Schlagwort-Archive: OpenAI

OpenAI Goes From Stock Market Savior to Burden as AI Risks Mount

 

Takeaways by Bloomberg AI

  • Wall Street’s sentiment toward companies associated with artificial intelligence is shifting, with OpenAI down and Alphabet Inc. up.
  • The maker of ChatGPT is facing questions about its lack of profitability and the need to grow rapidly to pay for its massive spending commitments.
  • Alphabet’s perceived strength goes beyond its Gemini AI model, with the company having a ton of cash at its disposal and a host of adjacent businesses, making it a deep-pocketed competitor in the AI trade.
 

Wall Street’s sentiment toward companies associated with artificial intelligence is shifting, and it’s all about two companies: OpenAI is down, and Alphabet Inc. is up.

The maker of ChatGPT is no longer seen as being on the cutting edge of AI technology and is facing questions about its lack of profitability and the need to grow rapidly to pay for its massive spending commitments. Meanwhile, Google’s parent is emerging

as a deep-pocketed competitor with tentacles in every part of the AI trade.

“OpenAI was the golden child earlier this year, and Alphabet was looked at in a very different light,” said Brett Ewing, chief market strategist at First Franklin Financial Services. “Now sentiment is much more tempered toward OpenAI.”

 

As a result, the shares of companies in OpenAI’s orbit — principally Oracle Corp., CoreWeave Inc., and Advanced Micro Devices Inc., but also Microsoft Corp., Nvidia Corp. and SoftBank, which has an 11% stake in the company — are coming under heavy selling pressure. Meanwhile, Alphabet’s momentum is boosting not only its stock price, but also those it’s associated with like Broadcom Inc., Lumentum Holdings Inc., Celestica Inc., and TTM Technologies Inc.

Read More: Alphabet’s AI Strength Fuels Biggest Quarterly Jump Since 2005

The shift has been dramatic in magnitude and speed. Just a few weeks ago, OpenAI was sparking huge rallies in any company related to it. Now, those connections look more like an anchor. It’s a change that carries wide-ranging implications, given how central the closely held company has been to the AI mania that has driven the stock market’s three-year rally.

“A light has been shined on the complexity of the financing, the circular deals, the debt issues,” Ewing said. “I’m sure this exists around the Alphabet ecosystem to a certain degree, but it was exposed as pretty extreme for OpenAI’s deals, and appreciating that was a game-changer for sentiment.”

A basket of companies connected to OpenAI has gained 74% in 2025, which is impressive but far shy of the 146% jump by Alphabet-exposed stocks. The technology-heavy Nasdaq 100 Index is up 22%.

OpenAI vs Alphabet

Stocks in orbits of OpenAI and Alphabet have diverged

Source: Bloomberg, Morgan Stanley

Data is normalized with percentage appreciation as of January 2, 2025.

The skepticism surrounding OpenAI can be dated to August, when it unveiled GPT-5 to mixed reactions. It ramped up last month when Alphabet released the latest version of its Gemini AI model and got rave reviews. As a result, OpenAI Chief Executive Officer Sam Altman declared a “code red” effort to improve the quality of ChatGPT, delaying other projects until it gets its signature product in line.

‘All the Pieces’

Alphabet’s perceived strength goes beyond Gemini. The company has the third highest market capitalization in the S&P 500 and a ton of cash at its disposal. It also has a host of adjacent businesses, like Google Cloud and a semiconductor manufacturing operation that’s gaining traction. And that’s before you consider the company’s AI data, talent and distribution, or its successful subsidiaries like YouTube and Waymo.

 

“There’s a growing sense that Alphabet has all the pieces to emerge as the dominant AI model builder,” said Brian Colello, technology equity senior strategist at Morningstar. “Just a couple months ago, investors would’ve given that title to OpenAI. Now there’s more uncertainty, more competition, more risk that OpenAI isn’t the slam-dunk winner.”

Read More: Alphabet’s AI Chips Are a Potential $900 Billion ‘Secret Sauce’

Representatives for OpenAI and Alphabet didn’t respond to requests for comment.

The difference between being first or second place goes beyond bragging rights, it also has significant financial ramifications for the companies and their partners. For example, if users gravitating to Gemini slows ChatGPT’s growth, it will be harder for OpenAI to pay for cloud-computing capacity from Oracle or chips from AMD.

