On Wednesday, OpenAI announced that ChatGPT users now have access to GPT-4.1, an AI language model previously available only through the company's API since its launch one month ago. The update brings what OpenAI describes as improved coding and web development capabilities to paid ChatGPT subscribers, with wider enterprise rollout planned in the coming weeks.

Adding GPT-4.1 and 4.1 mini to ChatGPT adds to an already complex model selection that includes GPT-4o, various specialized GPT-4o versions, o1-pro, o3-mini, and o3-mini-high models. There are technically nine AI models available for ChatGPT Pro subscribers. Wharton professor Ethan Mollick recently publicly lampooned the awkward situation on social media.

As of May 14, 2025, ChatGPT Pro users have access to eight main AI models, plus Deep Research. Credit: Benj Edwards

Deciding which AI model to use can be daunting for AI novices. Reddit users and OpenAI forum members alike commonly voice confusion about the available options. "I do not understand the reason behind having multiple models available for use," wrote one Reddit user in March. "Why would anyone use anything but the best one?" Another Redditor said they were "a bit lost" with the many ChatGPT models available after switching back from using Anthropic Claude.

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Imagine a world where AI-powered bots can buy or sell cryptocurrency, make investments, and execute software-defined contracts at the blink of an eye, depending on minute-to-minute currency prices, breaking news, or other market-moving events. Then imagine an adversary causing the bot to redirect payments to an account they control by doing nothing more than entering a few sentences into the bot’s prompt.

That’s the scenario depicted in recently released research that developed a working exploit against ElizaOS, a fledgling open source framework.

ElizaOS is a framework for creating agents that use large language models to perform various blockchain-based transactions on behalf of a user based on a set of predefined rules. It was introduced in October under the name Ai16z and was changed to its current name in January. The framework remains largely experimental, but champions of decentralized autonomous organizations (DAOs)—a model in which communities or companies are governed by decentralized computer programs running on blockchains—see it as a potential engine for jumpstarting the creation of agents that automatically navigate these so-called DAOs on behalf of end users.

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Using AI can be a double-edged sword, according to new research from Duke University. While generative AI tools may boost productivity for some, they might also secretly damage your professional reputation.

On Thursday, the Proceedings of the National Academy of Sciences (PNAS) published a study showing that employees who use AI tools like ChatGPT, Claude, and Gemini at work face negative judgments about their competence and motivation from colleagues and managers.

"Our findings reveal a dilemma for people considering adopting AI tools: Although AI can enhance productivity, its use carries social costs," write researchers Jessica A. Reif, Richard P. Larrick, and Jack B. Soll of Duke's Fuqua School of Business.

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On Monday, ChatGPT-maker OpenAI announced it will remain under the control of its founding nonprofit board, scrapping its controversial plan to split off its commercial operations as a for-profit company after mounting pressure from critics.

In an official OpenAI blog post announcing the latest restructuring decision, CEO Sam Altman wrote: "We made the decision for the nonprofit to stay in control after hearing from civic leaders and having discussions with the offices of the Attorneys General of California and Delaware."

The move represents a significant shift in OpenAI's proposed restructuring. While the most recent previous version of the company's plan (which we covered in December) would have established OpenAI as a Public Benefit Corporation with the nonprofit merely holding shares and having limited influence, the revised approach keeps the nonprofit firmly in control of operations.

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On Thursday, Anthropic announced significant upgrades to its AI assistant Claude, extending its research capabilities to run for up to 45 minutes before delivering comprehensive reports. The company also expanded its integration options, allowing Claude to connect with popular third-party services.

Much like Google's Deep Research (which debuted on December 11) and ChatGPT's deep research features (February 2), Anthropic announced its own "Research" feature on April 15. Each can autonomously browse the web and other online sources to compile research reports in document format, and open source clones of the technique have debuted as well.

Now, Anthropic is taking its Research feature a step further. The upgraded mode enables Claude to conduct "deeper" investigations across "hundreds of internal and external sources," Anthropic says. When users toggle the Research button, Claude breaks down complex requests into smaller components, examines each one, and compiles a report with citations linking to original sources.

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A new study analyzing the Danish labor market in 2023 and 2024 suggests that generative AI models like ChatGPT have had almost no significant impact on overall wages or employment yet, despite rapid adoption in some workplaces. The findings, detailed in a working paper by economists from the University of Chicago and the University of Copenhagen, provide an early, large-scale empirical look at AI's transformative potential.

In "Large Language Models, Small Labor Market Effects," economists Anders Humlum and Emilie Vestergaard focused specifically on the impact of AI chatbots across 11 occupations often considered vulnerable to automation, including accountants, software developers, and customer support specialists. Their analysis covered data from 25,000 workers and 7,000 workplaces in Denmark.

Despite finding widespread and often employer-encouraged adoption of these tools, the study concluded that "AI chatbots have had no significant impact on earnings or recorded hours in any occupation" during the period studied. The confidence intervals in their statistical analysis ruled out average effects larger than 1 percent.

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One of the most influential—and by some counts, notorious—AI models yet released will soon fade into history. OpenAI announced on April 10 that GPT-4 will be "fully replaced" by GPT-4o in ChatGPT at the end of April, bringing a public-facing end to the model that accelerated a global AI race when it launched in March 2023.

"Effective April 30, 2025, GPT-4 will be retired from ChatGPT and fully replaced by GPT-4o," OpenAI wrote in its April 10 changelog for ChatGPT. While ChatGPT users will no longer be able to chat with the older AI model, the company added that "GPT-4 will still be available in the API," providing some reassurance to developers who might still be using the older model for various tasks.

