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There’s a Reason That Netflix’s Ad Business Has Underwhelmed
Somewhat lost in the company’s slew of firings and engagement warnings over the past few weeks was the fact that Netflix is scaling back its ad forecast for 2026 from $3 billion to $2.7 billion. That’s despite the fact that: CTV is growing by 14-15% this year This year saw a monster upfront Netflix is the biggest streamer out there this side of YouTube, and was the platform that for years brands were supposedly dying to be a part of In other words, that’s an alarming pull-back from Netflix. A Wall Street analyst I spoke with last week was blunt, calling Netflix’s ad business “disappointing.” To me, a red flag on the ad front is that Netflix has never revealed how many ad-supported subscribers it has. Because if I know anything about how big companies work, if they have a positive number that makes them look really good - they tend to share it. (How many subs does go90 have right now? We’re not saying just yet. How are ads going on ChatGPT? Oh, a billion run rate - I see). Yes, to date Netflix has shared some fuzzy ad numbers, such as the idea that the company’s ad tier ‘reaches’ 250 million users globally.’ Here’s why that figure gives me pause: a)they lead with global, and b) they don’t explain how they are calculated. (Are they based on average people in each room? Surveys of some kind?). Regardless, some insiders say that Netflix’s ad tier is scaling nicely - others not so much. Either way, one of Netflix’s biggest challenge on the ad front appears to have been its starting point. Remember, when Netflix launched ads in 2022, it had 73 million subs in the US and Canada. That’s 73 million people who were used to Netflix with zero ads. And when the ad product rolled out, the company clearly was going after new users - a limited pool of potential subscribers by default. That focus seems to have changed over time, as Netflix has tried to convert longtime subscribers to the ad tier. Historically Netflix subscribers are very sticky. But is the company’s low churn a problem? Not necessarily, according to Antenna data. Rather, Netflix’s consumer loyalty is a bit of a double-edged sword. “Netflix is the one streamer which actually has seen substantial “trade downs” but has much lower ‘win backs,’ said Antenna founder Jonathan Carson (win-backs being people who were on the ad-free plan, cancelled, then rejoined on the ad plan). “Netflix has lower churn and so has less opportunity to win back old subs with a lower cost plan option.” This all might explain why Netflix has been so slow to roll out all of these table stakes targeting options until just this week. It likely didn’t have enough users to carve up into various targets. etc. However, what really hit Netflix’s ad business was Amazon’s move a few years ago to turn on advertising for Prime Video by default. Suddenly the market was awash in premium inventory, and Netflix was playing catch-up on multiple fronts. Thus, the streaming giant appears to still be figuring out if if wants lead in ‘programmatic TV,’ or premium ‘upfront’ TV sales. “The CTV market now sorts into three businesses, and Netflix is not the leader of any of them,” wrote analyst Jack Myers. Ouch. So how does Netflix ratchet up its ads business? Get the next NFL package You’re gonna have to wait until 2030 unless one of the bigger media companies like Skydance opts out, but the easiest (not cheapest) way for Netflix to become a huge TV ads contender is to get one of the regular NFL packages, ideally Sunday afternoons. That way it can sell ads to its whole U.S. subs base overnight. Go FAST This one is risky, because to program a compelling FAST channel, you’d likely have to take some older Netflix exclusive shows and make them available for free to non-subscribers. But FASTs, for better or worse, are ad-inventory machines. Go full UGC I’m not sure I see this happening. There is something already off about Netflix carrying podcasts and shows for creators. The experience sticks out in what is such a produced, and generally non-communal environment. But Netflix could open up its platform by allowing creators to upload videos without having to be approved by the service. But that opens a whole other can of worms (policing brand