By contrast, Alphabet’s partners in building out its AI effort are thriving. Shares of Lumentum, which makes optical components for Alphabet’s data centers, have more than tripled this year, putting them among the 30 best performers in the Russell 3000 Index. Celestica provides the hardware for Alphabet’s AI buildout, and its stock is up 252% in 2025. Meanwhile Broadcom — which is building the tensor processing unit, or TPU, chips Alphabet uses — has seen its stock price leap 68% since the end of last year.

OpenAI has announced a number of ambitious deals in recent months. The flurry of activity “rightfully brought scrutiny and concern over whether OpenAI can fund all this, whether it is biting off more than it can chew,” Colello said. “The timing of its revenue growth is uncertain, and every improvement a competitor makes adds to the risk that it can’t reach its aspirations.”

In fairness, investors greeted many of these deals with excitement, because they appeared to mint the next generation of AI winners. But with the shift in sentiment, they’re suddenly taking a wait-and-see attitude.

“When people thought it could generate revenue and become profitable, those big deal numbers seemed possible,” said Brian Kersmanc, portfolio manager at GQG Partners, which has about $160 billion in assets. “Now we’re at a point where people have stopped believing and started questioning.”

Kersmanc sees the AI euphoria as the “dot-com era on steroids,” and said his firm has gone from being heavily overweight tech to highly skeptical.

Self-Inflicted Wounds

“We’re trying to avoid areas of over-hype and a lot of those were fueled by OpenAI,” he said. “Since a lot of places have been touched by this, it will be a painful unwind. It isn’t just a few tech names that need to come down, though they’re a huge part of the index. All these bets have parallel trades, like utilities, with high correlations. That’s the fear we have, not just that OpenAI spun up this narrative, but that so many things were lifted on the hype.”

OpenAI’s public-relations flaps haven’t helped. The startup’s Chief Financial Officer Sarah Friar recently suggested the US government “backstop the guarantee that allows the financing to happen,” which raised some eyebrows. But she and Altman later clarified that the company hasn’t requested such guarantees.

Then there was Altman’s appearance on the “Bg2 Pod,” where he was asked how the company can make spending commitments that far exceed its revenue. “If you want to sell your shares, I’ll find you a buyer — I just, enough,” was the CEO’s response.

Altman’s dismissal was problematic because the gap between OpenAI’s revenue and its spending plans between now and 2033 is about $207 billion, according to HSBC estimates.

“Closing the gap would need one or a combination of factors, including higher revenue than in our central case forecasts, better cost management, incremental capital injections, or debt issuance,” analyst Nicolas Cote-Colisson wrote in a research note on Nov. 24. Considering that OpenAI is expected to generate revenue of more than $12 billion in 2025, its compute cost “compounds investor nervousness about associated returns,” not only for the company itself, but also “for the interlaced AI chain,” he wrote.

To be sure, companies like Oracle and AMD aren’t solely reliant on OpenAI. They operate in areas that continue to see a lot of demand, and their products could find customers even without OpenAI. Furthermore, the weakness in the stocks could represent a buying opportunity, as companies tied to ChatGPT and the chips that power it are trading at a discount to those exposed to Gemini and its chips for the first time since 2016, according to a recent Wells Fargo analysis.

“I see a lot of untapped demand and penetration across industries, and that will ultimately underpin growth,” said Kieran Osborne, chief investment officer at Mission Wealth, which has about $13 billion in assets under management. “Monetization is the end goal for these companies, and so long as they work toward that, that will underpin the investment case.”

Source: https://www.bloomberg.com/news/articles/2025-12-07/openai-goes-from-stock-market-savior-to-anchor-as-ai-risks-mount

OpenAI’s Atlas Browser Takes Direct Aim at Google Chrome

The new ChatGPT-powered web browser is OpenAI’s boldest play yet to reinvent how people use the web.

Illustration of several web browsers with the OpenAI logo and search bars

Illustration: WIRED Staff

OpenAI announced on Tuesday it’s rolling out a new internet browser called Atlas that integrates directly with ChatGPT. Atlas includes features like a sidebar window people can use to ask ChatGPT questions about the web pages they visit. There’s also an AI agent that can click around and complete tasks on a user’s behalf.

“We think that AI represents a rare, once-a-decade opportunity to rethink what a browser can be about,” OpenAI CEO Sam Altman said during a livestream announcing Atlas. “Tabs were great, but we haven’t seen a lot of browser innovation since then.”

