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Amazon routinely ranks its own products ahead of items from competitors, the Federal Trade Commission (FTC) said in an antitrust lawsuit—echoing the findings of a 2021 investigation by The Markup. Amazon’s search results are biased in the company’s favor, even when it knows competing products are of superior quality, the FTC added, helping the online retailer extract what the agency called “enormous monopoly rents from everyone within its reach.” The FTC’s search allegations help form the basis for a broader lawsuit accusing Amazon of stifling competition by punishing sellers whose goods can be bought more cheaply elsewhere, and by requiring sellers to use Amazon’s fulfillment service in order to offer faster “Prime” shipping. “Amazon has used a set of punitive and coercive tactics to unlawfully maintain its monopolies,” FTC Chair Lina Khan said in a written statement. The agency, along with 17 state attorneys general, filed the suit in a federal district court in Seattle. The complaint comes nearly two years after a Markup investigation found that products from Amazon-owned brands or exclusive to Amazon ranked ahead of other items in search results, including in cases where competitors had higher customer ratings and more reviews. The investigation found that knowing only whether a product was from an Amazon brand could predict if it got the top search rank 70 percent of the time, making that factor more important than a product’s star rating or review count. When an Amazon brand took the top search spot, it was usually labeled as “featured from our brands,” and Amazon at the time told The Markup it considered such “featured” results to be “merchandising placements” rather than search results or advertising. It also denied that it favored its own brands in the results. Amazon did not answer questions on the FTC’s search ranking claims, instead pointing to a statement from Amazon Senior Vice President, Global Public Policy and General Counsel David Zapolsky, who wrote on Tuesday that the suit was “wrong on the facts and the law, and we look forward to making that case in court.” “The practices the FTC is challenging have helped to spur competition and innovation across the retail industry, and have produced greater selection, lower prices, and faster delivery speeds for Amazon customers and greater opportunity for the many businesses that sell in Amazon’s store,” he also wrote. In its suit, the FTC said Amazon is “stacking the deck” in search results against competitors by “burying organic content under recommendation widgets, such as the ‘expert recommendation widget,’ which display Amazon’s private label products” over others. Such recommendations used to exist entirely outside of search, the agency said, displaying on product detail pages or on pages that delivered personalized suggestions to customers, to replenish previously purchased products, for example. It is possible the suit contains additional specific allegations about Amazon’s search rankings, since the rankings’ section in the public version of the complaint contains several redacted paragraphs. FTC spokesperson Victoria Graham said portions of the complaint were redacted because they contain non-public information. Robert Gomez, an Amazon seller who criticized the company’s “unfair advantages” in The Markup’s 2021 investigation, said it has only grown harder on Amazon in the ensuing years to turn a profit on his Kaffe line of coffee products like grinders and brewers. Sponsored results increasingly dominate the top of search results, he said, making pricey ad buys crucial, and he said Amazon has locked him out of the all-important “buy box,” through which it distributes sales from product detail pages, for a product of his that Walmart sells for $17.88—a lower price than he said he could afford to offer on Amazon due to additional costs he must pay there. Gomez said that while he’s encouraged by the FTC’s suit, since it will bring further attention to what he considers Amazon’s uneven marketplace, he said “I’m not holding my breath” for an outcome that helps sellers like him. Even if the agency can roll back the practices it considers unlawfully anticompetitive, it would leave untouched other practices Gomez said cut into the margins of independent sellers, like Amazon selling sponsored search results or selling certain brands directly, meaning those brands don’t have to act as their own seller in the Amazon Marketplace and thus end up giving a smaller cut to Amazon. “These anticompetitive practices continue to happen,” Gomez said, “and now more than ever, we are being affected by them.” Update, Sept. 28, 2023 This story has been updated to note that Amazon did not answer specific questions about FTC’s search ranking claims. Ryan Tate, Investigative Editor Design and Graphics Gabriel Hongsdusit Engagement Maria Puertas Copy Editing and Production Sabrina Toppa Editing Sisi Wei Also published here Amazon routinely ranks its own products ahead of items from competitors, the Federal Trade Commission (FTC) said in an antitrust lawsuit—echoing the findings of a 2021 investigation by The Markup. Amazon’s search results are biased in the company’s favor, even when it knows competing products are of superior quality, the FTC added, helping the online retailer extract what the agency called “enormous monopoly rents from everyone within its reach.” called The FTC’s search allegations help form the basis for a broader lawsuit accusing Amazon of stifling competition by punishing sellers whose goods can be bought more cheaply elsewhere, and by requiring sellers to use Amazon’s fulfillment service in order to offer faster “Prime” shipping. lawsuit “Amazon has used a set of punitive and coercive tactics to unlawfully maintain its monopolies,” FTC Chair Lina Khan said in a written statement. The agency, along with 17 state attorneys general, filed the suit in a federal district court in Seattle. The complaint comes nearly two years after a Markup investigation found that products from Amazon-owned brands or exclusive to Amazon ranked ahead of other items in search results, including in cases where competitors had higher customer ratings and more reviews. The investigation found that knowing only whether a product was from an Amazon brand could predict if it got the top search rank 70 percent of the time, making that factor more important than a product’s star rating or review count. a Markup investigation found When an Amazon brand took the top search spot, it was usually labeled as “featured from our brands,” and Amazon at the time told The Markup it considered such “featured” results to be “merchandising placements” rather than search results or advertising. It also denied that it favored its own brands in the results. Amazon did not answer questions on the FTC’s search ranking claims, instead pointing to a statement from Amazon Senior Vice President, Global Public Policy and General Counsel David Zapolsky, who wrote on Tuesday that the suit was “wrong on the facts and the law, and we look forward to making that case in court.” wrote on Tuesday “The practices the FTC is challenging have helped to spur competition and innovation across the retail industry, and have produced greater selection, lower prices, and faster delivery speeds for Amazon customers and greater opportunity for the many businesses that sell in Amazon’s store,” he also wrote. In its suit, the FTC said Amazon is “stacking the deck” in search results against competitors by “burying organic content under recommendation widgets, such as the ‘expert recommendation widget,’ which display Amazon’s private label products” over others. Such recommendations used to exist entirely outside of search, the agency said, displaying on product detail pages or on pages that delivered personalized suggestions to customers, to replenish previously purchased products, for example. It is possible the suit contains additional specific allegations about Amazon’s search rankings, since the rankings’ section in the public version of the complaint contains several redacted paragraphs. FTC spokesperson Victoria Graham said portions of the complaint were redacted because they contain non-public information. public version Robert Gomez, an Amazon seller who criticized the company’s “unfair advantages” in The Markup’s 2021 investigation, said it has only grown harder on Amazon in the ensuing years to turn a profit on his Kaffe line of coffee products like grinders and brewers. Sponsored results increasingly dominate the top of search results, he said, making pricey ad buys crucial, and he said Amazon has locked him out of the all-important “buy box,” through which it distributes sales from product detail pages , for a product of his that Walmart sells for $17.88—a lower price than he said he could afford to offer on Amazon due to additional costs he must pay there. through which it distributes sales from product detail pages Gomez said that while he’s encouraged by the FTC’s suit, since it will bring further attention to what he considers Amazon’s uneven marketplace, he said “I’m not holding my breath” for an outcome that helps sellers like him. Even if the agency can roll back the practices it considers unlawfully anticompetitive, it would leave untouched other practices Gomez said cut into the margins of independent sellers, like Amazon selling sponsored search results or selling certain brands directly, meaning those brands don’t have to act as their own seller in the Amazon Marketplace and thus end up giving a smaller cut to Amazon. “These anticompetitive practices continue to happen,” Gomez said, “and now more than ever, we are being affected by them.” Update, Sept. 28, 2023 Update, Sept. 28, 2023 This story has been updated to note that Amazon did not answer specific questions about FTC’s search ranking claims. Ryan Tate, Investigative Editor Ryan Tate , Investigative Editor Ryan Tate Design and Graphics Gabriel Hongsdusit Gabriel Hongsdusit Gabriel Hongsdusit Engagement Maria Puertas Maria Puertas Maria Puertas Copy Editing and Production Sabrina Toppa Sabrina Toppa Sabrina Toppa Editing Sisi Wei Sisi Wei Sisi Wei Also published here here

