Get the latest New Orleans weather forecast, news, and alerts from the WGNO weather team. See the latest episode of Weather &amp; Science for Kids.

Rain ending Friday evening but more on the way this weekend

New Orleans Weather & 7 Day Forecast | WGNO.com

Any storms that develop may have brief heavy downpours and gusty winds.

More storms for New Orleans late Friday through Saturday

Expect another warm day on Friday in Northern California, but a weekend cooldown is coming.

Northern California forecast: 90s on Friday ahead of a cooler weekend

Dramatic mapping shows spirals of wind and rain circling over the Shetland Islands and the North Sea - with heavy downpours set to pummel the UK into next week

UK summer forecast: Country split in HALF as Arctic storms send temperatures tumbling in parts of Britain

Severe thunderstorms packing damaging winds of up to 60 mph and heavy rains are possible across Louisiana Friday afternoon and evening, followed by scattered storms that should continue through the

Severe weather is possible across Louisiana this weekend. See forecast for your city.

According to the India Meteorological Department (IMD), the weather forecast indicates a mainly clear sky, gradually transitioning to partly cloudy conditions in the afternoon or evening. Temperatures are expected to peak at 38 degrees Celsius, with a minimum temperature hovering around 24 ...

Pune Weather: City To Witness Thunderstorms, Rain From June 3 | Check Weekly Forecast | Times Now

Bay Area Heat Wave Could Push Temps To Triple Digits: Forecast - San Francisco, CA - The first heat wave of the season is on the horizon, and it looks like a scorcher. Here&#x27;s what to know.

Bay Area Heat Wave Could Push Temps To Triple Digits: Forecast | San Francisco, CA Patch

It's one of auto racing's biggest days of the year, and weather will likely be a factor. - Articles from The Weather Channel | weather.com

Daytona 500 Forecast: Rain Likely Sunday | Weather.com

Up to several inches could pile up overnight into early Saturday; the snow could fall quite heavily for a time.

D.C.-area forecast: Increasing clouds ahead of accumulating snow tonight - The Washington Post

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Steal My Subscription, Revenue Forecasting Model (P.S.: It Tracks Churn!)

The HackerNoon Newsletter: Polymarket Explained: How Blockchain Prediction Markets Are Shaping the Future of Forecasting (11/9/2024)

Polymarket Explained: How Blockchain Prediction Markets Are Shaping the Future of Forecasting

Polymarket обяснява: Как пазарите за прогнозиране на блокчейн оформят бъдещето на прогнозирането

Polymarket vysvětlil: Jak trhy s predikcí blockchainu utvářejí budoucnost prognózování

Polymarket explicado: cómo los mercados de predicción de blockchain están dando forma al futuro de la previsión

Polymarket توضیح داد: چگونه بازارهای پیش بینی بلاک چین آینده پیش بینی را شکل می دهند

Պոլիմարկետը բացատրեց. ինչպես են բլոկչեյն կանխատեսման շուկաները ձևավորում կանխատեսումների ապագան

פולימרקט הסביר: כיצד שווקי חיזוי בלוקצ'יין מעצבים את עתיד החיזוי

ポリマーケットの解説: ブロックチェーン予測市場が予測の未来をどう形作るのか

Полимаркет тайлбарлав: Блокчейн таамаглалын зах зээл урьдчилан таамаглах ирээдүйг хэрхэн бүрдүүлж байна

Polymarket Explained: Cum piețele de predicție Blockchain modelează viitorul prognozei

Polymarket ayaa Sharaxay: Sida Blockchain Suuqyada Saadaasha ay u qaabeynayaan Mustaqbalka Saadaasha

Полимаркет објаснио: како тржишта предвиђања блокчејна обликују будућност предвиђања

Полимаркет фаҳмонд: Чӣ гуна бозорҳои пешгӯии блокчейн ояндаи пешгӯиро ташаккул медиҳанд

