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Western Union to Present at the UBS Global Technology and AI Conference on December 4th
Architect of liberalisation Dr Manmohan Singh passes away at 92HENDERSON, Nev. (AP) — Ashlon Jackson scored a career-high 30 points and No. 14 Duke defeated No. 10 Kansas State, 73-62 on Monday, in the semifinals of the Ball Dawgs Classic. The Blue Devils (6-1) overcame an early 11-point deficit behind Jackon’s shooting hand to advance to Wednesday’s championship game against the winner of the game between No. 9 Oklahoma and DePaul. Jackson, who has scored in double figures in all six of Duke’s games, shot 12 of 19 (63.1%) from the floor, including 6 of 9 (66.7%) from 3-point range. Reigan Richardson added 16 points for the Blue Devils. Kansas State (5-1) was led by Ayoka Lee, who had 16 points. Serena Sundell scored 15 and Kennedy Taylor came off the bench to add 11 for the Wildcats. Kansas State: With her 16-point performance, Lee needs 48 points to pass Kendra Wecker (2001-05) for the Kansas State career scoring record. Wecker scored 2,333 points. Lee, the 2024-25 Preseason Big 12 Player of the Year, is averaging 15.3 points. Duke: Jackson hit her season average of 13.3 points by the 3:54 mark of the second quarter when her pull-up jumper gave her 14. The junior guard was 8 of 11 from the floor, including 4 of 5 from 3-point range, and had 20 points by halftime. With the Blue Devils trailing by six midway through the second quarter, Jackson triggered a 15-0 run with 13 of the team’s points to help Duke take a lead they’d never relinquish. Duke will face the winner of No. 9 Oklahoma-DePaul on Wednesday in the championship game, while Kansas State will face the loser in the consolation game. Get poll alerts and updates on AP Top 25 basketball throughout the season. Sign up here. AP women’s college basketball: and
NEW YORK: Investors are expecting more gains for the US stock market in 2025 after two straight standout years, fueled by a solid economy supporting corporate profits, moderating interest rates and pro-growth policies from incoming president Donald Trump. The benchmark S&P 500 up over 23 percent year-to-date, even with a recent speed bump, and is on pace for its second straight year of gains exceeding 20 percent, lifted by megacap tech stocks and excitement over the business potential of artificial intelligence. Investors are more confident about the economy than this time a year ago, with consumers and businesses having absorbed higher interest rates and the Federal Reserve now lowering them - albeit by not as much as hoped. Corporate profits are also expected to be strong, with S&P 500 earnings projected to rise 14 percent in 2025, according to LSEG IBES. On the other side of the ledger, inflation remains stubborn, and Wall Street is wary of a rebound that could lead the Fed to change course on its easing cycle. Indeed, stocks pulled back sharply on Wednesday after the central bank projected fewer rate cuts next year as it braced for firmer inflation. Such prospects could become more likely if Trump implements tariffs on US imports that lead to higher consumer prices. Stock valuations, meanwhile, are around their steepest levels in more than three years, leaving greater potential for turbulence. “We’ve been on quite the tear coming off the lows back at the end of 2022. It’s been pretty eye-watering,” said Garrett Melson, portfolio strategist at Natixis Investment Managers. “Animal spirits... are certainly running pretty wild right now, but you might need to temper that a little bit as you start to move through the year,” said Melson, who thinks the stock market could still produce solid gains of around 10 percent in 2025 if not the returns of the prior two years. Wall Street firms are mostly projecting gains for the market next year, with S&P 500 year-end targets ranging from 6,000 to 7,000. The index was last hovering around 5,900. Optimistic investors can point to a bull market that is neither old nor over-extended, by historic measures. The current bull market for the S&P 500 that began in October 2022 is less than half as long as the average length of the 10 prior ones, according to Keith Lerner, co-chief investment officer at Truist Advisory Services. The S&P 500’s roughly 64 percent gain during this latest run trails the 108 percent median gain and 184 percent average rise of the prior bull markets, according to Lerner. “If you zoom out a little bit, yes, we have a lot of gains, but if you look at a typical bull market, it suggests that we still have further gains to go,” Lerner said. Other historic signs also bode well. The S&P 500 has gained an average of 12.3 percent following the eight instances of back-to-back 20 percent annual gains since 1950, according to Ryan Detrick, chief market strategist at Carson Group, compared to a 9.3 percent overall average increase over that time. The index increased six of the eight times. Bolstering the upbeat sentiment is the prevailing sense on Wall Street that the economy has weathered the rate hikes the Fed implemented starting in 2022 to quell inflation. A Natixis Investment Managers survey conducted in recent weeks found 73 percent of institutional investors said the US will avoid a recession in 2025. That’s a sharp turnaround from a year ago, when 62 percent projected such a downturn in the coming year. Citigroup’s economic surprise index, which measures how economic data performs versus expectations, has been solidly positive for the past two months, another rosy sign for investors. Adding to expectations of a solid economy, Trump is expected to pursue an agenda that includes tax cuts and deregulation that supports growth. “We’re leaving 2024 on pretty good footing, and we think there is some re-acceleration in 2025,” said Sameer Samana, senior global market strategist at Wells Fargo Investment Institute. “Markets tend to front-run