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AI skeptics have found a new way to express their disdain for the creeping presence of artificial intelligence: through slurs. Out on the streets and in stores, people have begun harassing robots they encounter in the wild. (Anyone else feel a bit sorry for the robot?) Online, the internet has revived a Star Warsinspired insult, clanker, with Google Trends data showing a spike in searches for the term in early June. @semdenpriv original sound – semdenpriv POV: Me at the clanker rally in 2088, one TikTok user joked. Keep your oily soulless clanker hands away from my delicious human food, another X user wrote in response to a clip of Elon Musks Optimus robot dishing out popcorn at the Tesla Diner (not a sentence I ever thought Id write). Keep your oily soulless clanker hands away from my delicious human food https://t.co/DXF7JNKD0W— EckhartsLadder (@EckhartsLadder) July 20, 2025 The term has also been picked up by politicians. Sick of yelling ‘REPRESENTATIVE’ into the phone 10 times just to talk to a human being? Sen. Ruben Gallego (D-AZ) posted on X last month. My new bill makes sure you dont have to talk to a clanker if you dont want to. Sick of yelling REPRESENTATIVE into the phone 10 times just to talk to a human being? My new bill makes sure you dont have to talk to a clanker if you dont want to. pic.twitter.com/9aUv478gSP— Ruben Gallego (@RubenGallego) July 30, 2025 While some direct their insults at the technology itself, others target those using AI systems. On one thread, suggestions for users of the xAI chatbot Grok included Grokkers, Groklins, and Grocksuckers. Meanwhile, on TikTok, someone coined sloppers for people becoming increasingly overreliant on ChatGPT. @intrnetbf shoutout to Monica. Incredible command over the English language original sound – intrnetbf The trend reflects a broader mood. Concerns about AI among U.S. adults have grown since 2021, according to the Pew Research Center. More than half (51%) say they are more concerned than excited about the technologys rise, with worries ranging from AI taking away jobs to chatbot addiction. Still, some see embracing new slurseven those aimed at robotsas problematic, especially when they echo existing racial slurs or stereotypes. @thebrookboys This bout to be the biggest fear for all Dads in year 2050 #meme #clanker #robo Bell Sound/Temple/Gone/About 10 minutes(846892) – yulu-ism project Others simply fear theyll regret their words later. As one X user wrote: I dont want to have to look a robot in the eye in fifty years and be like, you dont understand it was a different time star wars did give us a slur for robots (clankers) but i dont use it bc i dont want to have to look a robot in the eye in fifty years and be like you dont understand it was a different time— anna !!! (@frogs4girls) July 20, 2025
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E-Commerce
In the late 2010s, cultured meat was everywhereand yet nowhere. From Reddit to major magazine covers, articles touted the latest advances in “lab-grown meat,” promising cruelty-free, environmentally friendly steaks at your local supermarket. The hype was palpable. One 2019 report predicted cultured meats would halve the number of cows on the planet by 2030, disrupting the world’s oldest industry by delivering ethical meat with negligible environmental impact that tasted identical to traditional meatand at a fraction of the price. [Photo: Vow] That promise of rapid disruption terrified conventional animal agriculture stakeholders. Under pressure from these livestock constituents, lawmakers in multiple states have banned this new protein source entirely. Florida and Alabama passed bans in 2024, with more states following. Indiana imposed a manufacturing moratorium with steep fines, Nebraska prohibited its production and sale, and Montanas governor signed legislation to ensure consumers could “continue to enjoy authentic meat.” In June, a Texas ban became law, with the state’s agriculture commissioner touting the “God-given right” to pasture-raised meateven though the vast majority of what Americans eat comes from industrial feedlots. But here’s the irony: Lawmakers are fighting a version of cultured meat that never materialized. Today, while you can eat cultured meat at more than 60 venues in Singapore and Australia, and cultured seafood at two restaurants in the U.S. at the time of this writing, it’s far from the rapid disruption that was forecasted. More than a decade after the world’s first cultured hamburger was announced, the hype has virtually disappeared.The reality of how and why this all transpired is complicated. However, we would argue that what we’re witnessing isn’t industry failure, but the natural evolution of a transformative invention finding its true market fit. Cultured meat technology works; what needed adjustment were the timelines and business models that promised too much, too quickly, and to replicate conventional meats that people already enjoy en masse.Rather than viewing this as a setback, some in the industry are discovering something potentially more valuable: sustainable, scalable pathways to market that don’t require displacing existing agriculture but can grow alongside it. As the industry turns the page to a new chapter, once uncertain regulatory pathways are now established in multiple countries. [Photo: Vow] The technology itself continues to advance. Production yields are improving, costs are declining, and new species beyond traditional livestock are proving viable for cultivation.More importantly, early market success demonstrates genuine consumer appetite. In Singapore, where cultured meat has been available the longest, restaurants report strong repeat customers and growing demand. In Australia, where cultured meat became available at dozens of restaurants in recent weeks, initial sales and demand for the items are taking off. Forged Cultured Japanese Quail Whipped Pate [Photo: Vow] This suggests cultured meat purveyors arent just scratching a theoretical itch, but delivering real value and excitement that consumers recognize and seek out.This reality is leading to a strategic pivot that may actually benefit both the industry and consumers: innovation over imitation. Rather than trying to perfectly replicate a chicken wing or rib-eye steakproducts that traditional animal agriculture already produces and consumers are accustomed tocompanies that are finding success are creating entirely new culinary experiences that excite chefs and diners alike. Forged Cultured Japanese Quail Foie Gras [Photo: Vow] Take Japanese quail, a species that demonstrates cultivated meat’s unique advantages. Traditional quail foie gras is impossible to produce commerciallythe birds are so petite that conventional methods are prohibitively labor-intensive, and the production process itself remains controversial. Japanese quail, however, proves remarkably well-suited for cultivation technology, enabling the creation of previously undoable delicacies like foie gras, whipped pâté, and even edible tallow candles. Forged Cultured Japanese Quail Tallow Candle [Photo: Vow] And Vow can make a lot of it. The company recently completed the largest cultured meat harvest in history: more than one metric ton of quail. And it projects it will have the capacity, by the end of 2025, to harvest up to 130 metric tons annually. While that’s still minimal compared with the 12.29 million metric tons of beef American farmers produced in 2023 and 2024, it is proof that cultured meat can offer consumers genuinely new choices and advance consumer acceptance. Its an illustration of how the industry can position itself as expanding culinary possibilities while avoiding potential conflicts with traditional agriculture.Rather than letting politicians dictate what should be on our plates in order to protect incumbent industries, we should trust consumers to decide for themselves. When given the freedom to choose, consumers are embracing these innovations as exciting additions to culinary experiences, the evidence suggests. Thats a decision best left to diners, not lawmakers.