safety, copyright infringement, overall quality). Make a huge RMN Deal I was going to suggest something crazy, such as ‘instead of buying Warner Brothers, Netflix should buy Target. But the company doesn’t want to be in the brick and mortar business. Yet for Netflix’s ad business to compete in an Amazon world, it needs to be able to connect ad exposures to shopping. What if, instead of letting Amazon target Netflix viewers with its DSP, Netflix inked a deal with Target, or even took control of Roundel, and created its version of the Amazon full-funnel for brands? Price us out. I hope they don’t do this, but Netflix’s price increases - while significant - haven’t been enough to push long-term customers out of the ad-free tier. But if they made those prices so prohibitive, it could push large swaths of consumers to trade down. Then again, that could also dent engagement - as binging becomes much less appealing once you bring ads into the equation. Or, they could just launch a bunch of hit shows and maybe everyone will quickly forget all this angst. More from Amazon unBoxed A few weeks ago, I recorded two podcasts live at Amazon’s unBoxed event in San Francisco. I was able to go really deep with several brand partners about how Amazon’s various data and ad products work - particularly the under appreciated Amazon Marketing Cloud. First, I talked with Laura Abadias Velilla, of the Amazon specialty agency Amerge, along with Amazon’s own Charlotte Maines, Vice President, Devices Content & Advertising, about how the two partners helped a very specific printer company Epson launch into a brand new category - high end projectors. It’s a great example of how brands connect audience data across platforms to make budget deployment decisions. Next, I chatted with Monks VP Head of Digital Investment for North America Tom Burchill about how the agency actually brought in data from Linkedin to help target B2B customers across various Amazon properties for the brand Quicken. Check it out: Check these out, or just listen:
The 'real' Spider-Man: Why Hollywood should look to the jumping spider
Imagine a new Spider-Man movie opening not with a laboratory accident involving a black widow, but with a tiny, fuzzy spider sitting quietly beneath a leaf. It has enormous eyes, extraordinary jumping ability, and a silk-producing system so sophisticated that scientists are only beginning to understand it.Meet the regal jumping spider, Phidippus regius — perhaps the most biologically convincing inspiration for Spider-Man that Hollywood has overlooked.
Bridge winners
HUNTINGTON DUPLICATE BRIDGE ASSOCIATION: Monday, Oct. 5 — Elaine Whitt and Bob Hardin, Patricia Crews and Fuzzy Harrold. Wednesday, Oct. 7 — Bennie Shields and Bob Hardin, Elaine Whitt and
Ben Hollingsworth says 'Virgin River' is 'apple pie entertainment'
The 42-year-old actor credited the success of "Virgin River" to the fact that "it is a comforting show," which he compared to "a warm fuzzy blanket, or a hug."
What could MS Legislature, politicians do to lower gas prices?
The days of the $20 tank are so far in the rearview mirror that the memories have grown fuzzy.
Inspired By My Love Of STEM, I Collaborated With 22 Teens To Write A Kids Picture Book About Science
Teen STEM student Eliana Miao wrote a picture book about the microbiome and empathy titled "The Brave Fuzzy: A Microbiome Story"
Paly students combine science, compassion in children’s book
A group of Palo Alto High School students recently published a picture book — “The Brave Fuzzy” — that is geared toward getting young children excited for science.
Madonna Sparks Health Concerns After VMAs as Sources Claim She Was ‘Wobbling Back and Forth’—‘Everything Seemed to Unravel’
The singer also reportedly "looked flushed and fuzzy."
A sold-out crowd celebrates the 2026 Fast 50 honorees
FTB Services, the franchisee of Fuzzy's Tacos and Dave's Hot Chicken, topped the 2025 Fast 50 list with 466% revenue growth over three years.
3 newsy must-reads on AI in healthcare (plus 3 bonus picks)
1.) Some clinical AI has a fuzzy ROI. That doesn’t mean it shouldn’t be reimbursed. 2.) What makes biomedical research different from other sectors where AI has been quickly adopted? 3.) Unmoved by enthusiasm, health execs are making AI decisions around organizational priorities.