the economy, so they will position for that economic re-acceleration sooner rather than later.” However, stocks are also leaving 2024 at elevated valuations: The S&P 500 is trading at nearly 22 times expected earnings over the next 12 months, according to LSEG. That is well above its long-term average of 15.8, and not far from the 22.6 level it reached earlier this month, its highest since early 2021. Investors maintain that valuations can stay high for long periods and do not necessarily indicate imminent declines. But future gains may rest more on earnings growth, while higher valuations could make stocks more easily rattled by any disappointments. Risks include policy uncertainty such as Trump’s expected push to raise tariffs on imports from China and other trading partners, which analysts estimate could hurt corporate profits. Higher tariffs could also increase inflation, which is another worry for investors. The pace of inflation has fallen dramatically since hitting 40-year highs in 2022, but remains above the Fed’s 2 percent target. The latest reading of the consumer price index found a 2.7 percent annual inflation rate. “How low we can get rates is really going to be dependent on how low we can get inflation,” said Michael Reynolds, vice president of investment strategy at Glenmede. “If we see inflation settling out to the 3-ish percent range, we think the Fed’s not going to be as aggressive next year.” Glenmede is recommending investors take a neutral posture on overall portfolio risk, including for equities. “Investors should be what I would call cautiously optimistic,” Reynolds said. “We ... have an economy that’s showing signs of late-stage expansion alongside valuations that are pretty rich.” — Reuters
Billionaire investor Stanley Druckenmiller recently made a big investment in Broadcom (AVGO), increasing his stake to 239,980 shares . That stake, up 35% since September, is worth almost $56 million, one of the largest holdings in Druckenmiller’s private family fund. Now, Broadcom shares have surged almost 70% recently, so the stock is probably due for a breather. But Druckenmiller’s big bet suggests there’s plenty of upside potential left. I anticipate some consolidation into January and perhaps even February before the stock makes its next big move. That’s why the trade to make on Broadcom in the near term is a short iron condor. Let me show you. The short iron condor is a combination of a short call spread and a short put spread – a trade that sells premium on both the lower and upper bounds of price under the notion that prices remain rangebound in consolidation of strength. As a result, the position loses the value we collected for selling it, and we are able to buy it back at a cheaper cost and keep the difference as profit for holding the spreads over time. Here’s the trade structure I’m looking at: Sell to open 1 AVGO 17 Jan 260 calls Buy to open 1 AVGO 17 Jan 265 calls Buy to open 1 AVGO 17 Jan 220 puts Sell to open 1 AVGO 17 Jan 215 puts At this writing, the credit collected is $1.67 and this number represents the total profit for the position. The reason I chose these particular strikes is because of AVGO’s support and resistance levels. The relative resistance zone currently sits right around $250, and from here we should consolidate (move around within a range of price for a period of time). The relative support is near $225. This strategy provides four outcomes to exit the trade: Buy back the iron condor as its value erodes – I like to collect 50% of the original collected premium, in general. Buy back the iron condor within ten days of expiration, especially if there is no movement in price. If the prices spike above the short strikes (in this case, above $260 or below $220) for more than 3 days: exit the position, irrespective of where profits sit. Buy back the iron condor if it moves above your threshold for loss – usually between 35%-50% above the collected premium. Last Chance to Save 65% – End-of-Year Sale Ends December 31! Transform your trading strategy with Benzinga Edge. Get exclusive stock picks, daily trade setups, and real-time alerts. Last chance to save 65%— don't miss the End-of-Year Sale before it's gone! Image via Flickr © 2024 Benzinga.com. Benzinga does not provide investment advice. All rights reserved.Join our daily and weekly newsletters for the latest updates and exclusive content on industry-leading AI coverage. Learn More Every week — sometimes every day—a new state-of-the-art AI model is born to the world. As we move into 2025, the pace at which new models are being released is dizzying, if not exhausting. The curve of the rollercoaster is continuing to grow exponentially, and fatigue and wonder have become constant companions. Each release highlights why this particular model is better than all others, with endless collections of benchmarks and bar charts filling our feeds as we scramble to keep up. Eighteen months ago, the vast majority of developers and businesses were using a single AI model . Today, the opposite is true. It is rare to find a business of significant scale that is confining itself to the capabilities of a single model. Companies are wary of vendor lock-in, particularly for a technology which has quickly become a core part of both long-term corporate strategy and short-term bottom-line revenue. It is increasingly risky for teams to put all their bets on a single large language model (LLM). But despite this fragmentation, many model providers still champion the view that AI will be a winner-takes-all market. They claim that the expertise and compute required to train best-in-class models is scarce, defensible and self-reinforcing. From their perspective, the hype bubble for building AI models will eventually collapse, leaving behind a single, giant artificial general intelligence (AGI) model that will be used for anything and everything. To exclusively own such a