According to a FTC Lawsuit, Amazon Ranks Its Own Products First

From powering a chatbot to crunching information in massive data centers, artificial intelligence (AI) is growing everywhere. However, one major problem remains at the center of it all — companies are running out of chips to feed the technology. Several factors are contributing to this shortage, and organizations may be looking at serious bottlenecks in the future. So, what can supply chain managers do before things become more complicated? It starts with understanding some of the leading circumstances fueling the AI chip shortage. 1. Raw Material Shortages AI chips depend on rare and hard-to-source materials, such as gallium, germanium, cobalt, and tungsten, for their semiconductors. These minerals are in short supply, and many come from politically volatile regions or countries with tight control over them. For example, China has a 60% share of worldwide rare earth elements output and has already threatened export restrictions in response to escalating trade tensions. Mining and processing these minerals is not easy either. Environmental regulations, labor conditions, and a lack of domestic refining capacity in many Western countries all contribute to a slower supply chain. That bottleneck keeps fabricators from operating at full capacity, delaying chip production further. Still, these resources have become essential to the production of chips, and demand will only keep growing. According to market projections, the global semiconductor materials market may growfrom $72.03 billion in 2025 to $96.24 billion by 2032. As the need for raw materials skyrockets, the competition for these resources will determine who gets to build the future — and who gets left behind. 2. Increased Demand for AI Computing Power Since the 2022 breakthrough in generative AI, every company wanted in on the action. From scrappy startups to cloud titans, diverse companies vied to build the next big thing in artificial intelligence. With tools like ChatGPT gaining popularity, the demand for AI computing power has exploded. Suddenly, every application needed machine learning. Explosions like this have consequences, especially for the chips doing all the heavy lifting. AI models — especially the massive large language models — require advanced GPUs and AI accelerators. These components are expensive to make and even harder to scale. As model sizes expand into the billions of parameters, so does the appetite for computing power. This increases the need for serious infrastructure. Major players like AWS, Google Cloud, and Microsoft Azure have already boosted their capital spendingby around 36% in 2024, with most of it going toward AI infrastructure. As the need for chips rises, there is not nearly enough supply. That’s where the center of the shortage lies. The supply chain can't handle this kind of spike. With AI now baked into everything, lead times are growing, and production backlogs are piling up. Yet, with the global AI chip market expected to hit $89.6 billion by 2029, it will be even more challenging for supply to keep pace with accelerating demand. 3. Geopolitical Tensions and Trade Restrictions Semiconductors are more than tech components — they’re geopolitical currency. The U.S.-China trade war has turned chipmaking into a chess game, and AI chips are now one of the most contested pieces. To curb China’s access to advanced technologies, the U.S.has imposed export restrictions on high-end chips and the equipment used to manufacture them. Nvidia — one of the biggest names in the market — has already taken hits. The company held an estimated 90% share of China’s AI chip market before the U.S. imposed sanctions. Yet, it’s more than about individual companies. The entire semiconductor ecosystem is wildly globalized. A single AI chip might involve design in California, fabrication in Taiwan, materials from Japan, and assembly in Malaysia. When governments throw up barriers, the ripple effects can paralyze entire supply chains. 4. AI-Driven Demand from PCs and Smartphones AI is now reaching into consumers’ pockets. Until recently, most of the heavy AI lifting happened in the cloud. However, chipmakers are now bringing AI capabilities into smart devices, creating an even higher demand for AI chips. For example, smartphones are quickly evolving into mini AI machines. Their capabilities range from real-time language translation to generative image editing — features that require powerful on-device processing. Fitting energy-efficient chips into already thermally constrained devices can be a tough engineering challenge and an even tougher supply chain issue. However, AI smartphone shipments are growing exponentially, with market forecasts predicting them to growfrom 234 million units in 2024 to more than 900 million by 2028. While this happens, enterprise and cloud-scale AI demand is still spiking, so the use cases are growing. Strategic Responses to Mitigating Shortages Supply chain managers can take steps to reduce the potentially negative impacts of AI chip shortages. Here’s how they may play smarter in a chip-starved market: Diversifying suppliers: Relying on a single vendor can create setbacks. That’s why spreading sourcing across multiple regions and vendors is important.
Strengthening supplier relationships: Businesses must treat suppliers like strategic partners rather than vending machines. From collaborative planning to long-term contracts, various tactics can give companies a leg up when things are tight.
Investing in inventory buffering: Stocking inventory based on usage data and realistic forecasting can buy time without draining cash flow.
Enhancing demand forecasting with AI: Ironically, AI can solve the chip shortage. With predictive analytics, organizational leaders can anticipate surges, identify vulnerabilities, and plan accordingly.
Exploring domestic or nearshore manufacturing: Shorter supply chains can support smoother operations. If reshoring isn’t an option, nearshoring can offer a solid middle ground for critical components. Solving the AI Chip Shortage AI may be booming, but there are not enough chips to keep pace with demand. With each issue occurring simultaneously, they pull the semiconductor supply chain in every direction. For supply chain managers and tech leaders to stay ahead, there will be a growing matter of planning smarter and building strength before it begins. From powering a chatbot to crunching information in massive data centers, artificial intelligence (AI) is growing everywhere. However, one major problem remains at the center of it all — companies are running out of chips to feed the technology. Several factors are contributing to this shortage, and organizations may be looking at serious bottlenecks in the future. From powering a chatbot to crunching information in massive data centers, artificial intelligence (AI) is growing everywhere. However, one major problem remains at the center of it all — companies are running out of chips to feed the technology. Several factors are contributing to this shortage, and organizations may be looking at serious bottlenecks in the future. So, what can supply chain managers do before things become more complicated? It starts with understanding some of the leading circumstances fueling the AI chip shortage. 1. Raw Material Shortages AI chips depend on rare and hard-to-source materials, such as gallium, germanium, cobalt, and tungsten, for their semiconductors. These minerals are in short supply, and many come from politically volatile regions or countries with tight control over them. For example, China has a 60% share of worldwide rare earth elements output and has already threatened export restrictions in response to escalating trade tensions. has a 60% share has a 60% share Mining and processing these minerals is not easy either. Environmental regulations, labor conditions, and a lack of domestic refining capacity in many Western countries all contribute to a slower supply chain. That bottleneck keeps fabricators from operating at full capacity, delaying chip production further. Still, these resources have become essential to the production of chips, and demand will only keep growing. According to market projections, the global semiconductor materials market may grow from $72.03 billion in 2025 to $96.24 billion by 2032. As the need for raw materials skyrockets, the competition for these resources will determine who gets to build the future — and who gets left behind. from $72.03 billion in 2025 from $72.03 billion in 2025 2. Increased Demand for AI Computing Power 2. Increased Demand for AI Computing Power Since the 2022 breakthrough in generative AI, every company wanted in on the action. From scrappy startups to cloud titans, diverse companies vied to build the next big thing in artificial intelligence. With tools like ChatGPT gaining popularity, the demand for AI computing power has exploded. Suddenly, every application needed machine learning. Explosions like this have consequences, especially for the chips doing all the heavy lifting. AI models — especially the massive large language models — require advanced GPUs and AI accelerators. These components are expensive to make and even harder to scale. As model sizes expand into the billions of parameters, so does the appetite for computing power. This increases the need for serious infrastructure. Major players like AWS, Google Cloud, and Microsoft Azure have already boosted their capital spending by around 36% in 2024 , with most of it going toward AI infrastructure. As the need for chips rises, there is not nearly enough supply. by around 36% in 2024 by around 36% in 2024 That’s where the center of the shortage lies. The supply chain can't handle this kind of spike. With AI now baked into everything, lead times are growing, and production backlogs are piling up. Yet, with the global AI chip market expected to hit $89.6 billion by 2029, it will be even more challenging for supply to keep pace with accelerating demand. expected to hit $89.6 billion expected to hit $89.6 billion 3. Geopolitical Tensions and Trade Restrictions 3. Geopolitical Tensions and Trade Restrictions Semiconductors are more than tech components — they’re geopolitical currency. The U.S.-China trade war has turned chipmaking into a chess game, and AI chips are now one of the most contested pieces. To curb China’s access to advanced technologies, the U.S. has imposed export restrictions on high-end chips and the equipment used to manufacture them. Nvidia — one of the biggest names in the market — has already taken hits. The company held an estimated 90% share of China’s AI chip market before the U.S. imposed sanctions. has imposed export restrictions has imposed export restrictions an estimated 90% share an estimated 90% share Yet, it’s more than about individual companies. The entire semiconductor ecosystem is wildly globalized. A single AI chip might involve design in California, fabrication in Taiwan, materials from Japan, and assembly in Malaysia. When governments throw up barriers, the ripple effects can paralyze entire supply chains. 4. AI-Driven Demand from PCs and Smartphones 4. AI-Driven Demand from PCs and Smartphones AI is now reaching into consumers’ pockets. Until recently, most of the heavy AI lifting happened in the cloud. However, chipmakers are now bringing AI capabilities into smart devices, creating an even higher demand for AI chips. For example, smartphones are quickly evolving into mini AI machines. Their capabilities range from real-time language translation to generative image editing — features that require powerful on-device processing. Fitting energy-efficient chips into already thermally constrained devices can be a tough engineering challenge and an even tougher supply chain issue. However, AI smartphone shipments are growing exponentially, with market forecasts predicting them to grow from 234 million units in 2024 to more than 900 million by 2028. While this happens, enterprise and cloud-scale AI demand is still spiking, so the use cases are growing. from 234 million units in 2024 from 234 million units in 2024 Strategic Responses to Mitigating Shortages Strategic Responses to Mitigating Shortages Supply chain managers can take steps to reduce the potentially negative impacts of AI chip shortages. Here’s how they may play smarter in a chip-starved market: Diversifying suppliers: Relying on a single vendor can create setbacks. That’s why spreading sourcing across multiple regions and vendors is important. Strengthening supplier relationships: Businesses must treat suppliers like strategic partners rather than vending machines. From collaborative planning to long-term contracts, various tactics can give companies a leg up when things are tight. Investing in inventory buffering: Stocking inventory based on usage data and realistic forecasting can buy time without draining cash flow. Enhancing demand forecasting with AI: Ironically, AI can solve the chip shortage. With predictive analytics, organizational leaders can anticipate surges, identify vulnerabilities, and plan accordingly. Exploring domestic or nearshore manufacturing: Shorter supply chains can support smoother operations. If reshoring isn’t an option, nearshoring can offer a solid middle ground for critical components. Diversifying suppliers: Relying on a single vendor can create setbacks. That’s why spreading sourcing across multiple regions and vendors is important. Diversifying suppliers: Strengthening supplier relationships: Businesses must treat suppliers like strategic partners rather than vending machines. From collaborative planning to long-term contracts, various tactics can give companies a leg up when things are tight. Strengthening supplier relationships: Investing in inventory buffering: Stocking inventory based on usage data and realistic forecasting can buy time without draining cash flow. Investing in inventory buffering: Enhancing demand forecasting with AI: Ironically, AI can solve the chip shortage. With predictive analytics, organizational leaders can anticipate surges, identify vulnerabilities, and plan accordingly. Enhancing demand forecasting with AI: Exploring domestic or nearshore manufacturing: Shorter supply chains can support smoother operations. If reshoring isn’t an option, nearshoring can offer a solid middle ground for critical components. Exploring domestic or nearshore manufacturing: Solving the AI Chip Shortage AI may be booming, but there are not enough chips to keep pace with demand. With each issue occurring simultaneously, they pull the semiconductor supply chain in every direction. For supply chain managers and tech leaders to stay ahead, there will be a growing matter of planning smarter and building strength before it begins.