Atlas debuts as Silicon Valley races to use generative AI to reshape how people experience the internet. Google has also announced a plethora of AI features for its popular Chrome browser, including a “sparkle” button that launches its Gemini chatbot. Chrome remains the most used browser worldwide.

OpenAI says the Atlas browser will be available starting today for ChatGPT users globally on macOS. Windows and mobile options are currently in the works. Atlas is free to use, though its agent features are reserved for subscribers to OpenAI’s ChatGPT Plus or ChatGPT Pro plans.

OpenAI highlighted how Atlas can help users research vacations and other activities.

“We’ve made some major upgrades to search on ChatGPT when accessed via Atlas,” Ryan O’Rouke, OpenAI’s lead designer for the browser, said during the livestream. If a user asks for movie reviews in the Atlas search bar, a chatbot-style answer will pop up first, rather than the more traditional collection of blue links users might expect when searching the web via Google.

Now, in addition to that result, users can switch to other tabs to see a collection of website links, images, videos, or news related to their queries. It’s a bit of an inversion of the Google Chrome experience. Rather than the search result being a collection of links with AI features added on top of that, the AI chatbot is central in Atlas, with the list of website links or image results as secondary.

Another feature OpenAI highlighted in the livestream is Atlas’ ability to collect “browser memories.” The capability is optional, and is an iteration of ChatGPT’s existing memory tool that stores details about users based on their past interactions with the chatbot. The browser can recall what you searched for in the past and use that data when suggesting topics of interest and actions to take, like automating an online routine it detects or returning back to a website you previously visited that could be helpful for a current project.

In Atlas users can highlight whatever they are writing and request assistance from ChatGPT.
Atlas has an optional memory feature that can recall what users searched for in the past.

Tech giants and smaller startups have been experimenting with baking AI into web browsers for the past several years. Microsoft was one of the first movers when it threw its AI tool, called Bing at the time, into its Edge browser as a sidebar. Since then, browser-focused companies like Opera and Brave have also continued to tinker with different AI integrations. Another notable entry in the AI browser wars is Perplexity’s Comet, which launched this year and is also free to use.

Source: https://www.wired.com/story/openai-atlas-browser-chrome-agents-web-browsing/

Forget SEO. Welcome to the World of Generative Engine Optimization

This holiday season, rather than searching on Google, more Americans will likely be turning to large language models to find gifts, deals, and sales. Retailers could see up to a 520 percent increase in traffic from chatbots and AI search engines this year compared to 2024, according to a recent shopping report from Adobe. OpenAI is already moving to capitalize on the trend: Last week, the ChatGPT maker announced a major partnership with Walmart that will allow users to buy goods directly within the chat window.

As people start relying on chatbots to discover new products, retailers are having to rethink their approach to online marketing. For decades, companies tried to game Google’s search results by using strategies known collectively as search engine optimization, or SEO. Now, in order to get noticed by AI bots, more brands are turning to “generative engine optimization,” or GEO. The cottage industry is expected to be worth nearly $850 million this year, according to one market research estimate.

GEO, in many ways, is less a new invention than the next phase of SEO. Many GEO consultants, in fact, came from the world of SEO. At least some of their old strategies likely still apply since the core goal remains the same: anticipate the questions people will ask and make sure your content appears in the answers. But there’s also growing evidence that chatbots are surfacing different kinds of information than search engines.

Imri Marcus, chief executive of the GEO firm Brandlight, estimates that there used to be about a 70 percent overlap between the top Google links and the sources cited by AI tools. Now, he says, that correlation has fallen below 20 percent.

Search engines often favor wordiness—think of the long blog posts that appear above recipes on cooking websites. But Marcus says that chatbots tend to favor information presented in simple, structured formats, like bulleted lists and FAQ pages. “An FAQ can answer a hundred different questions instead of one article that just says how great your entire brand is,” he says. “You essentially give a hundred different options for the AI engines to choose.”

The things people ask chatbots are often highly specific, so it’s helpful for companies to publish extremely granular information. “No one goes to ChatGPT and asks, ‘Is General Motors a good company?’” says Marcus. Instead, they ask if the Chevy Silverado or the Chevy Blazer has a longer driving range. “Writing more specific content actually will drive much better results because the questions are way more specific.”

These insights are helping to refine the marketing strategies of Brandlight’s clients, which include LG, Estée Lauder, and Aetna. “Models consume things differently,” says Brian Franz, chief technology, data and analytics officer at Estée Lauder Companies. “We want to make sure the product information, the authoritative sources that we use, are all the things that are feeding the model.” Asked whether he would ever consider partnering with OpenAI to let people shop Estée Lauder products within the chat window, Franz doesn’t hesitate. “Absolutely,” he says.