model would mean to be the most powerful company in the world. The size of this prize has kicked off an arms race for more and more GPUs, with a new zero added to the number of training parameters every few months. We believe this view is mistaken. There will be no single model that will rule the universe, neither next year nor next decade. Instead, the future of AI will be multi-model. Language models are fuzzy commodities The Oxford Dictionary of Economics defines a commodity as a “standardized good which is bought and sold at scale and whose units are interchangeable.” Language models are commodities in two important senses: But while language models are commoditizing, they are doing so unevenly. There is a large core of capabilities for which any model, from GPT-4 all the way down to Mistral Small, is perfectly suited to handle. At the same time, as we move towards the margins and edge cases, we see greater and greater differentiation, with some model providers explicitly specializing in code generation, reasoning, retrieval-augmented generation (RAG) or math. This leads to endless handwringing, reddit-searching, evaluation and fine-tuning to find the right model for each job. And so while language models are commodities, they are more accurately described as fuzzy commodities . For many use cases, AI models will be nearly interchangeable, with metrics like price and latency determining which model to use. But at the edge of capabilities, the opposite will happen: Models will continue to specialize, becoming more and more differentiated. As an example, Deepseek-V2.5 is stronger than GPT-4o on coding in C#, despite being a fraction of the size and 50 times cheaper. Both of these dynamics — commoditization and specialization — uproot the thesis that a single model will be best-suited to handle every possible use case. Rather, they point towards a progressively fragmented landscape for AI. Multi-modal orchestration and routing There is an apt analogy for the market dynamics of language models: The human brain. The structure of our brains has remained unchanged for 100,000 years, and brains are far more similar than they are dissimilar. For the vast majority of our time on Earth, most people learned the same things and had similar capabilities. But then something changed. We developed the ability to communicate in language — first in speech, then in writing. Communication protocols facilitate networks, and as humans began to network with each other, we also began to specialize to greater and greater degrees. We became freed from the burden of needing to be generalists across all domains, to be self-sufficient islands. Paradoxically, the collective riches of specialization have also meant that the average human today is a far stronger generalist than any of our ancestors. On a sufficiently wide enough input space, the universe always tends towards specialization. This is true all the way from molecular chemistry, to biology, to human society. Given sufficient variety, distributed systems will always be more computationally efficient than monoliths. We believe the same will be true of AI. The more we can leverage the strengths of multiple models instead of relying on just one, the more those models can specialize, expanding the frontier for capabilities. An increasingly important pattern for leveraging the strengths of diverse models is routing — dynamically sending queries to the best-suited model, while also leveraging cheaper, faster models when doing so doesn’t degrade quality. Routing allows us to take advantage of all the benefits of specialization — higher accuracy with lower costs and latency — without giving up any of the robustness of generalization. A simple demonstration of the power of routing can be seen in the fact that most of the world’s top models are themselves routers: They are built using Mixture of Expert architectures that route each next-token generation to a few dozen expert sub-models. If it’s true that LLMs are exponentially proliferating fuzzy commodities, then routing must become an essential part of every AI stack. There is a view that LLMs will plateau as they reach human intelligence — that as we fully saturate capabilities, we will coalesce around a single general model in the same way that we have coalesced around AWS, or the iPhone. Neither of those platforms (or their competitors) have 10X’d their capabilities in the past couple years — so we might as well get comfortable in their ecosystems. We believe, however, that AI will not stop at human-level intelligence; it will carry on far past any limits we might even imagine. As it does so, it will become increasingly fragmented and specialized, just as any other natural system would. We cannot overstate how much AI model fragmentation is a very good thing. Fragmented markets are efficient markets: They give power to buyers, maximize innovation and minimize costs. And to the extent that we can leverage networks of smaller, more specialized models rather than send everything through the internals of a single giant model, we move towards a much safer, more interpretable and more steerable future for AI. The greatest inventions have no owners. Ben Franklin’s heirs do not own electricity. Turing’s estate does not own all computers. AI is undoubtedly one of humanity’s greatest inventions; we believe its future will be — and should be — multi-model. Zack Kass is the former head of go-to-market at OpenAI . Tomás Hernando Kofman is the co-Founder and CEO of Not Diamond . DataDecisionMakers Welcome to the VentureBeat community! DataDecisionMakers is where experts, including the technical people doing data work, can share data-related insights and innovation. If you want to read about cutting-edge ideas and up-to-date information, best practices, and the future of data and data tech, join us at DataDecisionMakers. 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