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E-Commerce
As a partner at Theory Ventures, a VC firm built around deep technology and market research, I spend my days swimming in information: academic papers, market reports, interview notes, and written analyses. Our job is to synthesize these data points into a nuanced perspective to inform our investment decisions. Reading the hype online, its tempting to think you can just delegate anything to AI. But for something so critical to our job, we dont just want it done, we need it to be excellent. How much can AI really do for us? In this piece, I will share: How we structure instructions to get the best analysis out of an AI model Where I critically intervene and rely on my own thoughts How you can get an AI to mirror the way you write When relying on an LLM you often get something that only seems good at first glance: often the AI has missed details, or an important nuance. And for this core part of my job, decent isnt enoughI need output to be excellent. This AI accuracy gap creates a painful cycle where you spin in circles, trying to re-prompt the system to get what you want until youre essentially left rewriting the entire output on your own. In the end, its unclear if AI actually helped at all. The more effective approach is understanding how you (the human) do the thinking and leave writing (i.e., formatting and synthesis) to the LLM. This simple separation is what elevates AI-augmented workflows from decent to exceptional. Heres an example of how we build those kinds of workflows at Theory Ventures, and how you can too. Well illustrate an example with the automation of our internal market research reports. Step 1: Define the thinking process Prepare a document with very detailed instructions on the underlying analysis/construction you seek to achieveclearly outline the context & goals, then dive into all of the details on how you deconstruct a broad analysis: the specific questions you would ask, follow-up sub-questions, how they should be answered with data, and key callouts or exceptions. You can use an AI assistant to help you generate a first draft of this, sharing completed documents and asking it to deconstruct the analysis. But these instructions are critical, so its important to finish writing it by hand and continue to update it over time when you tweak your analysis. Example analysis instructions included in the prompt (note: the full instructions will typically be 2 to 10 or more pages long) Analyze the underlying market structure: Is it fragmented or consolidated? Why? (e.g., high specialization needs, regulatory barriers, network effects, legacy tech debt). How is fragmentation changing over time, and is it different across market segments? Use the following data sources and analyses: . . . Evaluate key market dynamics: What are the typical switching costs? How prevalent is tech debt? What are the typical sales cycles and buyer behaviors? How do incumbents maintain their position (moats)? Use the following data sources and analyses: . . . Step 2: Lay out your human-led analysis Provide your primary analysis, along with raw notes and instructions to the AI. We set our systems up so they require the user to provide their key takeaways and analysis to guide the system towards whats most importanthighlighting areas to focus on, key opportunities, and potential concerns. These are typically four to five detailed bullet points of two to four sentences each. This is the crux of the analysis and should therefore never be AI-generated. Example key takeaways provided to the system: This market has historically been small and fragmented without major software providers. We expect it will grow dramatically, primarily through currently automating labor spend and consolidating a set of point solutions. The underlying demand for this capability will also increase with XYZ challenges. We feel very confident in these two growth levers. Theres substantial concentration at the upper end of the market. Major platforms control around X% of the market and have all invested heavily in their own technology. But below the top-n largest players, there is a healthy cohort of medium-large buyers that have the scale to need this solution but dont want to build it. We think this is sufficient to build a sizeable company, although market concentration and build versus buy remains a key long-term risk. Step 3: Run an interactive Q&A to hone the analysis This dialogue is the most interesting and fun step: Have the system generate questions to clarify the contours of your analysis. Based on the primary analysis, along with the notes and general instructions, the system asks questions about things that either werent clear or had conflicting information/instructions. This helps sharpen the analysis and gives the user the opportunity to share more of their thought process and guidance. Example Q&A: Q from the AI: You said that major platforms have invested heavily in this technology, but conversations with some of those companies indicated an excitement to buy. Do you think that will be common, or were they exceptions? A from the human: Good point. I do think that many of them will buy eventually, but because theyve built a lot of technology internally they are more likely to need a new platform only for certain components, versus buying an end-to-end system. And the very largest companies (top three to five) will build everything in-house. Step 4: Share past work to match tone, not ideas Use previous examples of your work to replicate tone and style only after the scaffolding work is done. Most people skip immediately to this step, but we found (and research shows) that providing finished examples is most useful just to match tone and writing style, as opposed to shaping the analysis itself. In researching the best AI-native products, weve seen that practically all of the work goes into defining the thinking and analysis portion of the problemdetailed instructions, guidelines, orchestration, and toolingso the AI system knows what it should do and just executes on it. At Theory Ventures, weve started to mirror the same system by developing highly-constrained, human-in-the-loop workflows that direct the analysis, leaving the LLM to execute basic information extraction and synthesis. Thats how weand our AI systemshave started working smarter. Not by asking AI to think for us, but by helping it think better.
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E-Commerce
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