What if pollsters are just too afraid of Trump this time around? Out of curiosity about the data-science side of things, in September, I started my own little forecast for the 2024 U.S. presidential election, which considers three different scenarios: ① based on weighted high-quality polls only; ② adjusted by the average polling bias from 2016/20; and ③ adjusted by just half the 2016/20 bias. I’ve previously written about my methodology and predictions here and here. In the latter article, I lay out why I believe another polling bias underestimating Trump is a very real—even probable—possibility that can’t be dismissed. Trump as a candidate seems to completely defy the laws of “normal” elections. It’s unclear how much pollsters have understood and fixed the reasons for their misses in the past 2 elections; the “shy Trump voter” might still exist. On top, Republican-initiated voter suppression—like purging voters from voter rolls in Virginia this close to an election, as recently allowed by the Supreme Court—will disadvantage Democrats in at least some places, which can’t be captured by opinion polling. In the past weeks, however, I’ve also seen arguments as to why things could be the other way ‘round this time, and I must admit that I find some of them quite convincing. Mason McCann argues around campaign-related fundamentals and that Harris has an advantage in every single one of them: money, ground game, vibe, you name it. And it’s true that it seems the Trump campaign can’t really keep up with these. While my take has been that this election is probably most similar to 2016/20 (based on the assumption that you can’t compare elections involving Trump to “normal” elections), McCann argues it might actually be closer to 2012 due to certain similarities: an “enthusiastic” campaign, “perceptions of a bad economy,” and a “very overconfident republican campaign,” among others. Finally, he makes the point that 2024 is different from 2016/20 in that HRC simply didn’t do a lot of campaigning in the Rust Belt, and in 2020, there was no GOTV ground game due to Covid, which, of course, looks very different this year. https://twitter.com/Guntar/status/1851339376196010260?embedable=true So, campaign-related fundamentals point to Harris, and admittedly, this is hard to capture in a purely polling-based forecast. If Dems manage to get their supporters to vote in bigger numbers than in 2016/20—and observations so far suggest this—there might be “a mild surprise when the electorate is not as GOP as the pollsters assume,” writes Dan Guild (@dcg1114 on Twitter). “Pollsters consistently assume a very Republican electorate—my guess is to avoid ’16/’20.” https://twitter.com/dcg1114/status/1851779439899578606?embedable=true Nate Cohn admits as much in his latest article. And @Angry_Staffer, based on this, writes: “Basically, pollsters are terrified of being wrong again, so they’re flat-out ignoring ‘very blue’ results.” https://twitter.com/Angry_Staffer/status/1852421106155540596?embedable=true So, just for funsies, let’s take a look at the case that this year will play out like 2012, shall we? Luckily, Prof. Jacob Long has done a fantastic job analyzing and providing past polling biases on his blog, and that’s what I’ll be working with. I’ll just add the point estimate for the bias to my own polling averages without adjusting my standard deviation. Simply to get a quick, general idea in which direction this would move the forecast. We’re all just 🔮ing here anyway. As a reminder, this is what my current prediction based on weighted high-quality polls only looks like. Michigan, Nevada, North Carolina, and Pennsylvania are toss-ups (according to 538’s definition). Overall, Harris is the ever-so-slight favorite. And when we adjust by the 2012 polling bias, we get this: This would look a lot better for Democrats, but it’s still a relatively close race. Georgia is a toss-up now while Harris has much more comfortable chances in Michigan and Nevada. I’ve also run my Monte Carlo simulation with these probabilities: ④ With 2012 polling bias
===============================
Harris's probability of winning: 71.38%
Trump's probability of winning: 28.20%
Probability of tie: 0.41% While we’re at it, let’s also have a look at how the 2022 polling bias—from the most recent and first “post-Covid” election—would change the predictions: Most significantly here, Michigan, Pennsylvania, and Wisconsin are now lean or likely D. And these are also the ones where Harris is the favorite in all three of the above scenarios. Chances of winning with the 2022 polling bias: ⑤ With 2022 polling bias
===============================
Harris's probability of winning: 66.56%
Trump's probability of winning: 33.10%
Probability of tie: 0.34% So, what are we gonna do with this now? What’s the prediction? Who has the better chance of winning the election, and how good are they? I have no clue whatsoever. As I already said above, it’s all 🔮ing at this point. There are so many variables in this, so many things that could go one or the other way … What we can do at best is think about which scenarios are maybe a bit more likely than others. My opinion on this: Accurate polls (①) and the same polling bias as in 2016/20 (②) are rather unlikely. This leaves ③ (half 2016/20 bias), ④ (2012 bias), and ⑤ (2022 bias) as the more likely of my scenarios. In any case, the election is gonna happen, there’ll be a winner, and there’ll be one option that is closer than the others. All we need is just a little more patience. Please also feel free to head over to my Github—where I'll collect and archive everything—and have a look at the complete data and calculations: https://github.com/maxspeicher/2024-us-presidential-election. I also post regular updates on Bluesky and Twitter. ☕ I love coffee, and if you want to support my work, you can always send me one, or subscribe to my newsletter. 🗞️ What if pollsters are just too afraid of Trump this time around? What if pollsters are just too afraid of Trump this time around? Out of curiosity about the data-science side of things, in September, I started my own little forecast for the 2024 U.S. presidential election, which considers three different scenarios: ① based on weighted high-quality polls only; ② adjusted by the average polling bias from 2016/20; and ③ adjusted by just half the 2016/20 bias. I’ve previously written about my methodology and predictions here and here . own little forecast half here here In the latter article, I lay out why I believe another polling bias underestimating Trump is a very real—even probable—possibility that can’t be dismissed. Trump as a candidate seems to completely defy the laws of “normal” elections. It’s unclear how much pollsters have understood and fixed the reasons for their misses in the past 2 elections; the “shy Trump voter” might still exist. On top, Republican-initiated voter suppression—like purging voters from voter rolls in Virginia this close to an election, as recently allowed by the Supreme Court —will disadvantage Democrats in at least some places, which can’t be captured by opinion polling. recently allowed by the Supreme Court In the past weeks, however, I’ve also seen arguments as to why things could be the other way ‘round this time, and I must admit that I find some of them quite convincing. Mason McCann argues around campaign-related fundamentals and that Harris has an advantage in every single one of them: money, ground game, vibe, you name it. And it’s true that it seems the Trump campaign can’t really keep up with these. While my take has been that this election is probably most similar to 2016/20 (based on the assumption that you can’t compare elections involving Trump to “normal” elections), McCann argues it might actually be closer to 2012 due to certain similarities: an “enthusiastic” campaign, “perceptions of a bad economy,” and a “very overconfident republican campaign,” among others. Mason McCann Finally, he makes the point that 2024 is different from 2016/20 in that HRC simply didn’t do a lot of campaigning in the Rust Belt, and in 2020, there was no GOTV ground game due to Covid, which, of course, looks very different this year. very https://twitter.com/ Guntar /status/1851339376196010260?embedable=true https://twitter.com/ Guntar Guntar /status/1851339376196010260?embedable=true So, campaign-related fundamentals point to Harris, and admittedly, this is hard to capture in a purely polling-based forecast. If Dems manage to get their supporters to vote in bigger numbers than in 2016/20—and observations so far suggest this—there might be “a mild surprise when the electorate is not as GOP as the pollsters assume,” writes Dan Guild ( @dcg1114 on Twitter). “Pollsters consistently assume a very Republican electorate—my guess is to avoid ’16/’20.” @dcg1114 https://twitter.com/dcg1114/status/1851779439899578606?embedable=true https://twitter.com/dcg1114/status/1851779439899578606?embedable=true Nate Cohn admits as much in his latest article . And @Angry_Staffer , based on this, writes: “Basically, pollsters are terrified of being wrong again, so they’re flat-out ignoring ‘very blue’ results.” his latest article @Angry_Staffer https://twitter.com/Angry_Staffer/status/1852421106155540596?embedable=true https://twitter.com/Angry_Staffer/status/1852421106155540596?embedable=true So, just for funsies, let’s take a look at the case that this year will play out like 2012, shall we? Luckily, Prof. Jacob Long has done a fantastic job analyzing and providing past polling biases on his blog , and that’s what I’ll be working with. I’ll just add the point estimate for the bias to my own polling averages without adjusting my standard deviation. Simply to get a quick, general idea in which direction this would move the forecast. We’re all just 🔮ing here anyway. analyzing and providing past polling biases on his blog As a reminder, this is what my current prediction based on weighted high-quality polls only looks like. Michigan, Nevada, North Carolina, and Pennsylvania are toss-ups (according to 538’s definition). Overall, Harris is the ever-so-slight favorite. And when we adjust by the 2012 polling bias, we get this: This would look a lot better for Democrats, but it’s still a relatively close race. Georgia is a toss-up now while Harris has much more comfortable chances in Michigan and Nevada. I’ve also run my Monte Carlo simulation with these probabilities: my Monte Carlo simulation ④ With 2012 polling bias
===============================
Harris's probability of winning: 71.38%
Trump's probability of winning: 28.20%
Probability of tie: 0.41% ④ With 2012 polling bias
===============================
Harris's probability of winning: 71.38%
Trump's probability of winning: 28.20%
Probability of tie: 0.41% While we’re at it, let’s also have a look at how the 2022 polling bias—from the most recent and first “post-Covid” election—would change the predictions: Most significantly here, Michigan, Pennsylvania, and Wisconsin are now lean or likely D. And these are also the ones where Harris is the favorite in all three of the above scenarios. Chances of winning with the 2022 polling bias: ⑤ With 2022 polling bias
===============================
Harris's probability of winning: 66.56%
Trump's probability of winning: 33.10%
Probability of tie: 0.34% ⑤ With 2022 polling bias
===============================
Harris's probability of winning: 66.56%
Trump's probability of winning: 33.10%
Probability of tie: 0.34% So, what are we gonna do with this now? What’s the prediction? Who has the better chance of winning the election, and how good are they? I have no clue whatsoever. As I already said above, it’s all 🔮ing at this point. There are so many variables in this, so many things that could go one or the other way … What we can do at best is think about which scenarios are maybe a bit more likely than others. My opinion on this: Accurate polls (①) and the same polling bias as in 2016/20 (②) are rather unlikely. This leaves ③ (half 2016/20 bias), ④ (2012 bias), and ⑤ (2022 bias) as the more likely of my scenarios. the In any case, the election is gonna happen, there’ll be a winner, and there’ll be one option that is closer than the others. All we need is just a little more patience. Please also feel free to head over to my Github—where I'll collect and archive everything—and have a look at the complete data and calculations: https://github.com/maxspeicher/2024-us-presidential-election . I also post regular updates on Bluesky and Twitter . https://github.com/maxspeicher/2024-us-presidential-election Bluesky Twitter ☕ I love coffee, and if you want to support my work, you can always send me one, or subscribe to my newsletter. 🗞️ ☕ I love coffee, and if you want to support my work, you can always send me one , or subscribe to my newsletter . 🗞️ send me one send me one my newsletter my newsletter

