system learning from feedback has to divide its effort between exploiting what already scores well and exploring options it hasn’t tried, in case they’re better.
Computer scientists have long understood that focusing on exploitation to the exclusion of exploration is a trap that locks you into "the first decent option" so you can never discover the best one.
Which means that this algorithmic homogeneity has a well-understood corrective: "forcing exploration back in." The problem is that markets hate this kind of exploration. A company that lives and dies by how many clicks it gets is never going to sacrifice 20% of its traffic by showing its users weird, untested options that score worse than the median because these weird things have never had a chance to prove that they are desirable.
This is a classic market failure, and, as Leek points out, there are regulatory responses in the UK (the Digital Markets, Competition and Consumers Act) and the EU (the Digital Services Act), both of which require the largest platforms to open up their recommendation systems, but so far, regulators have focused on "online harms" rather than variety (though the DSA does require platforms to offer algorithmic recommendations that are not based on your personal traits).
Leek identifies this willingness of states to set conditions for algorithm design as a means by which "exploration" can be forced back into the system. She's also bullish on interoperability, so that users can leave platforms with bad recommenders, without losing access to their media or social circles. As she writes, "the deepest discipline on a feed that has trapped you is the credible ability to leave it and take your data with you." I couldn't agree more:
She's less hopeful about individual responses. Demanding that you be an "adventurous consumer" is a way of letting systems off the hook. When every street has the same restaurants and every bookshop has the same books and the people in your life are all locked into one of two social media platforms, "choosing wisely" only gets you so far. Shopping isn't politics!
Leek is a superb writer. After reading this piece yesterday, I sent it to half a dozen people and then read everything else in Leek's newsletter archives. Not only is it all brilliant, but I also realized that she'd written one of the most memorable articles about cities and platforms I've read in the last year, "How Google Maps quietly allocates survival across London’s restaurants – and how I built a dashboard to see through it":
I should have added Leek's newsletter to my RSS reader when I read that last December. I've rectified that oversight! What a fantastic thinker, scientist and communicator! If she isn't being relentlessly pestered by editors and literary agents offering her a book deal, then it really does prove that the recommender systems are elevating the bland median over the thoroughly, delightfully spiky outliers.
Highlighting a few especially key parts (bolding mine):
This is also a useful move for understanding the AI investment bubble. It's not just billionaires who don't think other people are as real as they are and consequently their jobs can be done by chatbots. It's also billionaires who believe that bosses can be sold AI and don't care if the AI is defective, because that's your boss's problem after he buys the AI and fires you.
[…] Leek's foundational point is that in a data-driven society, "predictions" are self-fulfilling prophecies. As Leek puts it: "Once prediction shapes the choices in front of us, we lose the ability to tell the difference between what people wanted and what the system made easy to want.”
[…] "Personalisation under a standard loss function is regression to the collective mean with extra steps." That is to say, "regression to the mean" (the tendency of varied things to become more standardized) cannot be avoided with the standard personalization algorithm. That algorithm is going to play it safe, showing you things that are broadly palatable, and because your choices are constrained to the average, you will choose average things.
Her research found that the biggest predictor of a pub surviving was its similarity to the median pub; which is to say that the more distinctive a pub was, the more "character" it had, the more likely it was to close. Pubs that are different from the average pub are harder to categorize, which means they're harder for a bank manager to assess for creditworthiness or for a landlord to justify extending a long-term lease to. The algorithms used to allocate capital and real estate are also recommenders, and they also drive variety out of the system.
This matters. As Leek writes, ecologists have long understood the importance of variety for systemic resilience: they call it "the insurance value of biodiversity." A diverse system has reservoirs of species and variation that may not be optimized for how things stand now, but that can move into niches created when things change in ways that lay waste to the previously dominant organisms.
“…billionaires who believe that bosses can be sold AI and don't care if the AI is defective, because that's your boss's problem after he buys the AI and fires you…”
↓
“…[sellers] who believe that [customers] can be sold [a product] and don’t care if [that product] is defective, because that’s [the customer’s] problem after he buys the [product]—”
fraud. you’ve literally just defined fraud.
a third of the economy is currently running on digitial snake oil peddled by billionaire tech quacks.
like. obviously everything else here is important. but tbh it all fades into the background compared to the scale of the blatant fraudulent scheme at the heart of it all.
Yes! Neither the proponents of corporate-owned AI nor the majority of opponents of AI in general are willing to say The Magic Words, formulated by tech journalist Ed Zitron as the answer to his own question about why there are no revolutionary, far-reaching if not ubiquitous AI-based solutions in the fields where it's pushed the hardest. And The Magic Words are: "none of that shit even works".
Those words destroy the sales pitch, and moreso, The Sales Pitch But As A Bad Thing™️: the Great Replacement Theories peddled to Business Idiots from corporate event stages by the likes of Mira Murati, Scam Altman or Dario Amodei find no base in reality, when the business plan is to rope in all the rubes, addict them to a product that doesn't work by allowing them to integrate it deeply with their systema, then bilk, bilk, bilk. With their precious corporate data and all the workflows trapped in the AI corporation data centers, they're effectively held hostage. This is the point that the staggering majority of people is missing.
State’s Democratic lieutenant governor bests Angie Craig in another election contest pitting a progressive against a moderate
Rachel Leingang at The Guardian:
Peggy Flanagan, Minnesota’s lieutenant governor, won the Democratic primary for US Senate in a race centered on immigration enforcement after the Trump administration crackdown turned deadly and destructive earlier this year.
Flanagan ousted congresswoman Angie Craig in yet another example of progressive campaigns toppling moderates amid an anti-establishment wave among Democrats.
“It’s time to fight for what people actually need,” Flanagan recently told the Guardian. “And so often I think Democrats are like: ‘What do we think we can win?’ Instead of: ‘What are the fights that we should be picking?’”
Flanagan will now face Michele Tafoya, a Republican former National Football League sideline reporter, in November. If she wins, Flanagan will become the first Native American woman in the Senate.
Meanwhile, the state’s gubernatorial race proved a rebuke of Donald Trump’s endorsement power as Mike Lindell, an election denier and pillow salesman, lost to Lisa Demuth, the Minnesota house speaker. Demuth will face Amy Klobuchar, a Democratic US senator, in November, with Klobuchar favored to win.
[...]
The role of immigration enforcement served as a major issue in Minnesota’s US Senate primary after the state experienced devastation, including the deaths of two residents and hundreds of deportations, when the Trump administration sent in thousands of immigration officers in early 2026 as part of Operation Metro Surge.
The lingering impacts of the crackdown had dogged Craig, who as a congresswoman voted for the Laken Riley Act, a federal law that requires immigration detention for undocumented immigrants accused of local crimes. In March, Craig said she regretted her vote on the act, but it has followed her campaign throughout her run for Senate.
Craig, meanwhile, attacked Flanagan on fraud scandals that have rocked state social services, saying the lieutenant governor had not faced accountability for any role in the scandals.
In the Minnesota US Senate race to succeed the retiring Sen. Tina Smith (DFL-MN), progressive favorite and Lt. Gov Peggy Flanagan won the Democratic Party primary over centrist Rep. Angie Craig (DFL-MN). Flanagan will face off against former NFL sideline reporter Michele Tafoya (R) in the general.
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