At least for the time being, brands are mostly worried about consumer awareness, rather than directly converting chatbot mentions into sales. It’s about making sure when people ask ChatGPT „What should I put on my skin after a sunburn?“ their product pops up, even if it’s unlikely anyone will immediately click and buy it. “Right now, in this really early learning stage where it feels like it’s almost going to explode, I don’t think we want to look at the ROI of a particular piece of content we created,” Franz says.

To create all of this new AI-optimized content, companies are, of course, turning to AI itself. “At the beginning, people speculated that AI engines will not be training on AI content,” Marcus says. “That’s not really the case.”

Source: https://www.wired.com/story/goodbye-seo-hello-geo-brandlight-openai/

OpenAI rolls out ‘instant’ purchases directly from ChatGPT, in a radical shift to e-commerce and a direct challenge to Google

https://fortune.com/2025/09/29/openai-rolls-out-purchases-direct-from-chatgpt-in-a-radical-shift-to-e-commerce-and-direct-challenge-to-google/

OpenAI said it will allow users in the U.S. to make purchases directly through ChatGPT using a new Instant Checkout feature powered by a payment protocol for AI co-developed with Stripe.

The new chatbot shopping feature is a big step toward helping OpenAI monetize its 700 million weekly users, many of whom currently pay nothing to interact with ChatGPT, as well as a move that could eventually steal significant market share from traditional Google search advertising.

The rollout of chatbot shopping features—including the possibility of AI agents that will shop on behalf of users—could also upend e-commerce, radically transforming the way businesses design their websites and try to market to consumers.

OpenAI said it was rolling out its Instant Checkout feature with Etsy sellers today, but would begin adding over a million Shopify merchants, including brands such as Glossier, Skims, Spanx, and Vuori “soon.”

The company also said it was open-sourcing the Agentic Commerce Protocol, a payment standard developed in partnership with payments processor Stripe that powers the Instant Checkout feature, so that any retailer or business could decide to build a shopping integration with ChatGPT. (Stripe’s and OpenAI’s commerce protocol, in turn, supports the open-source Model Context Protocol, or MCP, that was originally developed by AI company Anthropic last year. MCP is designed to allow AI models to directly hook into the backend systems of businesses and retailers. The new Agentic Commerce Protocol also supports more conventional API calls too.)

OpenAI will take what it described as small fee from the merchant on each purchase, helping to bolster the company’s revenue at a time when it is burning through many billions of dollars each year to train and support the running of its AI models.

 

How it works

OpenAI had previously launched a shopping feature in ChatGPT that helped users find products that were best suited to them, but the suggested results then linked out to merchants’ websites, where a user had to complete the purchase—analogous to the way a Google search works.

When a ChatGPT user asks a shopping-related question—such as “the best hiking boots for me that cost under $150” or “possible birthday gifts for my 10-year old nephew”—the chatbot will still respond with product suggestions. Under the new system, if a user likes one of the suggestions and Instant Checkout is enabled, they will be able to click a “Buy” button in the chatbot response and confirm their order, shipping, and payment details without ever leaving the chat.

OpenAI said its “product results are organic and unsponsored, ranked purely on relevance to the user.” The company also emphasized that the results are not affected by the fee the merchant pays it to support Instant Checkout.

Then, to determine which merchants that carry that particular product should be surfaced for the user, “ChatGPT considers factors like availability, price, quality, whether a merchant is the primary seller, and whether Instant Checkout is enabled,” when displaying results, the company said.

OpenAI said that ChatGPT subscribers, who pay a monthly fee for premium features, would be able to use the same credit or debit card to which they charge their subscription or store alternate payment methods to use.

OpenAI’s decision to launch the shopping feature using Stripe’s Agentic Commerce Protocol will be a big boost for that payment standard, which can be used across different AI platforms and also works with different payment processors—although it is easier to integrate for existing Stripe customers. The protocol works by creating an encrypted token for payment details and other sensitive data.

Currently, OpenAI says that the user remains in control, having to explicitly agree to each step of the purchasing process before any action is taken. But it is easy to imagine that in the future, users may be able to authorize ChatGPT or other AI models to act more “agentically” and actually make purchases for the user based on a prompt, without having to check back in with a user.