Election Forecasts, Schmelection Forecasts

Meteorology is rapidly evolving due to the development of new technology. Traditional forecasting methods, which relied heavily on physical instruments and historical data, are now being reshaped by integrating artificial intelligence (AI) and machine learning. This shift is expected to improve the accuracy and granularity of forecasts and fundamentally change how we interact with weather information. Bridging the Gap Between Short-Term and Long-Term Forecasts One of the most intriguing advancements in meteorology is the use of AI to bridge the gap between short-term and long-term forecasting. Traditionally, predicting weather events a few minutes to 24 hours ahead has been a challenge. Machine learning models are now being trained on vast datasets to predict weather patterns with unprecedented precision. These models can integrate real-time data, such as satellite imagery and user-generated reports, to produce hyperlocal forecasts. Being able to predict severe weather, like sudden storms or heavy rain, more quickly and accurately could be a big breakthrough soon. Enhancing User Experience Improvements in user experience must complement technological advancements. Integrating these new technologies in a way that is accessible and useful to the average user is one of the biggest challenges. Weather apps have become a primary source of information for many and must evolve to present complex data in a simple and actionable manner. The key to success lies in user-centric design. People need information quickly and in a format that is easy to understand, whether it’s for a sporting event, a morning commute, or planning a trip. The challenge is to personalize this information without overwhelming the user. Towards a More Adaptive Forecasting System Personalization is the future of weather apps. Imagine a weather app that knows you’re about to go for a run and gives you a tailored forecast for your route and time of day. Or one that alerts you to potential weather disruptions based on your calendar events. This level of personalization, however, raises concerns about data privacy and user consent. It’s crucial for companies to be transparent about how they use data, and users should have control over what they share. With responsible practices, personalized experiences can enhance usability without compromising privacy. The Role of AI in Meteorology The pioneers in using AI assistants in weather apps were Pixel phones (which analyze forecast data and offer summaries). Soon, AI hints appeared in the Rain Viewer weather app. I am sure that this is just the beginning of the trend, and soon, we will see the widespread use of AI in weather apps. Predicting the weather for the next decade is challenging due to the fast-paced technological advancements. AI is playing a crucial role in this evolution. The focus has shifted from hardware advancements, like new sensors, to developing sophisticated software that enhances weather prediction algorithms. By using big data, AI refines both short-term and long-term forecasts. Even though physical instruments might not change much, new software will soon make weather forecasts more accurate and reliable. Current forecasting models cannot process all the available data effectively, especially satellite information. There are five operational meteorological satellites, yet we can’t fully utilize their data. Most of it just gets stored as images without being immediately analyzed. The challenge is in processing all the available massive amounts of data from modern meteorological equipment in real time to provide hyperlocal forecasts. AI and machine learning can help fill this gap by automating data analysis and improving forecast accuracy. The breakthrough will be predicting exactly which houses a tornado will hit so that people can evacuate in time (now the Tornado Warning issues for large territories, and everyone knows that “there will be no tornado” and ignore it due to lack of such hyperlocal predictions). Simplifying User Experience Through Integration The future of weather forecasting is moving towards simplifying how information is delivered and processed. In the near future, you may walk past a digital billboard that shows you personalized weather information—like a forecast for your specific location—without needing to pull out your phone. The goal is to integrate weather data seamlessly into our daily environments. Dedicated weather apps may not be necessary, as the information will be readily accessible on any screen and personalized to our needs, current location, and activity. Weather Apps as Part of a Broader Ecosystem As standalone entities, weather apps may become redundant. People don’t need weather apps just to know the weather. They need them to make decisions, like whether to take an umbrella, call a taxi, or plan a vacation. Weather data is already being incorporated into various ecosystems. Modern cars can replan you driving route because of severe weather events, and smart home thermostats can adjust temperature settings based on forecasts, smart irrigation systems check not only how soil is wet, but also how much precipitation will come in the next 24 hours. Weather information is everywhere—cars, fridges, calendars, and even when buying tickets for a trip. This trend will continue, making weather forecasting an integral part of our everyday decisions rather than a separate service. Back to Basics: The Future of Weather Apps Interestingly, the future of weather apps might involve a return to simplicity. Over time, as features and complexities were added, many apps lost their initial value. The essence of a good weather app lies in its ease of use and clarity of information. The focus should be on creating an intuitive, straightforward experience where users can access relevant information without hassle. A More Integrated, User-Friendly Future As weather forecasting technology advances, it will become more important in our daily decisions. The focus will be on delivering relevant, timely, and personalized information through various digital platforms and devices. The future of weather forecasting is about making predictions a natural part of our daily lives, helping us make better decisions and simplifying our routines. It’s an exciting time for meteorology, and we are only beginning to scratch the surface of what is possible. The power of weather forecasting is in how it helps make our lives safer and easier. As technology advances, we must harness this potential to create a future where we are always ready for the weather, not just reacting to it. Meteorology is rapidly evolving due to the development of new technology. Traditional forecasting methods, which relied heavily on physical instruments and historical data, are now being reshaped by integrating artificial intelligence (AI) and machine learning. This shift is expected to improve the accuracy and granularity of forecasts and fundamentally change how we interact with weather information. Bridging the Gap Between Short-Term and Long-Term Forecasts One of the most intriguing advancements in meteorology is the use of AI to bridge the gap between short-term and long-term forecasting. Traditionally, predicting weather events a few minutes to 24 hours ahead has been a challenge. Machine learning models are now being trained on vast datasets to predict weather patterns with unprecedented precision. These models can integrate real-time data, such as satellite imagery and user-generated reports, to produce hyperlocal forecasts. Being able to predict severe weather, like sudden storms or heavy rain, more quickly and accurately could be a big breakthrough soon. Enhancing User Experience Improvements in user experience must complement technological advancements. Integrating these new technologies in a way that is accessible and useful to the average user is one of the biggest challenges. Weather apps have become a primary source of information for many and must evolve to present complex data in a simple and actionable manner. The key to success lies in user-centric design. People need information quickly and in a format that is easy to understand, whether it’s for a sporting event, a morning commute, or planning a trip. The challenge is to personalize this information without overwhelming the user. Towards a More Adaptive Forecasting System Personalization is the future of weather apps. Imagine a weather app that knows you’re about to go for a run and gives you a tailored forecast for your route and time of day. Or one that alerts you to potential weather disruptions based on your calendar events. This level of personalization, however, raises concerns about data privacy and user consent. It’s crucial for companies to be transparent about how they use data, and users should have control over what they share. With responsible practices, personalized experiences can enhance usability without compromising privacy. The Role of AI in Meteorology The pioneers in using AI assistants in weather apps were Pixel phones (which analyze forecast data and offer summaries). Soon, AI hints appeared in the Rain Viewer weather app. I am sure that this is just the beginning of the trend, and soon, we will see the widespread use of AI in weather apps. Predicting the weather for the next decade is challenging due to the fast-paced technological advancements. AI is playing a crucial role in this evolution. The focus has shifted from hardware advancements, like new sensors, to developing sophisticated software that enhances weather prediction algorithms. By using big data, AI refines both short-term and long-term forecasts. Even though physical instruments might not change much, new software will soon make weather forecasts more accurate and reliable. Current forecasting models cannot process all the available data effectively, especially satellite information. There are five operational meteorological satellites, yet we can’t fully utilize their data. Most of it just gets stored as images without being immediately analyzed. The challenge is in processing all the available massive amounts of data from modern meteorological equipment in real time to provide hyperlocal forecasts. AI and machine learning can help fill this gap by automating data analysis and improving forecast accuracy. The breakthrough will be predicting exactly which houses a tornado will hit so that people can evacuate in time (now the Tornado Warning issues for large territories, and everyone knows that “there will be no tornado” and ignore it due to lack of such hyperlocal predictions). Simplifying User Experience Through Integration The future of weather forecasting is moving towards simplifying how information is delivered and processed. In the near future, you may walk past a digital billboard that shows you personalized weather information—like a forecast for your specific location—without needing to pull out your phone. The goal is to integrate weather data seamlessly into our daily environments. Dedicated weather apps may not be necessary, as the information will be readily accessible on any screen and personalized to our needs, current location, and activity. Weather Apps as Part of a Broader Ecosystem As standalone entities, weather apps may become redundant. People don’t need weather apps just to know the weather. They need them to make decisions, like whether to take an umbrella, call a taxi, or plan a vacation. Weather data is already being incorporated into various ecosystems. Modern cars can replan you driving route because of severe weather events, and smart home thermostats can adjust temperature settings based on forecasts, smart irrigation systems check not only how soil is wet, but also how much precipitation will come in the next 24 hours. Weather information is everywhere—cars, fridges, calendars, and even when buying tickets for a trip. This trend will continue, making weather forecasting an integral part of our everyday decisions rather than a separate service. Back to Basics: The Future of Weather Apps Interestingly, the future of weather apps might involve a return to simplicity. Over time, as features and complexities were added, many apps lost their initial value. The essence of a good weather app lies in its ease of use and clarity of information. The focus should be on creating an intuitive, straightforward experience where users can access relevant information without hassle. A More Integrated, User-Friendly Future As weather forecasting technology advances, it will become more important in our daily decisions. The focus will be on delivering relevant, timely, and personalized information through various digital platforms and devices. The future of weather forecasting is about making predictions a natural part of our daily lives, helping us make better decisions and simplifying our routines. It’s an exciting time for meteorology, and we are only beginning to scratch the surface of what is possible. The power of weather forecasting is in how it helps make our lives safer and easier. As technology advances, we must harness this potential to create a future where we are always ready for the weather, not just reacting to it.

