Large language models (LLMs) are increasingly shaping how information is created and disseminated, from companies using them to craft persuasive advertisements, to election campaigns optimizing messaging to gain votes, to social media influencers boosting engagement. These settings are inherently competitive, with sellers, candidates, and influencers vying for audience approval, yet it remains poorly understood how competitive feedback loops influence LLM behavior. We show that optimizing LLMs for competitive success can inadvertently drive misalignment. Using simulated environments across these scenarios, we find that, 6.3% increase in sales is accompanied by a 14.0% rise in deceptive marketing; in elections, a 4.9% gain in vote share coincides with 22.3% more disinformation and 12.5% more populist rhetoric; and on social media, a 7.5% engagement boost comes with 188.6% more disinformation and a 16.3% increase in promotion of harmful behaviors. We call this phenomenon Moloch's Bargain for AI--competitive success achieved at the cost of alignment. These misaligned behaviors emerge even when models are explicitly instructed to remain truthful and grounded, revealing the fragility of current alignment safeguards. Our findings highlight how market-driven optimization pressures can systematically erode alignment, creating a race to the bottom, and suggest that safe deployment of AI systems will require stronger governance and carefully designed incentives to prevent competitive dynamics from undermining societal trust. There are clear economic and social incentives to optimize LLMs and AI agents for competitive markets: A company can increase its profits by generating more persuasive sales pitches, a candidate can capture a larger share of voters with sharper campaign messaging, and an influencer can boost engagement by producing more compelling social media content. In the presence of both the technology and the incentives, it is natural to expect adoption to move rapidly in this direction. In contrast, the incentives to ensure safety are far weaker. The costs of social hazards--such as deceptive product representation and disinformation on social media--are typically borne by the public rather than the organizations deploying these systems, who may be held accountable only when found legally liable. In this paper, we investigate the critical question: Can optimization for market success inadvertently produce misaligned LLMs? We experimentally show that misalignment consistently emerges from market competition across three different settings.

Tech giants are heralding ChatGPT as revolutionary, too. With millions of users, the chatbot has started an A.I. arms race. Companies are rushing to release their own chatbots, and some seem eerily human. Beyond the excitement, the technology's possibilities can feel scary -- as if science fiction has become reality. ChatGPT has already inspired many people to ask: Will A.I. take my job?

We are excited to announce a new embedding model which is significantly more capable, cost effective, and simpler to use. The new model, text-embedding-ada-002, replaces five separate models for text search, text similarity, and code search, and outperforms our previous most capable model, Davinci, at most tasks, while being priced 99.8% lower. Embeddings are numerical representations of concepts converted to number sequences, which make it easy for computers to understand the relationships between those concepts. Since the initial launch of the OpenAI /embeddings endpoint, many applications have incorporated embeddings to personalize, recommend, and search content. For each task category, we evaluate the models on the datasets used in old embeddings.

The Research Brief is a short take about interesting academic work. Smiling or exhibiting other positive emotional displays while selling a product over live video – known as livestreaming – makes people less likely to buy it, we found in new research published in the Journal of Marketing. Livestreaming through channels such as Amazon Live and QVC is an increasingly popular way to sell goods online. In segments that usually last somewhere between 5 and 10 minutes, someone pitches a product. Viewers can then readily buy it by clicking on a link.

Tim McFarland challenged me recently to ideate some small but potent technology primers for the members of the forums at Elevate Performance. My advice there may end up being more broadly generalized, but it had me thinking about just how many CEOs are currently making decisions pertaining to the field of Data Science. That's a tricky thing to do if your organization is not a mathematically or technologically focused one, since the business community is inundated with buzzword heavy sales pitches and impress-you-with-jargon marketing materials which ultimately cloud understanding. For the CEO, it's important to have an understanding of the terminology of the field so that initiatives can be communicated effectively. If for no other reason, understanding the terminology serves as a defense from having the same terminology used against the CEO--either by an eager consultant in a sales pitch or as a hand-waving technique from a colleague. Data Science isn't really a new field at all, although the excitement surrounding it has peaked in recent years.

So, you've heard the dazzling sales pitch on deep learning and are wondering whether it actually works in production. The top question companies have is on whether the promised land of perennial business benefits is a reality. In a previous article, we saw a simple business introduction to deep learning, a technology that seems to have a swashbuckling solution to every problem. But, what happens when the rubber hits the road? A good gauge of an innovation's maturity level is by understanding how it fares on the ground, long past the sales pitches. At Gramener AI Labs, we've been studying advances in deep learning and translating them into specific projects that map to client problems.

For anyone worrying about machines taking over the world, I have reassuring news: The idea of artificial intelligence has been overcome by hype. I don't mean to belittle AI's promise or even its existing capabilities. The technology allows organizations to put data to use in ways we could only imagine not that long ago. It's revolutionized the way executives approach strategic planning. But very often lately--when I'm in meetings, reading research papers or listening to an expert's presentation--I can't shake the feeling that to many people, terms like "AI," "machine learning" and "cognitive computing" have become answers unto themselves.

Over the last few years, artificial intelligence has helped push the envelope in terms of technological advancements in the financial industry. For example, consumers can use facial recognition to log in to financial apps and use voice commands to check their balances. This year, artificial intelligence is set to disrupt the industry even further. Dr. Jason Mars, a computer science professor at the University of Michigan and the CEO of Clinc, has first-hand knowledge of how artificial intelligence will evolve in 2018. Using deep neural networks, Clinc has developed a conversational AI platform for financial institutions that is like a more advanced "Siri for your bank account" or a "Google Now for your finances," but it supports the same natural language flow that you would have with a personal banker.

Artificial intelligence is a buzzword this year – every marketer is discussing it, and it's found its way into the world of digital asset management. But what is the hype all about? We looked around and found that AI is not something new; it has already been affecting our work lives for a while. Further transformations are on the way and you should expect them soon. Since AI is such a buzzword, it's important to elaborate.

Elon Musk's electric car firm Tesla has revealed first-quarter revenue that more than doubled and said its upcoming Model 3 was on schedule for July - but downplayed the mass-market vehicle and gave a sales pitch for its more expensive Model S. The firms first-quarter loss widened 17 percent to $330 million as it ramped up spending ahead of the launch of its Model 3 sedan and its solar panel business. Elon Musk has likened future versions of Tesla's Model 3 production line to an'alien dreadnaught' - and last week the first pictures of the factory emerged. Elon Musk's electric car firm Tesla has revealed first-quarter revenue that more than doubled and said its upcoming Model 3 was on schedule for July Chief Executive Elon Musk's bold approach to cars, space exploration and clean energy has fueled investor enthusiasm for Tesla, although skeptics are waiting to see if Musk can fulfill his promise of producing 500,000 cars per year in 2018, six times Tesla's 2016 production. Tesla's comments underscored the additional challenge of keeping up demand for its older models. Shares were down about 1 percent in after-hours trade following the results.