4 Issues Exacerbating the Looming AI Chip Shortage

Hello World is a weekly newsletter—delivered every Saturday morning—that goes deep into our original reporting and the questions we put to big thinkers in the field. Browse the archive here. Hello World is a weekly newsletter—delivered every Saturday morning—that goes deep into our original reporting and the questions we put to big thinkers in the field. Browse the archive here. Browse the archive here Hi there, It’s me, Tara García Mathewson, back in the Hello World rotation to get you thinking about civil rights in education. Earlier this week, the Center for Democracy & Technology (CDT) put out a report arguing that decades-old civil rights laws should be protecting students from discrimination based on their race—but they’re not. Reporting from The Markup was at the heart of one of its examples. but they’re not Did you see the story Todd Feathers wrote in April about an early warning system in Wisconsin designed to identify students at risk of dropping out? Well, the point of systems like these is to help schools figure out where to focus limited resources. They use past data about individual students, their schools, and districts to make predictions about current students. In Wisconsin, the system tends to be used by high schools considering how to serve incoming freshmen. But Todd cited some research out of the University of California, Berkeley in his story that looked at 10 years’ worth of data from Wisconsin’s system and found being labeled “high risk”—and therefore targeted for additional services—may not have made a difference. an early warning system in Wisconsin Kristin Woelfel, policy counsel at the Center for Democracy & Technology and lead author of this week’s report, said those findings prompted alarm bells. Government entities have to be careful about where they take race into account when treating people differently. Woelfel told me there’s a basic standard in civil rights law: Use of race must be “narrowly tailored to achieve a compelling government interest.” The Wisconsin system ostensibly used race to help prevent dropouts. That sounds like a compelling government interest! But not if it didn’t work. The UC Berkeley researchers looked at students just above and below the cutoff for being labeled high risk, and expected to see that students labeled high risk got relatively more services and support, and therefore graduated at relatively higher rates—but they didn’t find that. “The “high risk” label may not have helped students at all: the researchers couldn’t rule out a 0 percent increase in graduation rates as a result of the label. I discussed this with Cody Venzke, a senior policy counsel at the ACLU, which co-signed a letter the CDT sent to the U.S. Department of Education and the White House this week. The coalition urged the federal government to follow up its 2022 Blueprint for an AI Bill of Rights with more emphasis on civil rights. to the U.S. Department of Education and the White House Blueprint for an AI Bill of Rights Venzke said it’s always worth being suspicious when race is used to make decisions about how to treat individuals, given historical discrimination. In Wisconsin, The Markup’s reporting made clear educators were not properly trained to understand the dropout risk labels and intervene accordingly. The investigation features students who said they found the labels stigmatizing. And if that stigma isn’t offset by demonstrable benefits for students—in higher graduation rates, for example—Venzke sees the potential for a civil rights violation. But maybe it doesn’t have to be that way. Venzke points to schools’ obligation to protect students’ civil rights, ensure algorithmic systems are fair, and protect student privacy. “One of the ways you can achieve all three of those is by using aggregate data where aggregate data will fulfill the purpose,” Venzke said. As it turns out, the UC Berkeley researchers found it would. In their paper, currently under peer review, they recommend Wisconsin scrap its use of individual factors like a student’s race or even test scores in determining that student’s dropout risk. Juan Perdomo, lead author on the paper and now a postdoctoral fellow at Harvard, told me that in highly segregated Wisconsin, building an early warning system with data from entire schools or districts offers equally reliable predictions about which students are likely to drop out. currently under peer review “It also provides a much more actionable way of improving graduation outcomes,” Perdomo told me. And isn’t that the point? Early warning systems aren’t supposed to just be statistical exercises in identifying students at risk. They’re supposed to help better serve those students. If an early warning system identifies entire schools as high risk—because it doesn’t factor in individual characteristics—maybe it would drive statewide spending or districtwide training toward those schools and create a clearer path toward making a difference. For its part, the Wisconsin Department of Public Instruction is studying its early warning systems and considering changes. Communications Officer Chris Bucher told me the department has heard about concerns for some time now. So, we’ll see. In the meantime, we at The Markup will continue to report on algorithms that fuel discrimination and what that means for people’s lives. Thanks for reading! Tara García Mathewson Reporter The Markup Tara García Mathewson, Investigative Reporter Tara García Mathewson, Investigative Reporter Tara García Mathewson, Investigative Reporter Tara García Mathewson Graphics Graphics Gabriel Hongsdusit Gabriel Hongsdusit Gabriel Hongsdusit Gabriel Hongsdusit Engagement Engagement Maria Puertas Maria Puertas Maria Puertas Maria Puertas Copy Editing and Production Copy Editing and Production Sabrina Toppa Sabrina Toppa Sabrina Toppa Sabrina Toppa Editing Editing Monica Riese
Ko Bragg Monica Riese Monica Riese Monica Riese Ko Bragg Ko Bragg Ko Bragg Also published here Also published here here Photo by Artyom Kabajev on Unsplash Photo by Artyom Kabajev on Unsplash Artyom Kabajev Unsplash

The Dropout Risk System Is Facing Criticism After Our Report

Build an AI System to Recommend You the Jazziest Pants (Or Any Other Apparel) on the Planet 🩳

Bouw 'n AI-stelsel om jou die Jazziest Pants (Of enige ander klere) op die planeet te aanbeveel

بناء نظام الذكاء الاصطناعي لتشجيعك على أكثر جزر الجاز (أو أي ملابس أخرى) على الكوكب

Xatırladaq ki, dünyanın ən yaxşı futbolçularından biri və ya ən yaxşı futbolçudur.