What Could the Future of Weather Forecasting Look Like?

Artificial intelligence (AI) has made its way into banking and decentralized finance, so it’s no wonder people are now using it to forecast economic trends. Can it trade stocks better than an expert? Will the federal government look kindly upon its role? While only time can tell, several signs provide insight into those questions. The Difference Between Econometric Models and AI Traditional economic forecasts rely on statistical models and analytics tools. They can't really make predictions unless input data is in a particular, structured format. They’re not data-driven to the extent AI is — few tools can keep pace with its processing speeds. Human intuition is far less accurate than econometric models, but it’s used just as often. Instead of plugging numbers into a spreadsheet and paying attention to market forecasts, people make predictions based on their expertise and gut feelings. These strategies are often inaccurate. Take forecasts on the United States Consumer Price Index, for example. While experts estimated prices would rise by 7.5% in 2022, they actually increased by 6.5%, according to the U.S. Bureau of Labor Statistics. AI outperforms econometric models and human intuition. It can interpret complex relationships between variables, even considering outliers on a weighted scale. Also, it mitigates loss aversion, overconfidence, and the gambler’s fallacy, preventing biases from skewing forecasts. AI enables automation, so it can execute trades, adjust prices, or transfer funds at the most advantageous times. Naturally, it also reduces administrative work. According to one estimate, it could automate up to 70% of the workday today. Examples of AI Techniques Used to Forecast Trends Whatever AI technique a person uses influences their forecast generation approach. With machine learning, they can uncover nonlinear relationships between variables. These models can __process complex data in milliseconds __and update it whenever they receive new information. A neural network uses a series of interconnected nodes to recognize patterns. It’s inspired by the human brain — the artificial neurons work together in real-time to solve problems. As a result, its forecasts are precise and reasonable. With natural language processing, someone can process text-based data to make forecasts. This system can also analyze sentiment to determine tone and mood. Instead of pressing buttons, updating a spreadsheet, or entering code, the user can use plain language to generate output. Image recognition works similarly, analyzing images and videos to reveal hidden patterns or forecast trends. For example, when leveraged in an AI-powered surveillance system, it could evaluate foot traffic and purchase frequency to predict the following month’s sales figures. The Implications of AI Forecasts on Various Sectors Naturally, the finance sector will be most impacted by AI-driven economic forecasts. It could automate stock trading, predict inflation, and estimate growth rates with unparalleled accuracy. However, no technology is infallible — an algorithm simply doesn’t have the expertise of an industry professional, even if it has access to massive amounts of data. A good hypothetical is necessary to explain. During the non-fungible token craze, an algorithm may have misinterpreted the fad as a valuable emerging alternative investment. It couldn’t have understood the general public’s negative sentiment, which would’ve skewed its findings. The same situation could happen in e-commerce, where forecasts are vital for knowing how much to order and when to change prices. On one hand, AI could align retailers’ actions with customer demand, improving brand loyalty. Conversely, it might misread the situation because of incomplete information, leading to poor business outcomes. Whether the implications are more positive or negative, big change is coming. As a result, government policies may evolve. They’ll likely target data privacy and copyright instead of policing the act of forecasting. The Senate Task Force on AI has passed at least 15 bills to date, with more on the way. It probably won’t be nearly as strict as European lawmakers. However, any privacy and copyright laws limit what data companies can use in training and forecasting, which would reduce model accuracy right off the bat. Will AI Become the Standard for Forecasting Trends? Since AI is relatively new, it likely won’t replace industry experts and professional stock traders anytime soon. However, whether its hype continues, it will likely have a permanent place in forecasting since it enables automation and rapid insight generation. Artificial intelligence (AI) has made its way into banking and decentralized finance, so it’s no wonder people are now using it to forecast economic trends. Can it trade stocks better than an expert? Will the federal government look kindly upon its role? While only time can tell, several signs provide insight into those questions. The Difference Between Econometric Models and AI Traditional economic forecasts rely on statistical models and analytics tools. They can't really make predictions unless input data is in a particular, structured format. They’re not data-driven to the extent AI is — few tools can keep pace with its processing speeds. rely on statistical models rely on statistical models Human intuition is far less accurate than econometric models, but it’s used just as often. Instead of plugging numbers into a spreadsheet and paying attention to market forecasts, people make predictions based on their expertise and gut feelings. These strategies are often inaccurate. Take forecasts on the United States Consumer Price Index, for example. While experts estimated prices would rise by 7.5% in 2022, they actually increased by 6.5% , according to the U.S. Bureau of Labor Statistics. would rise by 7.5% would rise by 7.5% actually increased by 6.5% actually increased by 6.5% AI outperforms econometric models and human intuition. It can interpret complex relationships between variables, even considering outliers on a weighted scale. Also, it mitigates loss aversion, overconfidence, and the gambler’s fallacy, preventing biases from skewing forecasts. AI enables automation, so it can execute trades, adjust prices, or transfer funds at the most advantageous times. Naturally, it also reduces administrative work. According to one estimate, it could automate up to 70% of the workday today. up to 70% of the workday up to 70% of the workday Examples of AI Techniques Used to Forecast Trends Whatever AI technique a person uses influences their forecast generation approach. With machine learning, they can uncover nonlinear relationships between variables. These models can __ process complex data in milliseconds __and update it whenever they receive new information. process complex data in milliseconds A neural network uses a series of interconnected nodes to recognize patterns. It’s inspired by the human brain — the artificial neurons work together in real-time to solve problems. As a result, its forecasts are precise and reasonable. With natural language processing, someone can process text-based data to make forecasts. This system can also analyze sentiment to determine tone and mood. Instead of pressing buttons, updating a spreadsheet, or entering code, the user can use plain language to generate output. Image recognition works similarly, analyzing images and videos to reveal hidden patterns or forecast trends. For example, when leveraged in an AI-powered surveillance system, it could evaluate foot traffic and purchase frequency to predict the following month’s sales figures. The Implications of AI Forecasts on Various Sectors Naturally, the finance sector will be most impacted by AI-driven economic forecasts. It could automate stock trading, predict inflation, and estimate growth rates with unparalleled accuracy. However, no technology is infallible — an algorithm simply doesn’t have the expertise of an industry professional, even if it has access to massive amounts of data. A good hypothetical is necessary to explain. During the non-fungible token craze, an algorithm may have misinterpreted the fad as a valuable emerging alternative investment. It couldn’t have understood the general public’s negative sentiment, which would’ve skewed its findings. The same situation could happen in e-commerce, where forecasts are vital for knowing how much to order and when to change prices. On one hand, AI could align retailers’ actions with customer demand, improving brand loyalty. Conversely, it might misread the situation because of incomplete information, leading to poor business outcomes. Whether the implications are more positive or negative, big change is coming. As a result, government policies may evolve. They’ll likely target data privacy and copyright instead of policing the act of forecasting. The Senate Task Force on AI has passed at least 15 bills to date, with more on the way. It probably won’t be nearly as strict as European lawmakers. However, any privacy and copyright laws limit what data companies can use in training and forecasting, which would reduce model accuracy right off the bat. passed at least 15 bills passed at least 15 bills Will AI Become the Standard for Forecasting Trends? Since AI is relatively new, it likely won’t replace industry experts and professional stock traders anytime soon. However, whether its hype continues, it will likely have a permanent place in forecasting since it enables automation and rapid insight generation.