Making Our Own Luck
We are cross-posting a piece that former NIH official Kris Willis wrote for Macroscience. Kris is now the President and Founder of the Woodley Park Institute, for which I’m a board member. Picking winners or hedging bets? Federal science agencies have long wrestled with the question of how to ensure their billions of dollars of grants and contracts result in maximum benefits for Americans. On the surface, it might seem that a goal-oriented, interventionist style of management would be the best policy: prioritize our most pressing problems and distribute funding accordingly.This top-down strategy creates a unique hazard, though: bad choices can send resources in the wrong direction, resulting in slow progress that impedes the delivery of tangible benefits to citizens. Moreover, if decision makers pursue an applied advance when the underlying scientific principles remain poorly understood, they risk costly failures and a loss of support for the research enterprise at large. Examples include Nixon’s 1971 War on Cancer, undertaken on the premise that a cure could be achieved in less than a decade, the drive to build an operational commercial fusion plant by the mid-1990s, and the National Plan to Address Alzheimer’s Disease, which failed to produce the effective treatments and prevention strategies promised by 2025. The contrasting laissez-faire approach is to hedge your bets. If the path to an advance is impossible to predict, then the wisest choice might be to distribute funding to as widely as possible. The main risk of this strategy is that spreading funding thinly may limit the resources available to the most promising research, resulting in the same negative outcomes as the interventionist approach: limited support for the most promising topics, slow progress, and delayed returns. In reality, at science agencies like the National Science Foundation and National Institutes of Health, program managers navigate between these two extremes, setting some funds aside to target high-priority areas and using the rest to cover the widest possible range of meritorious proposals. This middle path seems like the most rational way to proceed, but it still requires making choices. How much should be set aside for high priority topics? What should those topics be? How should we define merit? Expert opinion can provide a helpful guide, but leading experts can have remarkably different opinions about what matters and what should be prioritized. Moreover, experts and administrators alike can be risk averse, giving an edge to high-profile, well-established concepts. What’s really needed is a data-driven framework to help guide decision-making, one that doesn’t simply repackage prestige or incumbency. Recognizing breakthroughs years in advance Many breakthroughs fail to attract funding or recognition at the earliest stages of development. Katalin Karikó, for example, spent years struggling to win grants for work on mRNA that would eventually win her and Drew Weissman a Nobel Prize. Similar stories can be told about Jim Allison’s groundbreaking studies leading to the development of cancer immunotherapy, Stan Pruisner’s demonstration that prions can self-replicate without DNA, or Robin Warren and Barry Marshall’s proof that Helicobacter pylori, not stress, causes ulcers. Yet at some point, the scientific community recognizes and responds to transformative breakthroughs like these. How does that change happen? And what if science funders could identify the areas that are on the cusp of a breakthrough? Those questions led my collaborators and me to study how scientists responded to past breakthroughs. Based on patterns in that data, we developed a means of predicting future breakthroughs, using biomedicine as a proof of concept. It’s worth taking a moment to describe how our approach works. Briefly, our prediction process begins by grouping the 18 million or so peer-reviewed papers in PubMed, the authoritative database of biomedical research, into roughly 50,000 unique topics. A small subset of these topics are so widely acclaimed as breakthroughs that they have been recognized with a major prize like a Nobel or a Lasker Award. We theorized that studying the development of these outliers over time, especially before they gained acclaim, was the key to identifying future instances of them. After defining topics, we chose 21 representative breakthroughs and asked what they looked like before they won prestigious prizes. To answer that question, we re-ran our topic-mapping algorithm repeatedly, winding back the clock one year each time, stringing together the years to follow the progress of each field as it grew and advanced. We found that in each case, the years leading up to a discovery followed a pattern: a burst of papers on a new, rapidly evolving topic, many of which quickly became influential. These characteristics—the percent of papers that are new, the percentage of papers that are brought in from other topics, and the influence of each individual paper—can all be measured separately for any given topic. Together, they act as a predictive signal that is detectable an average of five years before a transformative discovery is made, and up to thirty years before the discovery receives a major prize. Scanning the current research landscape for topics that display this signal allows us to predict what work will likely produce a future breakthrough. Each year we analyzed includes four or five signals, a number that appears to have remained constant across a twenty year time frame. In their classic work on the sociology of science, Bruno Latour and Steve Woolgar established that the desire to participate in a major discovery is an important motivator for scientists as they consider whether they should take up (or abandon) a research