আপনাকে পৃথিবীর সবচেয়ে জাজি পোশাক (অথবা অন্য কোনও পোশাক) সুপারিশ করার জন্য একটি এআই সিস্টেম তৈরি করুন

Construeix un sistema d'IA per recomanar-te els pantalons Jazziest (o qualsevol altre vestit) del planeta

Construye un sistema de IA para recomendarle los pantalones Jazziest (o cualquier otra ropa) en el planeta

Construír un sistema de IA para recomendarche os pantalóns de jazz (ou calquera outro traxe) no planeta

AIシステムを構築して、あなたに世界で最もジャズのパンツ(または他の服装)を推薦します。

Construiți un sistem AI pentru a vă recomanda cele mai jazz pantalonii (sau orice alt costum) de pe planetă

Постройте систему ИИ, чтобы рекомендовать вам самые джазовые брюки (или любой другой наряд) на планете

Изградите систем АИ да вам препоручи најјаззије панталоне (или било који други накит) на планети

Sizlere gezegendeki en iyi jazzi pantolonları (veya başka herhangi bir elbise) tavsiye etmek için bir AI sistemi oluşturun

Today, we witness how the era of generative AI is reshaping the way we search for information. Where we once fired off Google searches without a second thought, AI has now picked up the baton, helping people to find necessary information. But the topic is more nuanced than it may seem at first glance. Today, we witness how the era of generative AI is reshaping the way we search for information. Where we once fired off Google searches without a second thought, AI has now picked up the baton, helping people to find necessary information. But the topic is more nuanced than it may seem at first glance. On the one hand, a recent survey shows that 55% of people prefer using generative AI tools over traditional search engines. On the other hand, according to recent data, Google Search is 373 times bigger than ChatGPT search, highlighting that, despite the rise of AI, traditional search engines continue dominating. 55% 55% 373 373 So, is AI really the killer of traditional searching — or does it just complement it? Let’s dig into details, explore how both AI and search engines operate, and take a closer look at the strengths and shortcomings of generative AI. The Ways Machines Think: Google vs. AI It is important to first define key differences in the algorithms behind traditional search engines — with Google as one of the most prominent players — and generative AI. These two have fundamentally different technologies under the hood. Search engines like Google were developed decades ago when the technological boom was only gaining momentum. They were designed with a clear purpose: to find existing information on the web. They are not supposed to generate information — instead, they scan a massive index of the internet, sort through billions of web pages, and rank them based on authority, relevance, and other signals. So, the system is optimized for retrieval and built to guide you toward the sources. At their core, search engines rely on algorithms designed for ranking and indexing. For example, when you search for “the cheapest hotel in Miami,” the algorithm scans pages, indexing those with relevant keywords such as “cheapest hotel,” “Miami,” and “reviews.” Such factors as the credibility of the source (e.g., big hotel/booking aggregators), frequency of keyword appearance, and the quality of the overall content are ranked by the search engine to provide you with the most relevant results. Conversely, generative AI models are powered by large language models (LLMs) and are designed not to retrieve content but to generate it. Their core function is to predict the next word in a sequence, paying attention to patterns from vast training datasets, which include books, websites, code, and more. As a result, they provide human-like, conversational responses that are context-aware. Unlike search engines, which rely on ranking and indexing, generative AI models rely on probability and language modelling. Even when connected to real-time data through retrieval-augmented generation (RAG) systems, their output remains constructive — reflecting what they have absorbed without direct quotation from the source. Say you are curious about how to make up a perfect resume. In that case, AI could pull data from various HR guides, stories, or even advice from experts to generate a custom list of “dos” and “don’ts” for you. However, factual consistency is where AI typically stumbles. But this is what I will talk about later. Generally speaking, search engines lead you to the answers — generative AI builds them for you. Understanding this core difference is key to making the most of both — and now, let’s break down where AI truly excels over traditional search engines and where it falls short. Search Smarter, Not Harder? The Trade-offs of Generative AI At first glance, it might seem that AI is ready to replace conventional search bars — but is that really the case? This definitely calls for a deeper dive into the pitfalls and strengths of this cutting-edge technology, which could seem almost impeccable. Unarguably, when it comes to efficiency, generative AI models are more advantageous. They are not only faster than traditional search engines but also deliver comprehensive and context-aware answers — all in one go. This results in growing user satisfaction: 20% of consumers cite the speed, while 25% favor the AI for relevant search results. So, people simply do not need to sift through tons of links, as they get tailored responses to their requests almost immediately. growing growing Still, the benefits do not come without caveats, do they? While AI may feel smarter and faster than traditional search engines, it can occasionally manipulate facts, miss recent updates, or offer too generalized data — a phenomenon called “AI hallucination.” Plus, unlike Google, it does not show where the information comes from during the initial request, making users experience hurdles when trying to verify it. So, when accuracy is critical, many users turn to their old friends — search engines. called called A Rivalry or a Dynamic Duo? As it is becoming clear, AI offers undeniable advantages, but it is not without flaws. And this brings us to a crucial point: AI could be a great co-pilot, but it is not yet a full replacement. This is confirmed by the fact that 99% of people who use AI for searching purposes continue utilizing traditional search engines. 99% 99% In fact, AI, with its development by leaps and bounds, has yet to reach the point where it can fully replace Google or other search engines. One day, when AI is completely integrated into our online experiences, the act of “Googling” may feel like a voice from the past. However, for now, it is just a partnership rather than a competition.

Google Is Dead: Could AI Bury Traditional Searching?

Startups of The Year 2024 has officially wrapped, after months of nominations, voting, and support from our growing community. Startups of The Year 2024 With over 150,000 startups nominated and more than 4 million votes cast, this year's awards reflect the global tech ecosystem's appetite for future technology. 150,000 startups In the Media Industry, this year, a diverse group of Startups is redefining how content is produced and consumed, challenging conventional norms, and shaping the future of information and storytelling. Media Industry Startups of The Year 2024: Key Media Industry Stats Total Number of Participating Startups: 52,119
Sub Industries Represented: 15
Total Votes Gathered: 649,100
Winning Startups: 15 Total Number of Participating Startups: 52,119 Total Number of Participating Startups: Sub Industries Represented: 15 Sub Industries Represented: Total Votes Gathered: 649,100 Total Votes Gathered: Winning Startups: 15 Winning Startups: To discover winning startups in other industries or cities, head over to startups.hackernoon.com and use the search bar — the name at the top of each list is the winner.


You may also check out the full list of nominated startups in the Media Industry HERE.



Stay tuned for our Startups of The Year Winner Announcement Series, where we’ll be highlighting the top startups across various regions and industries. To discover winning startups in other industries or cities, head over to startups.hackernoon.com and use the search bar — the name at the top of each list is the winner.