The Role of AI in Forecasting Economic Trends

Andrea Renzetti, Department of Economics, Alma Mater Studiorium Universit`a di Bologna, Piazza Scaravilli 2, 40126 Bologna, Italy. Table of Links Abstract and Introduction Theory coherent TVP-VAR Forecasting with the TC-TVP-VAR Response analysis at the ZLB with the TC-TVP-VAR Conclusion and References A Appendix 3 Forecasting with the TC-TVP-VAR In this section I consider the problem of forecasting the rate of growth of GDP, the inflation rate and the Fed Fund rate using a trivariate TVP-VAR model. In the specific, I estimate a trivariate TVP-VAR for the US economy using data from 1970 up to 2019 and I compare the forecast accuracy of a standard TVP-VAR model to the forecasts from a TC-TVP-VAR. 3.1 Small scale New Keynesian model for the US economy In the TC-TVP-VAR I exploit the New Keynesian model in Del Negro et al. (2004) to parametrize the theory coherent prior. The conceptual framework commonly denoted as the 3-equation New Keynesian model constitutes the nucleus of Michael Woodford’s book “Interest and Prices” (Woodford 2003) and underpins most of modern monetary macroeconomics models.[21] More specifically, the structural model is composed by an IS curve (31), a New Keynesian Phillips curve (32), a monetary policy rule (33) and two equations that describe the dynamics for the log-deviation from the steady state of technological process (34) and government spending (35), namely The population moments needed to parametrize the prior are derived from the state-space representation of the New Keynesian model obtained by solving the system of non-linear rational expectation equations. In particular, the non-linear rational expectation equations are solved using the method based on matrix eigenvalue decomposition by Sims (2002) leading to a solution which has the form 3.2 Forecast comparison Table 1 shows the comparison of the forecasts from a standard TVP-VAR model and the TCTVP-VAR model. The forecasting exercise is designed such that I compute the recursive one quarter, two quarters, and one year ahead forecasts starting from 1985-Q1 up to 2019-Q4.22 To compare relative point forecast accuracy, in the table I report the Root Mean Squared Error (RMSE) while for evaluating density forecast accuracy I report the average Cumulative Ranked Probability Scores (CRPS). In the table I also include the results concerning the forecasts from a constant parameters VAR with flat prior and a constant parameters Bayesian VAR (BVAR) with Minnesota type of prior.23 Overall, the TC-TVP-VAR provides the most accurate point and density forecasts for both output growth and inflation rate, outperforming the standard TVP-VAR model at all the horizons considered. For forecasting output growth, the standard TVP-VAR performs poorly relative to the TC-TVP-VAR, but also to the constant parameters BVAR with Minnesota prior, suggesting that the model tends to fit some noise in the time series of output growth. In line with the previous forecasting literature, allowing for time variation of the parameters of the VAR is important for obtaining accurate forecasts of the inflation rate, as the standard TVP-VAR outperforms both the VAR with flat prior and the BVAR with the Minnesota prior. However, economic shrinkage is helpful to obtain more reliable point and density forecasts when modelling the time variation of the coefficients. Indeed the TC-TVP-VAR outperforms the standard TVP-VAR at all the horizons. As a caveat, the standard Minnesota prior centering the autoregressive coefficients on a random walk process outperforms the other competitors, including the TC-TVP-VAR, for forecasting the Fed Fund Rate. This result is consistent with the results of the forecasting exercise in Del Negro et al. (2004) which use the same small scale NK model to parametrize a prior for a constant parameters VAR. This paper is available on arxiv under CC 4.0 license. [21.] See Del Negro et al. (2004) for the more in depth details on the New Keynesian Model. [22.] Details on the data can be found in the appendix A.2.1 [23.] Appendix A.2.2 the reports details on the competing forecasting models Andrea Renzetti, Department of Economics, Alma Mater Studiorium Universit`a di Bologna, Piazza Scaravilli 2, 40126 Bologna, Italy. Andrea Renzetti, Department of Economics, Alma Mater Studiorium Universit`a di Bologna, Piazza Scaravilli 2, 40126 Bologna, Italy. Table of Links Abstract and Introduction Abstract and Introduction Theory coherent TVP-VAR Theory coherent TVP-VAR Forecasting with the TC-TVP-VAR Forecasting with the TC-TVP-VAR Response analysis at the ZLB with the TC-TVP-VAR Response analysis at the ZLB with the TC-TVP-VAR Conclusion and References Conclusion and References A Appendix A Appendix 3 Forecasting with the TC-TVP-VAR In this section I consider the problem of forecasting the rate of growth of GDP, the inflation rate and the Fed Fund rate using a trivariate TVP-VAR model. In the specific, I estimate a trivariate TVP-VAR for the US economy using data from 1970 up to 2019 and I compare the forecast accuracy of a standard TVP-VAR model to the forecasts from a TC-TVP-VAR. 3.1 Small scale New Keynesian model for the US economy In the TC-TVP-VAR I exploit the New Keynesian model in Del Negro et al. (2004) to parametrize the theory coherent prior. The conceptual framework commonly denoted as the 3-equation New Keynesian model constitutes the nucleus of Michael Woodford’s book “Interest and Prices” (Woodford 2003) and underpins most of modern monetary macroeconomics models.[21] More specifically, the structural model is composed by an IS curve (31), a New Keynesian Phillips curve (32), a monetary policy rule (33) and two equations that describe the dynamics for the log-deviation from the steady state of technological process (34) and government spending (35), namely The population moments needed to parametrize the prior are derived from the state-space representation of the New Keynesian model obtained by solving the system of non-linear rational expectation equations. In particular, the non-linear rational expectation equations are solved using the method based on matrix eigenvalue decomposition by Sims (2002) leading to a solution which has the form 3.2 Forecast comparison Table 1 shows the comparison of the forecasts from a standard TVP-VAR model and the TCTVP-VAR model. The forecasting exercise is designed such that I compute the recursive one quarter, two quarters, and one year ahead forecasts starting from 1985-Q1 up to 2019-Q4.22 To compare relative point forecast accuracy, in the table I report the Root Mean Squared Error (RMSE) while for evaluating density forecast accuracy I report the average Cumulative Ranked Probability Scores (CRPS). In the table I also include the results concerning the forecasts from a constant parameters VAR with flat prior and a constant parameters Bayesian VAR (BVAR) with Minnesota type of prior.23 Overall, the TC-TVP-VAR provides the most accurate point and density forecasts for both output growth and inflation rate, outperforming the standard TVP-VAR model at all the horizons considered. For forecasting output growth, the standard TVP-VAR performs poorly relative to the TC-TVP-VAR, but also to the constant parameters BVAR with Minnesota prior, suggesting that the model tends to fit some noise in the time series of output growth. In line with the previous forecasting literature, allowing for time variation of the parameters of the VAR is important for obtaining accurate forecasts of the inflation rate, as the standard TVP-VAR outperforms both the VAR with flat prior and the BVAR with the Minnesota prior. However, economic shrinkage is helpful to obtain more reliable point and density forecasts when modelling the time variation of the coefficients. Indeed the TC-TVP-VAR outperforms the standard TVP-VAR at all the horizons. As a caveat, the standard Minnesota prior centering the autoregressive coefficients on a random walk process outperforms the other competitors, including the TC-TVP-VAR, for forecasting the Fed Fund Rate. This result is consistent with the results of the forecasting exercise in Del Negro et al. (2004) which use the same small scale NK model to parametrize a prior for a constant parameters VAR. This paper is available on arxiv under CC 4.0 license. This paper is available on arxiv under CC 4.0 license. available on arxiv [21.] See Del Negro et al. (2004) for the more in depth details on the New Keynesian Model. [22.] Details on the data can be found in the appendix A.2.1 [23.] Appendix A.2.2 the reports details on the competing forecasting models