problem. Although further work is needed, the simplest explanation for our results is that this behavior is widespread enough to detect at scale. A foundational advance, or something like it, draws the attention of scientists and causes them to change the direction of their research. They vote with their feet, staking their own reputation and careers by publishing on and citing the new idea. That upswell of interest is the foundation of our breakthrough signal. What kind of research does our model identify as a breakthrough? After building a model based on the patterns in our 21 preselected breakthroughs, we tested it by identifying all early signals of discovery from 1994 through 1997. Of the 18 signals we found in that time period, 17 can be traced forward in time to a breakthrough. Examples include super-resolution fluorescence microscopy (see the image above), the directed evolution of proteins and enzymes (Chemistry Nobel, 2018), and sequencing the human genome (National Medal of Science, 2008). The discovery of the role of leptin signaling in obesity is a particularly interesting case study. Around 1950, scientists noticed the existence of a type of lab mouse that suffered from a unique inherited form of obesity. When given the freedom to choose their own diet, these mice ate more than normal and preferred food that was high in fat. Researchers had few clues as to why the animals overate; early experiments pointed to the existence of a soluble ob factor found circulating in the bloodstream that regulated appetite, but its identity was unknown. In 1994, Jeffrey Friedman and colleagues identified and sequenced the ob gene, noting that the protein it encoded appeared to fit the profile of a circulating factor. A year later, a trio of high-profile publications demonstrated that ob was a hormone that regulated body weight and fat deposition by regulating appetite. One of these was co-authored by Friedman, who christened it leptin from the ancient Greek word for thin. Using our model, the data from 1996 make it clear this advance would become a breakthrough. Scientists had flocked to study the biology of the new hormone, generating the flurry of new papers and citations required to produce a signal. One of these demonstrated that serum leptin concentrations reflected the amount of adipose tissue in the human body, providing clinical validation of the earlier mouse studies. Before the identification of leptin, scientists were unable to point to a specific molecule that controlled appetite and adiposity, so that it was impossible to rule out non-physiological causes of obesity; its characterization fundamentally changed our concept of weight gain. In 2010, Friedman and fellow pioneer Douglas Coleman were awarded a Lasker for the discovery. Our work also identifies breakthroughs in clinical practice and behavioral and social sciences, even though these areas are less likely to draw the attention of major prize committees. One example that falls into this category is the introduction of endovascular aneurysm repair, a minimally invasive surgical procedure for the treatment of abdominal aortic aneurysm, a leading cause of death among Americans over the age of 65. Relative to the previous standard of care, it reduces in-hospital mortality by almost four-fold. In 2017, Timothy Chuter received the American College of Surgeons’ prestigious Jacobsen Innovation award for his multiple refinements to the technique. Another under-recognized breakthrough identified by our model is the development of standardized survey instruments to assess the quality of life for HIV+ patients. The antiretroviral cocktails introduced in the mid-1990s reduced AIDS mortality, but treatment came with serious side effects. The new evaluations showed that patients whose disease progressed had worse physical functioning than Americans with other chronic diseases, while those who were asymptomatic maintained a health-related quality of life on par with the overall US population. This evidence made it possible for patients and activists to argue that even in the absence of a total cure, and in spite of the side effects, alleviation of symptoms was a worthwhile priority for clinicians. My colleagues and I have failed to identify any significant recognition for the physicians and health policy experts who pioneered this advance, although in 2016, researchers called for the World Health Organization to set targets for good health-related quality of life as part of its framework to end the AIDS pandemic. From forecasting to funding How should science funders use our predictions? Any serious discussion of what we should do with this new capability needs to begin with an understanding of its limitations. We can’t (and shouldn’t) invest all of our resources in the small number of topics predicted to produce breakthroughs. Innovative new fields are born from existing ones; they rarely arise de novo. If funders don’t maintain a diverse portfolio, the breakthroughs of tomorrow will have no antecedents. Think of this as eating the seed corn, or killing the goose that lays the golden egg. There are also multiple goals of funding beyond basic scientific discovery, including translating discoveries into clinical practice, developing new technologies, supporting economic development, and training new scientists. The investments needed to accomplish these goals are likely different from those required to support emerging breakthroughs. Finally, we must be aware that we may not capture every instance of a breakthrough. The concept is fuzzy, and not every significant discovery is recognized with a major award. Further, breakthroughs are rare, meaning that our dataset is, by necessity, small. This makes formal estimates of accuracy challenging. Although our overall success rate appears to be high, any