You may also check out the full list of nominated startups in the Media Industry HERE. To discover winning startups in other industries or cities, head over to startups.hackernoon.com and use the search bar — the name at the top of each list is the winner. To discover winning startups in other industries or cities, head over to startups.hackernoon.com and use the search bar — the name at the top of each list is the winner. startups.hackernoon.com startups.hackernoon.com You may also check out the full list of nominated startups in the Media Industry HERE. You may also check out the full list of nominated startups in the Media Industry HERE. HERE. HERE Stay tuned for our Startups of The Year Winner Announcement Series, where we’ll be highlighting the top startups across various regions and industries. Stay tuned for our Startups of The Year Winner Announcement Series, where we’ll be highlighting the top startups across various regions and industries. Startups of The Year Winner Announcement Series Startups of The Year Winner Announcement Series Without further ado, let’s meet the startups shaping the future of the Media Industry! Meet the Top Media Startups of The Year 2024 Media Startups Marketing (299.9k total votes, 8k startups) Marketing Winner: ViralMango - Influencer Marketing Winner: ViralMango - Influencer Marketing ViralMango - Influencer Marketing SWS LLP (VistaarWebX)
Leadige LLC SWS LLP (VistaarWebX) SWS LLP (VistaarWebX) Leadige LLC Leadige LLC Honorable Mentions: Honorable Mentions: 91Ninjas
Picky
Bihar Say
Reputation House
Super Duper Studios
TriOn & Co
Yito 91Ninjas 91Ninjas Picky Picky Bihar Say Bihar Say Reputation House Reputation House Super Duper Studios Super Duper Studios TriOn & Co TriOn & Co Yito Yito Social Media (292.4k total votes, 4.9k startups) Social Media Social Media Winner: OneUp Marketing e Innovación Winner: OneUp Marketing e Innovación OneUp Marketing e Innovación ViralMango - Influencer Marketing
Leadige LLC ViralMango - Influencer Marketing ViralMango - Influencer Marketing Leadige LLC Leadige LLC Honorable Mentions: Honorable Mentions: Picky
Super Duper Studios
Yito
X Marketing Agency
Business Upturn
Buzzpenny
Moxie AI Picky Picky Super Duper Studios Super Duper Studios Yito Yito X Marketing Agency X Marketing Agency Business Upturn Business Upturn Buzzpenny Buzzpenny Moxie AI Moxie AI Media Production (12.1k total votes, 8.3k startups) Media Production Winner: 91Ninjas Winner: 91Ninjas 91Ninjas Tech Lead Journal
Curio Buddy Tech Lead Journal Tech Lead Journal Curio Buddy Curio Buddy Honorable Mentions: Honorable Mentions: Reinforce Lab Limited
DoubleShift
YES SIR AGENCY
Yemberzal
Business Upturn
Ocupe
Sakah Reinforce Lab Limited Reinforce Lab Limited DoubleShift DoubleShift YES SIR AGENCY YES SIR AGENCY Yemberzal Yemberzal Business Upturn Business Upturn Ocupe Ocupe Sakah Sakah Messaging & Communications (12.8k total votes, 4.8k startups) Messaging & Communications 4.8k Winner: TriOn & Co Winner: TriOn & Co TriOn & Co ADV Today
UNBENCH ADV Today ADV Today UNBENCH UNBENCH Honorable Mentions: Honorable Mentions: AppNomu
INNZPIRA
Digiwearit
PayCape
TECH VOUDOU
Fast Remote Services
Octolens AppNomu AppNomu INNZPIRA INNZPIRA Digiwearit Digiwearit PayCape PayCape TECH VOUDOU TECH VOUDOU Fast Remote Services Fast Remote Services Octolens Octolens Blogging (10.8k total votes, 11.2k startups) Blogging Winner: Tech Lead Journal Winner: Tech Lead Journal Tech Lead Journal ADV Today
UNBENCH ADV Today ADV Today UNBENCH UNBENCH Honorable Mentions: Honorable Mentions: AppNomu
INNZPIRA
Digiwearit
PayCape
TECH VOUDOU
Codegain
Fast Remote Services AppNomu AppNomu INNZPIRA INNZPIRA Digiwearit Digiwearit PayCape PayCape TECH VOUDOU TECH VOUDOU Codegain Codegain Fast Remote Services Fast Remote Services Publishing (4.6k total votes, 1.8k startups) Publishing Winner: Codegain Winner: Codegain Codegain Tech Lead Journal
Curio Buddy Tech Lead Journal Tech Lead Journal Curio Buddy Curio Buddy Honorable Mentions: Honorable Mentions: Yemberzal
Business Upturn
RELAYTO/ Content Experience Platform
FoundersToday
Self-employed in Austria
Female Tech Leaders Community
Gaming365 Yemberzal Yemberzal Business Upturn Business Upturn RELAYTO/ Content Experience Platform RELAYTO/ Content Experience Platform FoundersToday FoundersToday Self-employed in Austria Self-employed in Austria Female Tech Leaders Community Female Tech Leaders Community Gaming365 Gaming365 Design (3.8k total votes, 2.2k startups) Design Winner: Sivi Winner: Sivi Sivi Meliorar
Heeeper Meliorar Meliorar Heeeper Heeeper Honorable Mentions: Honorable Mentions: Farlo
Kireli Studio
Service Design Network Young Talent Board
Sivi AI
Zenerate
HARVIN CHIP AND AI TECHNOLOGIES LLP
Hedayati design studio Farlo Farlo Kireli Studio Kireli Studio Service Design Network Young Talent Board Service Design Network Young Talent Board Sivi AI Sivi AI Zenerate Zenerate HARVIN CHIP AND AI TECHNOLOGIES LLP HARVIN CHIP AND AI TECHNOLOGIES LLP Hedayati design studio Hedayati design studio Art (3.6k total votes, 1.7k startups) Art Winner: Meliorar Winner: Meliorar Meliorar Heeeper
Farlo Heeeper Heeeper Farlo Farlo Honorable Mentions: Honorable Mentions: INFINITY-UP
Kireli Studio
Service Design Network Young Talent Board
HARVIN CHIP AND AI TECHNOLOGIES LLP
Animatory
Hedayati design studio
Metafor Design INFINITY-UP INFINITY-UP Kireli Studio Kireli Studio Service Design Network Young Talent Board Service Design Network Young Talent Board HARVIN CHIP AND AI TECHNOLOGIES LLP HARVIN CHIP AND AI TECHNOLOGIES LLP Animatory Animatory Hedayati design studio Hedayati design studio Metafor Design Metafor Design Events (2.6k total votes, 2.5k startups) Events Winner: Voguer Winner: Voguer Voguer Wellbeing Think Tank
Farahy Wellbeing Think Tank Wellbeing Think Tank Farahy Farahy Honorable Mentions: Honorable Mentions: YellowLyfe HQ
Bubbo
22Mohawks
Livibe
eventmate
Aristocrat Media
IT-DAY YellowLyfe HQ YellowLyfe HQ Bubbo Bubbo 22Mohawks 22Mohawks Livibe Livibe eventmate eventmate Aristocrat Media Aristocrat Media IT-DAY IT-DAY Writing And Editing (2.2k total votes, 745 startups) Writing And Editing Winner: TECHDELLA Winner: TECHDELLA TECHDELLA Curio Buddy
Cyber News Live Curio Buddy Curio Buddy Cyber News Live Cyber News Live Honorable Mentions: Honorable Mentions: FoundersToday
Nord Comms
CraveHRO
Gaming365
Manc Sport
Enigmatic Horizon
The Cymes Media FoundersToday FoundersToday Nord Comms Nord Comms CraveHRO CraveHRO Gaming365 Gaming365 Manc Sport Manc Sport Enigmatic Horizon Enigmatic Horizon The Cymes Media The Cymes Media Entertainment (2.1k total votes, 2.1k startups) Entertainment Winner: Farahy Winner: Farahy Farahy BettorEdge
YellowLyfe BettorEdge BettorEdge YellowLyfe YellowLyfe Honorable Mentions: Honorable Mentions: Crown TV Zambia


IT-DAY


Fireframe Studios


Neighborhood


Eurovision Services


Con el Corazón Co.