Theory Coherent Shrinkage of Time Varying Parameters in VARs: Forecasting with the TC-TVP-VAR

There have been several exciting AI developments in the retail industry in recent years, all of which could help you streamline various aspects of your business. ChatGPT aside, advancements in machine learning and adaptive AI put more power into your hands when it comes to automating core processes and using existing data to make accurate forecasts on future trends. AI demand forecasting should be of particular interest to you if you’re curious about how AI can help you manage inventory. What is AI Demand Forecasting? AI demand forecasting involves the use of machine learning algorithms to predict when stock is likely to run low based on customer behaviour and purchasing trends. By accessing historic data, AI can tap into analytics models to accurately estimate when you might run into issues with stock so you can be proactive rather than reactive in your approach to inventory management. You can also use AI to run different simulations and imagined scenarios based on product price, consumer demand, and market conditions to get a clear idea of how to introduce new products to the market and keep stock levels high enough to satisfy demand. What are the Benefits? There are several reasons you might want to employ AI demand forecasting in your retail business. Avoid Overstock and Stockouts Both overstock and stockouts are common issues for retail businesses, and they can come at a significant cost. Overstock, when you have more inventory than you need to meet demand, often results from overestimating the demand for a product. The issue here is you could end up sitting on a stock that never ships. Stockouts, on the other hand, refer to when you don’t have enough stock to meet demand due to underestimating demand. This, of course, means you could lose out on a lot of money as consumers can’t buy from you when they want to and might not return to make the investment. With AI demand forecasting, you can more confidently avoid both stocking issues. Automating parts of the supply chain could increase revenue by up to 4%, reduce inventory by up to 20%, and decrease supply chain costs by up to 10%. Grounding your stock control decisions in concrete data and accurate projections is one of the main benefits of investing in AI. Increase Product Availability to Meet Demand Whenever consumers are met with an “out of stock” message upon checkout, it doesn’t send a good signal. For many, it can be frustrating and worse for you, as the retailer can send them to alternative solutions from your competitors. Unless you have exceptionally strong branding and unique products, many consumers would sooner buy what they need elsewhere than wait for the product to come back in stock. If you can show your consumers that you reliably have products in stock - and even when you do run low and replace stocks quickly - you put yourself in a great position to secure their repeat business. A study by PwC showed that 42% of consumers in 2023 cited products being out of stock as one of the biggest factors in their shopping experience. With AI demand forecasting, you can make sure that product availability meets demand more often than not. Improve Coordination with Suppliers Your supply chain is crucial to your ability to meet demand and keep your customers satisfied. As such, it’s important that you take every precaution to ensure the chain doesn’t break down at any point. According to McKinsey & Company, AI-driven forecasting to supply chain management can cut down errors by between 20-50%. With AI demand forecasting, you can improve your communication and coordination with suppliers as you’re more readily able to provide forecasts and projections based on what you need for the upcoming months. Are there any Limitations? While AI demand forecasting is largely reliable, there are several factors to consider before investing, including: The need for high-quality data - In order to make the most of AI demand forecasting, you need to feed it high-quality data so it can make the most accurate predictions. If the data you have isn’t clear, your mileage will vary.
The need for staff training - As with any new software solution or tool, AI demand forecasting requires a certain level of expertise to use well.
Ongoing cost - After the initial cost of the software, you’ll also have to consider a likely subscription fee and any costs associated with employee training. A study by IBM showed that 33% of enterprise-scale companies cited AI skills and expertise as a barrier to introducing AI, and 25% mentioned data complexity. There have been several exciting AI developments in the retail industry in recent years, all of which could help you streamline various aspects of your business. ChatGPT aside, advancements in machine learning and adaptive AI put more power into your hands when it comes to automating core processes and using existing data to make accurate forecasts on future trends. AI demand forecasting should be of particular interest to you if you’re curious about how AI can help you manage inventory. What is AI Demand Forecasting? AI demand forecasting involves the use of machine learning algorithms to predict when stock is likely to run low based on customer behaviour and purchasing trends. By accessing historic data, AI can tap into analytics models to accurately estimate when you might run into issues with stock so you can be proactive rather than reactive in your approach to inventory management. You can also use AI to run different simulations and imagined scenarios based on product price, consumer demand, and market conditions to get a clear idea of how to introduce new products to the market and keep stock levels high enough to satisfy demand. What are the Benefits? There are several reasons you might want to employ AI demand forecasting in your retail business. Avoid Overstock and Stockouts Both overstock and stockouts are common issues for retail businesses, and they can come at a significant cost. Overstock, when you have more inventory than you need to meet demand, often results from overestimating the demand for a product. The issue here is you could end up sitting on a stock that never ships. Stockouts, on the other hand, refer to when you don’t have enough stock to meet demand due to underestimating demand. This, of course, means you could lose out on a lot of money as consumers can’t buy from you when they want to and might not return to make the investment. With AI demand forecasting, you can more confidently avoid both stocking issues. Automating parts of the supply chain could increase revenue by up to 4% , reduce inventory by up to 20%, and decrease supply chain costs by up to 10%. increase revenue by up to 4% increase revenue by up to 4% Grounding your stock control decisions in concrete data and accurate projections is one of the main benefits of investing in AI. Increase Product Availability to Meet Demand Whenever consumers are met with an “out of stock” message upon checkout, it doesn’t send a good signal. For many, it can be frustrating and worse for you, as the retailer can send them to alternative solutions from your competitors. Unless you have exceptionally strong branding and unique products, many consumers would sooner buy what they need elsewhere than wait for the product to come back in stock. If you can show your consumers that you reliably have products in stock - and even when you do run low and replace stocks quickly - you put yourself in a great position to secure their repeat business. A study by PwC showed that 42% of consumers in 2023 cited products being out of stock as one of the biggest factors in their shopping experience. 42% of consumers 42% of consumers With AI demand forecasting, you can make sure that product availability meets demand more often than not. Improve Coordination with Suppliers Your supply chain is crucial to your ability to meet demand and keep your customers satisfied. As such, it’s important that you take every precaution to ensure the chain doesn’t break down at any point. According to McKinsey & Company, AI-driven forecasting to supply chain management can cut down errors by between 20-50%. McKinsey & Company, McKinsey & Company, With AI demand forecasting, you can improve your communication and coordination with suppliers as you’re more readily able to provide forecasts and projections based on what you need for the upcoming months. Are there any Limitations? While AI demand forecasting is largely reliable, there are several factors to consider before investing, including: The need for high-quality data - In order to make the most of AI demand forecasting, you need to feed it high-quality data so it can make the most accurate predictions. If the data you have isn’t clear, your mileage will vary. The need for staff training - As with any new software solution or tool, AI demand forecasting requires a certain level of expertise to use well. Ongoing cost - After the initial cost of the software, you’ll also have to consider a likely subscription fee and any costs associated with employee training. The need for high-quality data - In order to make the most of AI demand forecasting, you need to feed it high-quality data so it can make the most accurate predictions. If the data you have isn’t clear, your mileage will vary. The need for high-quality data The need for staff training - As with any new software solution or tool, AI demand forecasting requires a certain level of expertise to use well. The need for staff training Ongoing cost - After the initial cost of the software, you’ll also have to consider a likely subscription fee and any costs associated with employee training. Ongoing cost A study by IBM showed that 33% of enterprise-scale companies cited AI skills and expertise as a barrier to introducing AI, and 25% mentioned data complexity. IBM IBM