The fact that users never have to leave the chat interface to make the purchase may pose a challenge to Alphabet’s Google, which makes most of its money by referring users to companies’ websites. Although Google may be able to roll out similar shopping features within its Gemini chatbot or “AI Mode” in Google Search, it’s unclear whether what it could charge for transactions completed in these AI-native ways would compensate for any loss in referral revenue and what the opportunities would be for the display of other advertising around chatbot queries.

Sam Altman – The Intelligence Age

Source: https://ia.samaltman.com/

In the next couple of decades, we will be able to do things that would have seemed like magic to our grandparents.

This phenomenon is not new, but it will be newly accelerated. People have become dramatically more capable over time; we can already accomplish things now that our predecessors would have believed to be impossible.

We are more capable not because of genetic change, but because we benefit from the infrastructure of society being way smarter and more capable than any one of us; in an important sense, society itself is a form of advanced intelligence. Our grandparents – and the generations that came before them – built and achieved great things. They contributed to the scaffolding of human progress that we all benefit from. AI will give people tools to solve hard problems and help us add new struts to that scaffolding that we couldn’t have figured out on our own. The story of progress will continue, and our children will be able to do things we can’t.

It won’t happen all at once, but we’ll soon be able to work with AI that helps us accomplish much more than we ever could without AI; eventually we can each have a personal AI team, full of virtual experts in different areas, working together to create almost anything we can imagine. Our children will have virtual tutors who can provide personalized instruction in any subject, in any language, and at whatever pace they need. We can imagine similar ideas for better healthcare, the ability to create any kind of software someone can imagine, and much more.

With these new abilities, we can have shared prosperity to a degree that seems unimaginable today; in the future, everyone’s lives can be better than anyone’s life is now. Prosperity alone doesn’t necessarily make people happy – there are plenty of miserable rich people – but it would meaningfully improve the lives of people around the world.

Here is one narrow way to look at human history: after thousands of years of compounding scientific discovery and technological progress, we have figured out how to melt sand, add some impurities, arrange it with astonishing precision at extraordinarily tiny scale into computer chips, run energy through it, and end up with systems capable of creating increasingly capable artificial intelligence.

This may turn out to be the most consequential fact about all of history so far. It is possible that we will have superintelligence in a few thousand days (!); it may take longer, but I’m confident we’ll get there.

How did we get to the doorstep of the next leap in prosperity?

In three words: deep learning worked.

In 15 words: deep learning worked, got predictably better with scale, and we dedicated increasing resources to it.

That’s really it; humanity discovered an algorithm that could really, truly learn any distribution of data (or really, the underlying “rules” that produce any distribution of data). To a shocking degree of precision, the more compute and data available, the better it gets at helping people solve hard problems. I find that no matter how much time I spend thinking about this, I can never really internalize how consequential it is.

There are a lot of details we still have to figure out, but it’s a mistake to get distracted by any particular challenge. Deep learning works, and we will solve the remaining problems. We can say a lot of things about what may happen next, but the main one is that AI is going to get better with scale, and that will lead to meaningful improvements to the lives of people around the world.

AI models will soon serve as autonomous personal assistants who carry out specific tasks on our behalf like coordinating medical care on your behalf. At some point further down the road, AI systems are going to get so good that they help us make better next-generation systems and make scientific progress across the board.

Technology brought us from the Stone Age to the Agricultural Age and then to the Industrial Age. From here, the path to the Intelligence Age is paved with compute, energy, and human will.

If we want to put AI into the hands of as many people as possible, we need to drive down the cost of compute and make it abundant (which requires lots of energy and chips). If we don’t build enough infrastructure, AI will be a very limited resource that wars get fought over and that becomes mostly a tool for rich people.

We need to act wisely but with conviction. The dawn of the Intelligence Age is a momentous development with very complex and extremely high-stakes challenges. It will not be an entirely positive story, but the upside is so tremendous that we owe it to ourselves, and the future, to figure out how to navigate the risks in front of us.

I believe the future is going to be so bright that no one can do it justice by trying to write about it now; a defining characteristic of the Intelligence Age will be massive prosperity.

Although it will happen incrementally, astounding triumphs – fixing the climate, establishing a space colony, and the discovery of all of physics – will eventually become commonplace. With nearly-limitless intelligence and abundant energy – the ability to generate great ideas, and the ability to make them happen – we can do quite a lot.