Arroz Estudios Crown TV Zambia Crown TV Zambia Crown TV Zambia IT-DAY IT-DAY IT-DAY Fireframe Studios Fireframe Studios Fireframe Studios Neighborhood Neighborhood Neighborhood Eurovision Services Eurovision Services Eurovision Services Con el Corazón Co. Con el Corazón Co. Con el Corazón Co. Arroz Estudios Arroz Estudios Arroz Estudios Games (1.6k total votes, 1.2k startups) Games Winner: INFINITY-UP Winner: INFINITY-UP INFINITY-UP Parampará Game Studio
JAK Games Parampará Game Studio Parampará Game Studio JAK Games JAK Games Honorable Mentions: Honorable Mentions: Nano Knight Studio
Bokonzi
Pixels n' Voxels
Paidia Gaming
SKDO Systems
Leolit Games
Neko Games Nano Knight Studio Nano Knight Studio Bokonzi Bokonzi Pixels n' Voxels Pixels n' Voxels Paidia Gaming Paidia Gaming SKDO Systems SKDO Systems Leolit Games Leolit Games Neko Games Neko Games News & Journalism (1.8k total votes, 968 startups) News & Journalism Winner: Cyber News Live Winner: Cyber News Live Cyber News Live FoundersToday
GPS ROTAS SEGURAS FoundersToday FoundersToday GPS ROTAS SEGURAS GPS ROTAS SEGURAS Honorable Mentions: Honorable Mentions: KUENHA


CraveHRO


Gaming365


Manc Sport


Enigmatic Horizon


The Cymes Media


Quantum Commodity Intelligence KUENHA KUENHA KUENHA CraveHRO CraveHRO CraveHRO Gaming365 Gaming365 Gaming365 Manc Sport Manc Sport Manc Sport Enigmatic Horizon Enigmatic Horizon Enigmatic Horizon The Cymes Media The Cymes Media The Cymes Media Quantum Commodity Intelligence Quantum Commodity Intelligence Quantum Commodity Intelligence Music (747 total votes, 630 startups) Music Winner: Watchers—We Make Your Platform Social Winner: Watchers—We Make Your Platform Social Watchers—We Make Your Platform Social Brandenburg Labs GmbH
Vibra Music Brandenburg Labs GmbH Brandenburg Labs GmbH Vibra Music Vibra Music Honorable Mentions: Honorable Mentions: Livibe
Ionio
Mixer
LUV NFT
Accord Communications Uganda
Arroz Estudios
Tiny Souls Media Livibe Livibe Ionio Ionio Mixer Mixer LUV NFT LUV NFT Accord Communications Uganda Accord Communications Uganda Arroz Estudios Arroz Estudios Tiny Souls Media Tiny Souls Media Film (664 total votes, 977 startups) Film Winner: Le Magazine du Mali Winner: Le Magazine du Mali Le Magazine du Mali Animatory
Baros Animatory Animatory Baros Baros Honorable Mentions: Honorable Mentions: Crown TV Zambia
Revista Empreende
Yasmin Kollektiv e.V.
emilemilija
BioEconomia
Fireframe Studios
Public Health Insight Crown TV Zambia Crown TV Zambia Revista Empreende Revista Empreende Yasmin Kollektiv e.V. Yasmin Kollektiv e.V. emilemilija emilemilija BioEconomia BioEconomia Fireframe Studios Fireframe Studios Public Health Insight Public Health Insight Congratulations to you all. We will contact you with details on your winnings! Congratulations to you all. We will contact you with details on your winnings! See the global regional announcements here.
See the global industry announcements here. See the global regional announcements here.
See the global industry announcements here. See the global regional announcements here. here See the global industry announcements here. here About HackerNoon’s Startups of The Year Startups of The Year 2024 is HackerNoon’s flagship community-driven event celebrating startups, technology, and the spirit of innovation. Currently in its third iteration, the prestigious Internet award recognizes and celebrates tech startups of all shapes and sizes. This year, over 150,000 entities across 4200+ cities, 6 continents, and 100+ industries participated in a bid to be crowned the best startup of the year! Millions of votes have been cast over the past few years, and many storieshave been written about these daring and rising startups. Startups of The Year 2024 many stories The winners will get a free interview on HackerNoon and an Evergreen Tech Company News page. free interview Evergreen Tech Company News Visit our FAQ page to learn more. FAQ Download our design assets here. here Check out the Startups of The Year Merch Shop here. here HackerNoon’s Startups of The Year is a branding opportunity unlike any other. Whether your goal is brand awareness or lead generation, HackerNoon has curated packages to solve your marketing challenges. Book a meeting with us to learn more! curated packages Book a meeting with us to learn more! Book a meeting with us to learn more! Meet our sponsors: Meet our sponsors: Wellfound: Join the #1 global, startup-focused community. At Wellfound, we're not just a job board—we're the place where top startup talent and the world's most exciting companies connect to build the future. Wellfound: Join the #1 global, startup-focused community Notion: Notion is trusted and loved by thousands of startups as their connected workspace—from building product roadmaps to tracking fundraising. Try Notion with unlimited AI, FREE for up to 6 months, to build and scale your company with one powerful tool.Get your offer now! Notion: Try Notion with unlimited AI, FREE for up to 6 months Get your offer now ! Hubspot: If you’re looking for a smart CRM platform that meets the needs of small businesses, look no further than HubSpot. Seamlessly connect your data, teams and customers in one easy-to-use scalable platform that grows with your business. Get started for free. Hubspot: Get started for free Bright Data: Startups that leverage public web data can make faster, data-driven decisions, giving them a competitive edge. With Bright Data’s scalable web data collection, businesses can grow from a small operation to an enterprise by harnessing insights at every stage. Bright Data: Bright Data’s scalable web data collection Algolia: Algolia NeuralSearch is the world's only AI end-to-end Search and Discovery Platform combining powerful keyword and natural language processing in a single API. Algolia: AI end-to-end Search and Discovery Platform