AI Demand Forecasting: Could It Improve Stock Control?

ARK Investment Management LLC predicts that AI agents will influence up to $9 trillion of global online sales of consumer goods and services by the end of the decade. This number is almost negligible today. In their recent research, "Generative AI: A New Consumer Operating System" they shared valuable insights into the future of AI in consumer technology. One key insight is the potential for search engines to ban AI-led traffic from specific operating systems, and likewise, for marketplaces to ban traffic from certain search engines due to competitive concerns. "If consumers come to rely on AI engines instead of manual research, search engines, marketplaces, and independent websites will lose significant traffic and ad impressions as AI agents take control of the most valuable ad impressions during the transaction process." However, the blog also suggests that, in the long run, "AI agents at the operating system level will aim to maximize consumer utility, encouraging most marketplaces to offer their inventory through these agents." Yes, it's no secret that a significant portion of our consumption decisions will be influenced by AI agents in the future. But, how will these agents shape the way we are influenced and make purchasing decisions? Personalization is no longer just a buzzword, nor is it limited to changing the color of a landing page based on predictive user anaysis. Now, let's break down the mechanisms of AI agents in advertising, as illustrated by the authors: Traditional Online Shopping Experience: Awareness Phase: The user initiates the process with a search query, typically typed into a search engine. Discovery + Research Phase: The user explores the search results, often clicking on links to marketplaces or merchant websites. This phase can involve multiple iterations, with the user navigating back and forth between the search engine and various merchant sites. Ad Opportunities: During this process, the user encounters numerous ads, both on search engines and merchant websites, providing abundant opportunities for digital ad impressions. Checkout: Once the user finds their desired product, they proceed to checkout. AI-Agent Powered Online Shopping Experience: Awareness Phase: The user initiates the process with a text or voice command to their personalized AI agent, integrated into their device or operating system. Autonomous Discovery + Research Phase: The AI agent takes charge, querying multiple search engines and marketplaces simultaneously. Curated Results: The AI agent compiles and presents refined results, reducing the user's active involvement in discovery and research. Checkout: The user reviews the curated results and proceeds to checkout, similar to the traditional process. So, what are the key differences and implications? First, efficiency: The AI agent-powered process offloads the discovery and research burden from the user to the agent, enhancing efficiency and saving time. Ad Opportunities in the Traditional Model: The user is exposed to multiple ads throughout their journey across search engines and merchant sites. Ad Opportunities in the AI Model: More limited and concentrated within the AI agent's curated results, reducing ad impressions. In the traditional model, the user actively engages in each step, requiring more effort and time. The AI-powered model offers a streamlined process where the user delegates most tasks to the AI agent, potentially improving the overall user experience. The digital advertising ecosystem is indeed poised for substantial disruption due to the increasing role of AI assistants. This will result in a reduction of advertising opportunities in search results, on marketplace platforms, and on independent websites. To maintain user trust, AI agents will need to curate advertising content that is natural, unobtrusive, and respectful of user privacy. Two anticipated forms of AI agent advertising are: Retrieval-based ads: Targeted ads presented alongside organic curation, similar to today's search ads.
Active intent-generation ads: Personalized recommendations based on user-authorized data from various sources. With these developments, the influence of AI agents in consumer spending will grow, and AI-facilitated ad spend is projected to reach an astonishing $9 trillion globally by the year 2030. AI agents are estimated to extract more than $800 billion in intermediation revenue from this massive ad spend. Now, let's turn our attention to another valuable insight from the blog: Voice and Natural User Interfaces (NUIs). Voice-centric NUIs, coupled with real-time data from AI wearables, will take personalization to the next level, transforming hardware into predictive assistants. Voice is becoming the new medium obviously. However, voice and AI wearables are expected to augment rather than replace smartphones, as privacy concerns and the need for audiovisual entertainment will likely maintain the demand for screens. Lastly, let's consider the potential category winners and platform strategies brought to us by ARK researchers. The shifts in market share between PC and mobile operating systems suggest that winning consumer hardware platforms will employ varied strategies. Vertical integration has proven more successful than licensing models, as evident by the dominance of Apple, Google, and Microsoft in terms of market share and ecosystem profits.’ Established tech giants like Apple, Google, Microsoft, and Meta possess significant advantages, including their vast ecosystems, data and AI capabilities, and extensive distribution networks. "By strategically incorporating AI, these companies could further strengthen their moats. Apple could enhance user experiences with more personalized and intuitive interactions. Google could make its services even more indispensable by leveraging its data and AI expertise. Microsoft could solidify its leadership in both consumer and business markets by boosting productivity and enterprise solutions with AI. Meta could revolutionize user engagement and experiences across its social platforms and VR environments with AI-driven content, advertising, and immersive interactions." However, the paper also highlights the potential for new entrants, such as Humane Ai Pin and Rabbit R1, to disrupt the market by focusing on consumer-centric innovations that extend beyond the ecosystems of the current leaders, offering user-friendly designs that cater to unmet needs. It's super exciting to consider the role of AI agents in our purchasing decisions and how this might evolve in the near future. AI agents will save us time, provide personalized recommendations, and even negotiate better deals. So, what do you think? Are you open to the idea of AI agent-powered spending, or do you prefer to maintain control over your purchases? Have you had any positive or negative experiences with AI-assisted shopping thus far? ARK Investment Management LLC predicts that AI agents will influence up to $9 trillion of global online sales of consumer goods and services by the end of the decade. ARK Investment Management LLC This number is almost negligible today. In their recent research, " Generative AI: A New Consumer Operating System " they shared valuable insights into the future of AI in consumer technology. Generative AI: A New Consumer Operating System Generative AI: A New Consumer Operating System One key insight is the potential for search engines to ban AI-led traffic from specific operating systems, and likewise, for marketplaces to ban traffic from certain search engines due to competitive concerns. One key insight is the potential for search engines to ban AI-led traffic from specific operating systems, and likewise, for marketplaces to ban traffic from certain search engines due to competitive concerns. "If consumers come to rely on AI engines instead of manual research, search engines, marketplaces, and independent websites will lose significant traffic and ad impressions as AI agents take control of the most valuable ad