As we have seen with other technologies, there will also be downsides, and we need to start working now to maximize AI’s benefits while minimizing its harms. As one example, we expect that this technology can cause a significant change in labor markets (good and bad) in the coming years, but most jobs will change more slowly than most people think, and I have no fear that we’ll run out of things to do (even if they don’t look like “real jobs” to us today). People have an innate desire to create and to be useful to each other, and AI will allow us to amplify our own abilities like never before. As a society, we will be back in an expanding world, and we can again focus on playing positive-sum games.

Many of the jobs we do today would have looked like trifling wastes of time to people a few hundred years ago, but nobody is looking back at the past, wishing they were a lamplighter. If a lamplighter could see the world today, he would think the prosperity all around him was unimaginable. And if we could fast-forward a hundred years from today, the prosperity all around us would feel just as unimaginable.

OpenAI Announces a New AI Model, Code-Named Strawberry Step – ChatGPT o1

The ChatGPT maker reveals details of what’s officially known as OpenAI o1, which shows that AI needs more

OpenAI made the last big breakthrough in artificial intelligence by increasing the size of its models to dizzying proportions, when it introduced GPT-4 last year. The company today announced a new advance that signals a shift in approach—a model that can “reason” logically through many difficult problems and is significantly smarter than existing AI without a major scale-up.

The new model, dubbed OpenAI o1, can solve problems that stump existing AI models, including OpenAI’s most powerful existing model, GPT-4o. Rather than summon up an answer in one step, as a large language model normally does, it reasons through the problem, effectively thinking out loud as a person might, before arriving at the right result.

“This is what we consider the new paradigm in these models,” Mira Murati, OpenAI’s chief technology officer, tells WIRED. “It is much better at tackling very complex reasoning tasks.”

The new model was code-named Strawberry within OpenAI, and it is not a successor to GPT-4o but rather a complement to it, the company says.

Murati says that OpenAI is currently building its next master model, GPT-5, which will be considerably larger than its predecessor. But while the company still believes that scale will help wring new abilities out of AI, GPT-5 is likely to also include the reasoning technology introduced today. “There are two paradigms,” Murati says. “The scaling paradigm and this new paradigm. We expect that we will bring them together.”

LLMs typically conjure their answers from huge neural networks fed vast quantities of training data. They can exhibit remarkable linguistic and logical abilities, but traditionally struggle with surprisingly simple problems such as rudimentary math questions that involve reasoning.

Murati says OpenAI o1 uses reinforcement learning, which involves giving a model positive feedback when it gets answers right and negative feedback when it does not, in order to improve its reasoning process. “The model sharpens its thinking and fine tunes the strategies that it uses to get to the answer,” she says. Reinforcement learning has enabled computers to play games with superhuman skill and do useful tasks like designing computer chips. The technique is also a key ingredient for turning an LLM into a useful and well-behaved chatbot.

Mark Chen, vice president of research at OpenAI, demonstrated the new model to WIRED, using it to solve several problems that its prior model, GPT-4o, cannot. These included an advanced chemistry question and the following mind-bending mathematical puzzle: “A princess is as old as the prince will be when the princess is twice as old as the prince was when the princess’s age was half the sum of their present age. What is the age of the prince and princess?” (The correct answer is that the prince is 30, and the princess is 40).

“The [new] model is learning to think for itself, rather than kind of trying to imitate the way humans would think,” as a conventional LLM does, Chen says.

OpenAI says its new model performs markedly better on a number of problem sets, including ones focused on coding, math, physics, biology, and chemistry. On the American Invitational Mathematics Examination (AIME), a test for math students, GPT-4o solved on average 12 percent of the problems while o1 got 83 percent right, according to the company.

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The new model is slower than GPT-4o, and OpenAI says it does not always perform better—in part because, unlike GPT-4o, it cannot search the web and it is not multimodal, meaning it cannot parse images or audio.

Improving the reasoning capabilities of LLMs has been a hot topic in research circles for some time. Indeed, rivals are pursuing similar research lines. In July, Google announced AlphaProof, a project that combines language models with reinforcement learning for solving difficult math problems.

AlphaProof was able to learn how to reason over math problems by looking at correct answers. A key challenge with broadening this kind of learning is that there are not correct answers for everything a model might encounter. Chen says OpenAI has succeeded in building a reasoning system that is much more general. “I do think we have made some breakthroughs there; I think it is part of our edge,” Chen says. “It’s actually fairly good at reasoning across all domains.”