Startups of The Year 2024 Winners: Media Industry

২০২৪ সালের স্টার্টআপ বিজয়ী: মিডিয়া শিল্প

Οι νεοσύστατες επιχειρήσεις του έτους 2024 Νικητές: Βιομηχανία Μέσων Ενημέρωσης

Startups del Año 2024 Ganadores: Media Industry

Le start-up dell'anno 2024 vincitori: Media Industry

Startups of the Year 2024 Winners: メディア産業

អ្នកចាប់ផ្តើមនៃឆ្នាំ 2024 អ្នកឈ្នះ: ឧស្សាហកម្មប្រព័ន្ធផ្សព្វផ្សាយ

Startups ຂອງປີ 2024 ຜູ້ຊ່ຽວຊານ: Media Industry

2024. gada jaunuzņēmumu uzvarētāji: Mediju nozare

Startup Tahun 2024 Pemenang: Media Industri

Startup-urile anului 2024 câștigătoare: Industria mass-media

Победители стартапов 2024 года: медиаиндустрия

2024年年初创企业获奖者:媒体行业

5 Free AI Tools Every Small Business Owner Should Know

I've been writing code for the last ~18 years, and professionally for about 8 years (including at Google, Uber) - and I’ve got to say I’ve really loved it. And what’s not to love? I got to spend most of my time building fun things, the reward feedback loop was tight and my tools got better pretty much every few years. Syntax highlighting, auto-complete, IntelliSense, project-level refactorings, and even early Github Copilot all made my experience writing code more joyful.  With every generation of improvements, it felt like these enhancements helped *me* be a better coder. Until now. This latest wave feels very, very different. With agentic programming (*cough* vibe-coding), it doesn't feel like another incremental upgrade. It actively scrambles my perception of what's coming next, and my role in it. I tried to narrow down on why exactly it feels so different. Then as I witnessed a code agent one-shot yet another small but still somewhat ambiguous task on my codebase, it suddenly dawned on me. It doesn't feel like I'm “coding” anymore, rather it feels like I’m “delegating”. It feels like I'm giving abstract instructions to another human, or a set of humans - rather than giving precise instructions to a computer. This has crossed some sort of chasm for me. A "feel the AGI" moment of sorts. "feel the AGI" https://x.com/sidharth_raja/status/1905667236628705476?embedable=true https://x.com/sidharth_raja/status/1905667236628705476?embedable=true It still feels like I’m “directing” or “programming” the system. But what’s different is that I’m now programming an organization of agent coders to achieve the goal, rather than programming the computer directly. Working this way requires me to confront an unfortunate new truth. A Sobering Realization The economic value of my knowing how to “code” has gone to zero. The fact of the matter is that everyone in the world now has (or soon will have) access to an army of increasingly brilliant coders in their pocket. One they can instantly summon whenever they want. And it’s basically free. It’s a bittersweet realization. I empathize with @amasad’s sentiment here, after a tweet where he says he “no longer thinks you should learn to code”. @amasad https://x.com/amasad/status/1905261929452519838?embedable=true https://x.com/amasad/status/1905261929452519838?embedable=true Coding for art’s sake v/s Coding for the user’s sake Coding for fun is enjoyable. Back in undergrad days, I really enjoyed competitive programming too. My team even went for the ACM-ICPC Asia regionals twice, and we generally had a blast. There’s a certain rush that comes from figuring out a problem, and writing code to solve it. It’s not entirely unlike a crossword puzzle or a sudoku or a math problem. Sure, you could get help to do it (erm. cheat!), but that’s not the point of it. This is an artisanal mindset. Coding for art's sake. For the mere fun of the game. When working on a product, you largely need to throw that mindset out the window. Here, the code mainly exists to serve the product and the user. It's a means to an end. The end user doesn’t care whether I wrote it, or instructed an agent to write it. The user only cares that it works. Correctly, reliably, securely, fast. That they can forget it exists, and get on with their day. So the question then becomes “What’s the fastest way to get to (good, maintainable) code that does that?” Unfortunately, it seems like the answer to that is that I may have to learn to… get out of the way. That maybe I mostly shouldn’t be writing code anymore, because doing so would make me the bottleneck, or even worse - the hindrance. In ”Average is Over”, Tyler Cowen talked about the dynamic of “human + computer” teams in chess. Such teamups (surprisingly) still held an advantage even as recently as 2013, but the trend line of human contribution to the team was clear. From his essay  “What are humans still good for”: ”Average is Over” “What are humans still good for” it is interesting to observe an approach to the flip point, where even the most talented humans move from being very real contributors to being strictly zero marginal product. Or negative marginal product, as the case may be it is interesting to observe an approach to the flip point, where even the most talented humans move from being very real contributors to being strictly zero marginal product. Or negative marginal product, as the case may be A similar dynamic seems to be playing out here in coding. For now, it appears I can still add value by looking at the machine output and adding value on top of it, but once again - for how long?. What’s currently good for quick prototyping today will improve and be the builder of robust systems tomorrow. Today’s toy is tomorrow’s tool. On one hand, low level things getting abstracted away isn't new for the field. Computer Science, much moreso than other fields has a rich history of composability. Chances are you haven't written in machine code or assembly in a while if ever (thank you compilers!). You've almost certainly used building blocks (libraries/APIs/platforms) created by other people. It's wrappers all the way down to the silicon. For now however, coding remains only one part of the overall software engineering process. And it just so turns out that the way I can bring most value to this system is not with my coding ability anymore, it’s with my vision and ability to articulate what I want, and to steer this organization of agents towards that goal. It's to eliminate ambiguity as much as possible, and to bridge the gap between the vision and the spec. In a sense, this role is closer to an “Agent Director” than a software engineer writing code. So where do we go from here? Because it's like delegation, axioms of human organization management seem to apply to agent organization management. For example: Know your agents limitations, and delegate accordingly. They will always willingly attempt to bite off more they can chew. Don't let them.
Setup systems of checks and balances to catch when a change is breaking, and to guide the agent towards writing good code. Type safety is good. Tests are good.
Setup an environment where agents can get the info they need to succeed. Documentation is good. Separation of concerns is good. A well organized codebase is good.
Parallelization is good. Don't just wait on a single agent in a single threaded way, especially for long running tasks. There's a solid chance that future elite programmers resemble torrential high APM Starcraft players - commanding and collating the outputs of their army of agent units.
And most importantly, make your vision clear and communicate it clearly, so that the agent can be empowered to make the right decisions fitting into your broader framework. What the agent really doesn’t know is what you want, so it’s up to you to direct it towards that. Know your agents limitations, and delegate accordingly. They will always willingly attempt to bite off more they can chew. Don't let them. Setup systems of checks and balances to catch when a change is breaking, and to guide the agent towards writing good code. Type safety is good. Tests are good. Setup an environment where agents can get the info they need to succeed. Documentation is good. Separation of concerns is good. A well organized codebase is good. Parallelization is good. Don't just wait on a single agent in a single threaded way, especially for long running tasks. There's a solid chance that future elite programmers resemble torrential high APM Starcraft players - commanding and collating the outputs of their army of agent units. torrential high APM Starcraft players And most importantly, make your vision clear and communicate it clearly, so that the agent can be empowered to make the right decisions fitting into your broader framework. What the agent really doesn’t know is what you want, so it’s up to you to direct it towards that. And after all is said and done though, when you finally deliver something - you’re still stamping it with your seal of quality. Your name and reputation is your brand. As the "lead" of those agents, you’re still accountable for their results. The buck stops with you.

Coding Might Not Be the Best Use of Your Time Any More

Kodering mag nie meer die beste gebruik van jou tyd wees nie

Код можа не быць найлепшым выкарыстаннем вашага часу

কোডিং আপনার সময়ের সেরা ব্যবহার হতে পারে না

Η κωδικοποίηση μπορεί να μην είναι η καλύτερη χρήση του χρόνου σας πια

Codificar puede no ser el mejor uso de tu tiempo ya

Koodaus ei ehkä ole parasta aikaa enää

कोडिंग अब आपके समय का सबसे अच्छा उपयोग नहीं हो सकता है

A kódolás már nem lehet a legjobb használata az időnek

コードはもはやあなたの時間の最善の使い方ではないかもしれません。

Coding შეიძლება არ იყოს საუკეთესო გამოყენება თქვენი დრო უფრო მეტი

ការកូដអាចមិនមែនជាការប្រើប្រាស់ល្អបំផុតនៃពេលវេលារបស់អ្នកជាច្រើនទៀត

Kodimi nuk mund të jetë më përdorimi më i mirë i kohës suaj

Bitcoin’s surge past $94,000 might just be the opening act. The real story of a bull market? It’s written in the fine print of on-chain metrics. Bitcoin’s surge past $94,000 might just be the opening act. The real story of a bull market? It’s written in the fine print of on-chain metrics. As Bitcoin reclaims the $94K again, altcoins are rallying across the board. But does the underlying blockchain data confirm a full-blown bull run? By dissecting trading volume, user activity, and wallet distribution, we uncover the market’s hidden signals. Transaction Flow Over the last 24 hours, according to bitsCrunch data, total trading volume across all networks hit $39.9 billion, with 7.2 million transactions processed by 3.03 million unique addresses involving 13,800 tokens. Since July 2023, transaction volume has steadily climbed from a low of 2 million to 10 million, accelerating sharply after June 2024. This surge reflects deepening market liquidity, despite a temporary dip in transaction counts this March. Short-term momentum remains bullish. bitsCrunch Who’s Driving the Action? On-chain trader activity charts reveal volatile participation since 2023. While monthly active traders plummeted below 2 million in October 2023, the metric rebounded to 8 million by late 2024 and stabilized near yearly highs in January 2025—aligning with classic “recovery-to-expansion” market cycles. Notably, growth isn’t linear. According to bitsCrunch data, mid-quarter dips (May, August) suggest profit-taking or sentiment shifts, while breaking news events temporarily swayed institutional and retail sentiment. Yet with daily active traders still at 3.03 million—just one-third of peak levels—this metric warrants close watching. bitsCrunch The Whale-Retail Divide BitsCrunch’s wallet distribution highlights crypto’s stark wealth concentration: 1,052 “Mega Whale” addresses hold over 100 million each. This 80/20 split mirrors traditional finance, where whales accumulate early, followed by retail traders amplifying price action. Mid-tier “Dolphins” ($1M–$10M) and “Fish” ($10K–$100K) also play critical roles, injecting liquidity that sustains market moves. Ethereum’s Slide and the Layer 1 Wars Polygon maintains steady traction with ~4,000 daily transactions, but Ethereum tells a different story. Despite price resilience, its on-chain activity plummeted in early 2025—a lagging indicator of fading dominance. As competing Layer 1 blockchains gain traction, Ethereum’s decline in activity could reshape the market landscape, forcing investors to rethink scalability narratives. Conclusion Bitcoin’s $94K rebound and altcoin rallies are eye-catching, but on-chain metrics reveal a nuanced reality: Trading activity hasn’t yet matched previous bull market intensity. With regulatory developments and policy shifts looming in 2025, both whales and retail traders are dancing cautiously. New narratives and sectors will emerge, but savvy investors focus on marginal data shifts—like whale accumulation patterns or sudden liquidity drains—to avoid getting caught in hype cycles. In a market where 1% of wallets control outsized influence, staying rational means letting on-chain data separate signal from noise. Stay curious. Stay skeptical. And always verify the chain. And always verify the chain.