impressions during the transaction process." "If consumers come to rely on AI engines instead of manual research, search engines, marketplaces, and independent websites will lose significant traffic and ad impressions as AI agents take control of the most valuable ad impressions during the transaction process." However, the blog also suggests that, in the long run, "AI agents at the operating system level will aim to maximize consumer utility, encouraging most marketplaces to offer their inventory through these agents." Yes, it's no secret that a significant portion of our consumption decisions will be influenced by AI agents in the future. consumption decisions will be influenced by AI agents in the future. But, how will these agents shape the way we are influenced and make purchasing decisions? Personalization is no longer just a buzzword, nor is it limited to changing the color of a landing page based on predictive user anaysis. Now, let's break down the mechanisms of AI agents in advertising, as illustrated by the authors: Traditional Online Shopping Experience: Awareness Phase: Awareness Phase: The user initiates the process with a search query, typically typed into a search engine. Discovery + Research Phase: Discovery + Research Phase: The user explores the search results, often clicking on links to marketplaces or merchant websites. This phase can involve multiple iterations, with the user navigating back and forth between the search engine and various merchant sites. Ad Opportunities: Ad Opportunities: During this process, the user encounters numerous ads, both on search engines and merchant websites, providing abundant opportunities for digital ad impressions. Checkout: Checkout: Once the user finds their desired product, they proceed to checkout. AI-Agent Powered Online Shopping Experience: Awareness Phase: Awareness Phase: The user initiates the process with a text or voice command to their personalized AI agent, integrated into their device or operating system. Autonomous Discovery + Research Phase: Autonomous Discovery + Research Phase: The AI agent takes charge, querying multiple search engines and marketplaces simultaneously. Curated Results: Curated Results: The AI agent compiles and presents refined results, reducing the user's active involvement in discovery and research. Checkout: Checkout: The user reviews the curated results and proceeds to checkout, similar to the traditional process. So, what are the key differences and implications? First, efficiency: The AI agent-powered process offloads the discovery and research burden from the user to the agent, enhancing efficiency and saving time. agent, enhancing efficiency and saving time. Ad Opportunities in the Traditional Model: The user is exposed to multiple ads throughout their journey across search engines and merchant sites. Ad Opportunities in the AI Model: More limited and concentrated within the AI agent's curated results , reducing ad impressions. More limited and concentrated within the AI agent's curated results In the traditional model, the user actively engages in each step, requiring more effort and time. The AI-powered model offers a streamlined process where the user delegates most tasks to the AI agent, potentially improving the overall user experience. The digital advertising ecosystem is indeed poised for substantial disruption due to the increasing role of AI assistants. This will result in a reduction of advertising opportunities in search results, on marketplace platforms, and on independent websites. reduction of advertising opportunities in search results, on marketplace platforms, and on independent websites. To maintain user trust, AI agents will need to curate advertising content that is natural, unobtrusive, and respectful of user privacy. Two anticipated forms of AI agent advertising are: Retrieval-based ads: Targeted ads presented alongside organic curation, similar to today's search ads. Active intent-generation ads: Personalized recommendations based on user-authorized data from various sources. Retrieval-based ads: Targeted ads presented alongside organic curation, similar to today's search ads. Active intent-generation ads: Personalized recommendations based on user-authorized data from various sources. With these developments, the influence of AI agents in consumer spending will grow, and AI-facilitated ad spend is projected to reach an astonishing $9 trillion globally by the year 2030. AI agents are estimated to extract more than $800 billion in intermediation revenue from this massive ad spend. Now, let's turn our attention to another valuable insight from the blog: Voice and Natural User Interfaces (NUIs). Voice and Natural User Interfaces (NUIs). Voice-centric NUIs, coupled with real-time data from AI wearables, will take personalization to the next level, transforming hardware into predictive assistants. Voice is becoming the new medium obviously. However, voice and AI wearables are expected to augment rather than replace smartphones, as privacy concerns and the need for audiovisual entertainment will likely maintain the demand for screens. voice and AI wearables are expected to augment rather than replace smartphones, Lastly, let's consider the potential category winners and platform strategies brought to us by ARK researchers. The shifts in market share between PC and mobile operating systems suggest that winning consumer hardware platforms will employ varied strategies. Vertical integration has proven more successful than licensing models, as evident by the dominance of Apple, Google, and Microsoft in terms of market share and ecosystem profits.’ Vertical integration has proven more successful than licensing models, as evident by the dominance of Apple, Google, and Microsoft in terms of market share and ecosystem profits.’ Established tech giants like Apple, Google, Microsoft, and Meta possess significant advantages, including their vast ecosystems, data and AI capabilities, and extensive distribution networks. "By strategically incorporating AI, these companies could further strengthen their moats. Apple could enhance user experiences with more personalized and intuitive interactions. Google could make its services even more indispensable by leveraging its data and AI expertise. Microsoft could solidify its leadership in both consumer and business markets by boosting productivity and enterprise solutions with AI. Meta could revolutionize user engagement and experiences across its social platforms and VR environments with AI-driven content, advertising, and immersive interactions." "By strategically incorporating AI, these companies could further strengthen their moats. Apple could enhance user experiences with more personalized and intuitive interactions. Google could make its services even more indispensable by leveraging its data and AI expertise. Microsoft could solidify its leadership in both consumer and business markets by boosting productivity and enterprise solutions with AI. Meta could revolutionize user engagement and experiences across its social platforms and VR environments with AI-driven content, advertising, and immersive interactions." However, the paper also highlights the potential for new entrants, such as Humane Ai Pin and Rabbit R1, to disrupt the market by focusing on consumer-centric innovations that extend beyond the ecosystems of the current leaders, offering user-friendly designs that cater to unmet needs. It's super exciting to consider the role of AI agents in our purchasing decisions and how this might evolve in the near future. AI agents will save us time, provide personalized recommendations, and even negotiate better deals. AI agents will save us time, provide personalized recommendations, and even negotiate better deals. So, what do you think? Are you open to the idea of AI agent-powered spending, or do you prefer to maintain control over your purchases? Have you had any positive or negative experiences with AI-assisted shopping thus far?

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