Noah Goodman, a professor at Stanford who has published work on improving the reasoning abilities of LLMs, says the key to more generalized training may involve using a “carefully prompted language model and handcrafted data” for training. He adds that being able to consistently trade the speed of results for greater accuracy would be a “nice advance.”

Yoon Kim, an assistant professor at MIT, says how LLMs solve problems currently remains somewhat mysterious, and even if they perform step-by-step reasoning there may be key differences from human intelligence. This could be crucial as the technology becomes more widely used. “These are systems that would be potentially making decisions that affect many, many people,” he says. “The larger question is, do we need to be confident about how a computational model is arriving at the decisions?”

The technique introduced by OpenAI today also may help ensure that AI models behave well. Murati says the new model has shown itself to be better at avoiding producing unpleasant or potentially harmful output by reasoning about the outcome of its actions. “If you think about teaching children, they learn much better to align to certain norms, behaviors, and values once they can reason about why they’re doing a certain thing,” she says.

Oren Etzioni, a professor emeritus at the University of Washington and a prominent AI expert, says it’s “essential to enable LLMs to engage in multi-step problem solving, use tools, and solve complex problems.” He adds, “Pure scale up will not deliver this.” Etzioni says, however, that there are further challenges ahead. “Even if reasoning were solved, we would still have the challenge of hallucination and factuality.”

OpenAI’s Chen says that the new reasoning approach developed by the company shows that advancing AI need not cost ungodly amounts of compute power. “One of the exciting things about the paradigm is we believe that it’ll allow us to ship intelligence cheaper,” he says, “and I think that really is the core mission of our company.”

Source: https://www.wired.com/story/openai-o1-strawberry-problem-reasoning/

Real World Use Cases for Apples Vision Pro + Version 2 – with the new operating system ChatGPT „GPT-4o“

The integration of advanced AI like OpenAI’s GPT-4o into Apple’s Vision Pro + Version 2 can significantly enhance its vision understanding capabilities.
Here are ten possible use cases:

1. Augmented Reality (AR) Applications:
– Interactive AR Experiences: Enhance AR applications by providing real-time object recognition and interaction. For example, users can point the device at a historical landmark and receive detailed information and interactive visuals about it.
– AR Navigation: Offer real-time navigation assistance in complex environments like malls or airports, overlaying directions onto the user’s view.

2. Enhanced Photography and Videography:
– Intelligent Scene Recognition: Automatically adjust camera settings based on the scene being captured, such as landscapes, portraits, or low-light environments, ensuring optimal photo and video quality.
– Content Creation Assistance: Provide suggestions and enhancements for capturing creative content, such as framing tips, real-time filters, and effects.

3. Healthcare and Medical Diagnosis:
– Medical Imaging Analysis: Assist in analyzing medical images (e.g., X-rays, MRIs) to identify potential issues, providing preliminary diagnostic support to healthcare professionals.
– Remote Health Monitoring: Enable remote health monitoring by analyzing visual data from wearable devices to track health metrics and detect anomalies.

4. Retail and Shopping:
– Virtual Try-Ons: Allow users to virtually try on clothing, accessories, or cosmetics using the device’s camera, enhancing the online shopping experience.
– Product Recognition: Identify products in stores and provide information, reviews, and price comparisons, helping users make informed purchasing decisions.

5. Security and Surveillance:
– Facial Recognition: Enhance security systems with facial recognition capabilities for authorized access and threat detection.
– Anomaly Detection: Monitor and analyze security footage to detect unusual activities or potential security threats in real-time.

6. Education and Training:
– Interactive Learning: Use vision understanding to create interactive educational experiences, such as identifying objects or animals in educational content and providing detailed explanations.
– Skill Training: Offer real-time feedback and guidance for skills training, such as in sports or technical tasks, by analyzing movements and techniques.

7. Accessibility and Assistive Technology:
– Object Recognition for the Visually Impaired: Help visually impaired users navigate their surroundings by identifying objects and providing auditory descriptions.
– Sign Language Recognition: Recognize and translate sign language in real-time, facilitating communication for hearing-impaired individuals.

8. Home Automation and Smart Living:
– Smart Home Integration: Recognize household items and provide control over smart home devices. For instance, identifying a lamp and allowing users to turn it on or off via voice commands.
– Activity Monitoring: Monitor and analyze daily activities to provide insights and recommendations for improving household efficiency and safety.

9. Automotive and Driver Assistance:
– Driver Monitoring: Monitor driver attentiveness and detect signs of drowsiness or distraction, providing alerts to enhance safety.
– Object Detection: Enhance autonomous driving systems with better object detection and classification, improving vehicle navigation and safety.