one signal may turn out to be a false positive. We found one apparent example: the development of the third generation COX-2 inhibitors, Vioxx and Celebrex, which were much celebrated in the early 2000s as effective but non-addictive pain relievers, meets all the algorithmic criteria of a breakthrough. Five years after it received FDA approval, researchers demonstrated that Vioxx was associated with serious cardiovascular complications, and the manufacturer voluntarily pulled the drug from the market. Celebrex remains available, although like other NSAIDs, it carries an FDA warning for increased cardiovascular risk. Decision makers who were overly focused on the breakthrough potential of this research might have wasted substantial resources nurturing an area that ultimately proved to be a failure—but so would those who relied on expert opinion at the time. That said, the potential benefits of funding the next mRNA vaccine or cancer immunotherapy years earlier than we might have are large. When we can’t assume the outcome of an action, it’s worth conducting an experiment. Can targeted support for areas that are poised to produce a breakthrough increase the return on scientific investment without cannibalizing future advances? With the necessary administrative infrastructure in place, conducting this experiment would be straightforward. First, identify all the breakthrough signals between 2018 and 2023, select half at random, and commit to a decade of investment. For biomedicine, a six year window should yield about 30 topics with the potential to produce a breakthrough. Providing strong support to half of these might be accomplished for $300 million a year. That’s roughly half of the recent annual budget of the NIH Common Fund or around 20% of the fiscal year 2025 budget for ARPA-H. After five years, then again at ten, compare to see which group — the one that was actively managed, or the control — produced more breakthroughs, and on what timescale. Although we used biomedicine as an example, the same experiment could just as well be run for physics, materials science, or any other discipline. Any attempt to undertake such an experiment should keep three points top of mind. First, we can predict the topic of future breakthroughs, but the people and vision for how to move the work forward are still important. In practice, this means success requires active program managers with the expertise to develop the science and the authority to do so, including by recruiting investigators. Second, we would be wise to learn from the failures of past attempts to fund transformative research. High among these are funding modestly repackaged work, confusing the genuinely novel with the merely unfamiliar, and relying too much on incumbent investigators. All of these risks can be mitigated by thoroughly and intelligently integrating high quality data into portfolio management. Finally, we need to have patience. Even with strong funding support, producing a breakthrough takes time. If supporting breakthroughs is the best way to nurture the birth of new fields, we also need to accurately describe and properly manage the other two stages, so that we invest wisely throughout the productive lifetime of ideas and divest once they reach the point of diminishing returns. We would benefit from more study of all of these phases, especially the transition from one to another. More research could answer questions like: When does a breakthrough become an established field? How do the availability of funding, the size and characteristics of the available workforce, and the accumulation of evidence that disagrees with prevailing models affect a field’s decline? Exactly how do old ideas give birth to new ones, and can we speed up the process? The more we understand about the dynamics of scientific progress, the better we can maximize the odds that our curiosity leads to results. I’d call that finding a way to make our own luck.
Bumblebees have tiny brains but they can solve problems like chimps and elephants
New research suggests the fuzzy insects may be capable of spontaneously solving problems the way animals with much larger brains do.
Celebrity chef reveals No. 1 mistake sabotaging your weight loss: 'Fuzzy math'
Rocco DiSpirito reveals the No. 1 mistake people make with healthy eating and weight loss, saying there is no one fix or cure that works for everyone.
Scientists Say That Time Itself Can Be in Two States at Once
If gravity’s the reason that quantum wave functions collapse, then time must be “fuzzy.”
Physicists just found a tiny flaw in time itself
Physicists are rethinking one of quantum mechanics’ biggest puzzles: how fuzzy possibilities become definite reality. New research suggests that spontaneous “collapse” processes—possibly linked to gravity—could subtly blur time itself. This wouldn’t affect clocks we use today, but it reveals a hidden limit to how precise time can ever be. The findings open a new path toward uniting quantum physics with gravity.
‘Water‑sowing’: When science and ancestral knowledge join forces to fight drought in the Venezuelan Andes
This ancestral practice combines reforestation, soil conservation, and the protection of frailejones — the tall, fuzzy Andean plants that harvest water — to capture fog and rain, recharge aquifers, and ensure the flow of rivers
Dead insects. Fuzzy white growth. Expired foods. Worst SC Midlands March restaurant inspections
There are plenty of great restaurants in the Midlands, but many don’t meet the state’s health and safety standards.
New study favors 'fuzzy' dark matter as the backbone of the universe — contrary to decades of research
New research using a space-time phenomenon predicted by Einstein presents evidence that the invisible backbone of the universe may be much "fuzzier" than we realized.