Is the Bull Market Back? Decoding Crypto’s Next Move Through On-Chain Data

tl;dr Developers often struggle with the applicability of benchmarks, understanding semantic versioning, and identifying a common standard to effectively leverage the right models.
These challenges often surface directly in developer workflows—whether it’s wiring up a model inside an IDE or deploying a feature that depends on consistent outputs across model versions.
Devs also face frustration when trying to improve results—particularly since prompting techniques and optimisation mechanisms are still not fully understood.
Beyond cost-to-performance ratio as the primary lens for model selection, the future of AI native developer workflow lie in a call for abstraction layers that simplify multi-model integration. Developers often struggle with the applicability of benchmarks, understanding semantic versioning, and identifying a common standard to effectively leverage the right models. right These challenges often surface directly in developer workflows—whether it’s wiring up a model inside an IDE or deploying a feature that depends on consistent outputs across model versions. Devs also face frustration when trying to improve results—particularly since prompting techniques and optimisation mechanisms are still not fully understood. Beyond cost-to-performance ratio as the primary lens for model selection, the future of AI native developer workflow lie in a call for abstraction layers that simplify multi-model integration. How does GPT-4.1 measure up? How does GPT-4.1 measure up? GPT-4.1 was recently released, and OpenAI disclosed its 54.6% score on SWE-bench Verified, improving by 21.4 percentage points over GPT‑4o and 26.6 percentage points over GPT‑4.5—making it a leading model for coding within OpenAI: SWE-bench Verified Though the release highlights an improvement in the OpenAI's model suite, those figures are slightly under the scores reported by Google and Anthropic for Gemini 2.5 Pro (63.8%) and Claude 3.7 Sonnet (62.3%), on the same benchmark. More importantly, the release of this new model is shedding light on emerging frustrations about how developers are perceiving the current state of the ecosystem. 63.8% 62.3% Wider ecosystem frustrations Wider ecosystem frustrations Frustration 1: Alignment on Benchmarks Frustration 1: Alignment on Benchmarks “The benchmarks are so whack, how does [GPT-4.1] score so high on SWE bench (20% higher almost) and fall behind DeepSeek on [Aider Benchmark]? - Reddit user. “The benchmarks are so whack, how does [GPT-4.1] score so high on SWE bench (20% higher almost) and fall behind DeepSeek on [Aider Benchmark]? - Reddit user. “The benchmarks are so whack, how does [GPT-4.1] score so high on SWE bench (20% higher almost) and fall behind DeepSeek on [Aider Benchmark]? - Reddit user. Benchmark results (like SWE-bench, Aider Polyglot, etc.) have their own sets of requirements, making it challenging to understand and trust the true high performers within a domain. For example, when switching to a model with superior benchmark scores in your dev tool, it may fail to execute your requirements as effectively as the previous model—despite the improved metrics. Developers are thus struggling to agree on model performance as the results are so specific to the task, codebase and prompts. Aider Polyglot Internal benchmarking is emerging as a critical practice, with teams developing custom evaluation frameworks suited to their specific needs. For instance, in the aim to build a secure AI native development platform, Tessl is leveraging known benchmarks as well as their own to assess the most fitting model for their use cases. Tessl While there are valid reasons for enterprises and developers to do this, many devs still just want to get work done—not manage model quirks. It’s also worth noting, that Michelle Pokrass @OpenAI mentioned that “[GPT‑4.1] has a focus on real work usage and utility, and less on benchmarks,” further highlighting the idea that benchmarks shouldn’t be considered the ultimate source of truth. Michelle Pokrass [GPT‑4.1] has a focus on real work usage and utility, and less on benchmarks, Frustration 2: Lack of Standardisation Frustration 2: Lack of Standardisation “4o with scheduled tasks”—why on earth is that a model and not a tool that other models can use?! — HackerNews user “4o with scheduled tasks”—why on earth is that a model and not a tool that other models can use?! — HackerNews user “4o with scheduled tasks”—why on earth is that a model and not a tool that other models can use?! — HackerNews user There’s a growing outcry from developers over the lack of standardization across models—in features, and interfaces. Feature fragmentation has forced manual model selection for each task. If you’re building a dev assistant that uses image generation, CoT reasoning, and code completion, this often means duct-taping together multiple APIs and writing fallback logic. This is reminiscent of the browser wars before the 2000s—different versions of websites for Internet Explorer, Netscape, and later Firefox, because there were no standard implementations of HTML, CSS, or JavaScript. Standardisation often comes not from vendors agreeing, but from users banding together out of shared pain—think of the lock-in and interoperability pain prior to SQL. The good news is that we are seeing this already in a couple of places, think OpenAI’s API as the defacto standard, as well as Anthropic’s MCP. We also anticipate that prompt routers will be be significant part of the solution. Analogous to HTTP router + HTML standard, a combination of prompt routing and standardization may be the path to absolving these frustrations. HTTP router + HTML standard Frustration 3: Semantic Drift in Model Versions Frustration 3: Semantic Drift in Model Versions GPT-4.1’s release highlights confusion. There was a lot of hype around GPT-5 coming out, and after the disappointing results from GPT-4.5, OpenAI released 4.1. Why GPT-4.1? The move back toward 4.1 feels like a semantic step backward (GPT-4.1 vs. GPT-4o vs. GPT-4 Turbo—what’s going on here?). While the claims and early results show improvement, and the focus between 4.5 and 4.1 differ, it still leaves room for confusion. If you’ve been building repeatable prompting flows, you will at the very least feel mildly overwhelmed by the model maze—is higher better? faster? newer? That said, the ambiguity hasn’t gone unnoticed. OpenAI has acknowledged these naming confusions, and we should expect future iterations to bring more clarity to the semantics and categorization framework. Frustration 4: Prompting and Hand-Waving Frustration 4: Prompting and Hand-Waving OpenAI publishing recommended workflow is a sign that prompting is becoming systematic, not just vibes. Yet, there’s frustration among the community where these practices still feel more like guesswork than true, quantifiable methods. recommended workflow vibes Developers report that small prompt changes lead to big performance differences, revealing the lack of standard prompting guidelines. Also, developers need to prompt different models in different ways—which is tricky to manage. Prompting today thus resembles alchemy before the scientific method—part intuition, part ritual, often surprisingly effective, but lacking universal principles. That said, tools like DSPy already offer a path toward a framework turning prompt design into a repeatable, testable, and improvable process. If you are a developer reading this, one last piece of advice would be to adopt the mindset of an Explorer. We’re in the prototyping phase of a new engineering discipline, and we need to shift our mental models accordingly. DSPy Explorer Future Implications Future Implications The optimal model choice often depends more on practical constraints (budget, speed, use case) than raw coding performance. Cost-to-performance ratio will become the primary lens for model selection—replacing the “latest and greatest” mindset. Indeed, LLM ecosystem is entering the “infrastructure layer wars,” where optimization and integration trump novelty. For instance, Google’s TPUs with Gemini 2.5 is not only leading on Aider’s coding benchmarks, but it is cheaper and faster to serve (scored 73% on Aider Polyglot for ~$6, vs. GPT-4.1’s ~$10). Even if GPT swing back with GPT-5, the hardware war with latency and costs, may ultimately decide which vendor developers and businesses favour. Read between the lines, and this is great news for developers and organizations—we’re benefiting from a race to the bottom on cost and a rise in serving efficiency! Google’s TPUs scored 73% on Aider Polyglot for ~$6, vs. GPT-4.1’s ~$10 At the same time, developers are voicing a roaming trend: devs don’t enjoy hand-picking models—they want abstraction layers to handle this automatically. This shift prioritizes developer experience, where developers expect multi-model environments to take on this work (think of a Cursor who automatically hand picks the best fitting model for the task you require). This evolution isn’t just about benchmark performance—it’s about empowering developers to fine-tune their infra: using leaner, cheaper models for routine jobs and automatically switching to more powerful ones when the stakes are higher. The future of AI native development doesn’t lie in manually picking models—it lies in building abstraction layers that make those choices invisible, so developers can focus on building, not juggling APIs. This evolution isn’t just technical—it’s deeply conceptual. It reflects a broader transformation in the developer’s role: from toolsmith to architect. That shift mirrors the "Implementation to Intent Pattern" from Patrick’s 4 Patterns of AI Native Development. If you haven’t explored it yet, it’s a must-read, and offers a compelling blueprint for where we’re headed. Implementation to Intent Pattern" 4 Patterns of AI Native Development

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