10. Environmental Monitoring:
– Wildlife Tracking: Use vision understanding to monitor and track wildlife in natural habitats for research and conservation efforts.
– Pollution Detection: Identify and analyze environmental pollutants or changes in landscapes, aiding in environmental protection and management.

These use cases demonstrate the broad potential of integrating advanced vision understanding capabilities into Apple’s Vision Pro + Version 2, enhancing its functionality across various domains and providing significant value to users.

Why Apple uses ChatGPT 4o as its new operating system for Apples Vision Pro + Version 2

Apple’s Vision Pro + Version 2, utilizing OpenAI’s ChatGPT „GPT-4o“ as the operating system offers several compelling marketing benefits. Here are the key advantages to highlight:

1. Revolutionary User Interface:
– Conversational AI: GPT-4o’s advanced natural language processing capabilities allow for a conversational user interface, making interactions with Vision Pro + more intuitive and user-friendly.
– Personalized Interactions: The AI can provide highly personalized responses and suggestions based on user behavior and preferences, enhancing user satisfaction and engagement.

2. Unmatched Productivity:
– AI-Driven Multitasking: GPT-4o can manage and streamline multiple tasks simultaneously, significantly boosting productivity by handling scheduling, reminders, and real-time information retrieval seamlessly.
– Voice-Activated Efficiency: Hands-free operation through advanced voice commands allows users to multitask efficiently, whether they are working, driving, or engaged in other activities.

3. Advanced Accessibility:
– Inclusive Design: GPT-4o enhances accessibility with superior voice recognition, understanding diverse speech patterns, and offering multilingual support, making Vision Pro + more accessible to a broader audience.
– Adaptive Assistance: The AI can provide context-aware assistance to users with disabilities, further promoting inclusivity and ease of use.

4. Superior Integration and Ecosystem:
– Apple Ecosystem Synergy: GPT-4o integrates seamlessly with other Apple devices and services, offering a cohesive and interconnected user experience across the Apple ecosystem.
– Unified User Experience: Users can enjoy a consistent and unified experience across all their Apple devices, enhancing brand loyalty and overall user satisfaction.

5. Enhanced Security and Privacy:
– Secure Interactions: Emphasize GPT-4o’s robust security measures to ensure user data privacy and protection, leveraging OpenAI’s commitment to ethical AI practices.
– Trustworthy AI: Highlight OpenAI’s dedication to ethical AI usage, reinforcing user trust in the AI-driven functionalities of Vision Pro +.

6. Market Differentiation:
– Innovative Edge: Position Vision Pro + as a cutting-edge product that stands out in the market due to its integration with GPT-4o, setting it apart from competitors.
– Leadership in AI: Showcase Apple’s leadership in technology innovation by leveraging OpenAI’s state-of-the-art advancements in AI.

7. Future-Proofing:
– Continuous Innovation: Regular updates from OpenAI ensure that Vision Pro + remains at the forefront of AI technology, with continuous improvements and new features.
– Scalable Solutions: The AI platform’s scalability allows for future enhancements, ensuring the product remains relevant and competitive over time.

8. Customer Engagement:
– Proactive Support: GPT-4o can offer proactive customer support and real-time problem-solving, leading to higher customer satisfaction and loyalty.
– Engaging Experiences: The AI can create engaging and interactive experiences, making the device more enjoyable and useful for daily activities.

9. Enhanced Creativity:
Creative Assistance: GPT-4o can assist users with creative tasks such as content creation, brainstorming, and project management, providing valuable support for both personal and professional use.
– Innovative Features: Highlight the unique AI-driven features that empower users to explore new creative possibilities, enhancing the appeal of Vision Pro +.

10. Efficient Learning and Adaptation:
– User Learning: GPT-4o continuously learns from user interactions, becoming more efficient and effective over time, offering a progressively improving user experience.
– Adaptive Technology: The AI adapts to user needs and preferences, ensuring that the device remains relevant and useful in a variety of contexts.

By leveraging these benefits, Apple can market the Vision Pro + Version 2 as a pioneering product that offers unparalleled user experience, productivity, and innovation, driven by the advanced capabilities of OpenAI’s GPT-4o.

Elon Musk OpenAI is far more than saving the world

Elon Musk’s Billion-Dollar AI Plan Is About Far More Than Saving the World

Elon Musk.