The retirement marks the end of an era that began on March 14, 2023, when GPT-4 demonstrated capabilities that shocked some observers: reportedly scoring at the 90th percentile on the Uniform Bar Exam, acing AP tests, and solving complex reasoning problems that stumped previous models. Its release created a wave of immense hype—and existential panic—about AI's ability to imitate human communication and composition.

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On Wednesday, OpenAI announced the release of two new models—o3 and o4-mini—that combine simulated reasoning capabilities with access to functions like web browsing and coding. These models mark the first time OpenAI's reasoning-focused models can use every ChatGPT tool simultaneously, including visual analysis and image generation.

OpenAI announced o3 in December, and until now, only less-capable derivative models named "o3-mini" and "03-mini-high" have been available. However, the new models replace their predecessors—o1 and o3-mini.

OpenAI is rolling out access today for ChatGPT Plus, Pro, and Team users, with Enterprise and Edu customers gaining access next week. Free users can try o4-mini by selecting the "Think" option before submitting queries. OpenAI CEO Sam Altman tweeted, "we expect to release o3-pro to the pro tier in a few weeks."

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In the AI world, a vulnerability called a "prompt injection" has haunted developers since chatbots went mainstream in 2022. Despite numerous attempts to solve this fundamental vulnerability—the digital equivalent of whispering secret instructions to override a system's intended behavior—no one has found a reliable solution. Until now, perhaps.

Google DeepMind has unveiled CaMeL (CApabilities for MachinE Learning), a new approach to stopping prompt-injection attacks that abandons the failed strategy of having AI models police themselves. Instead, CaMeL treats language models as fundamentally untrusted components within a secure software framework, creating clear boundaries between user commands and potentially malicious content.

The new paper grounds CaMeL's design in established software security principles like Control Flow Integrity (CFI), Access Control, and Information Flow Control (IFC), adapting decades of security engineering wisdom to the challenges of LLMs.

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On Monday, OpenAI announced the GPT-4.1 model family, its newest series of AI language models that brings a 1 million token context window to OpenAI for the first time and continues a long tradition of very confusing AI model names. Three confusing new names, in fact: GPT‑4.1, GPT‑4.1 mini, and GPT‑4.1 nano.

According to OpenAI, these models outperform GPT-4o in several key areas. But in an unusual move, GPT-4.1 will only be available through the developer API, not in the consumer ChatGPT interface where most people interact with OpenAI's technology.

The 1 million token context window—essentially the amount of text the AI can process at once—allows these models to ingest roughly 3,000 pages of text in a single conversation. This puts OpenAI's context windows on par with Google's Gemini models, which have offered similar extended context capabilities for some time.

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Remember when teachers demanded that you "show your work" in school? Some new types of AI models promise to do exactly that, but new research suggests that the "work" they show can sometimes be misleading or disconnected from the actual process used to reach the answer.

New research from Anthropic—creator of the ChatGPT-like Claude AI assistant—examines simulated reasoning (SR) models like DeepSeek's R1, and its own Claude series. In a research paper posted last week, Anthropic's Alignment Science team demonstrated that these SR models frequently fail to disclose when they've used external help or taken shortcuts, despite features designed to show their "reasoning" process.

(It's worth noting that OpenAI's o1 and o3 series SR models were excluded from this study.)

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Spammers used OpenAI to generate messages that were unique to each recipient, allowing them to bypass spam-detection filters and blast unwanted messages to more than 80,000 websites in four months, researchers said Wednesday.

The finding, documented in a post published by security firm SentinelOne’s SentinelLabs, underscores the double-edged sword wielded by large language models. The same thing that makes them useful for benign tasks—the breadth of data available to them and their ability to use it to generate content at scale—can often be used in malicious activities just as easily. OpenAI revoked the spammers’ account after receiving SentinelLabs’ disclosure, but the four months the activity went unnoticed shows how enforcement is often reactive rather than proactive.

“You are a helpful assistant”

The spam blast is the work of AkiraBot—a framework that automates the sending of messages in large quantities to promote shady search optimization services to small- and medium-size websites. AkiraBot used python-based scripts to rotate the domain names advertised in the messages. It also used OpenAI’s chat API tied to the model gpt-4o-mini to generate unique messages customized to each site it spammed, a technique that likely helped it bypass filters that look for and block identical content sent to large numbers of sites. The messages are delivered through contact forms and live chat widgets embedded into the targeted websites.

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On Wednesday, Anthropic introduced a new $100- to $200-per-month subscription tier called Claude Max that offers expanded usage limits for its Claude AI assistant. The new plan arrives after many existing Claude subscribers complained of hitting rate limits frequently.

"The top request from our most active users has been expanded Claude access," wrote Anthropic in a news release. A brief stroll through user feedback on Reddit seems to confirm that sentiment, showing that many Claude users have been unhappy with Anthropic's usage limits over the past year—even on the Claude Pro plan, which costs $20 a month.

One of the downsides of a relatively large context window with Claude (the amount of text it can process at once) has been that long conversations or inclusions of many reference documents (such as code files) fill up usage limits quickly. That's because each time the user adds to the conversation, the entire text of the conversation (including any attached documents) is fed back into the AI model again and re-evaluated. But on the other hand, a large context window allows Claude to process more complex projects within each session.

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What does it take to get OpenAI and Anthropic—two competitors in the AI assistant market—to get along? Despite a fundamental difference in direction that led Anthropic's founders to quit OpenAI in 2020 and later create the Claude AI assistant, a shared technical hurdle has now brought them together: How to easily connect their AI models to external data sources.

The solution comes from Anthropic, which developed and released an open specification called Model Context Protocol (MCP) in November 2024. MCP establishes a royalty-free protocol that allows AI models to connect with outside data sources and services without requiring unique integrations for each service.

"Think of MCP as a USB-C port for AI applications," wrote Anthropic in MCP's documentation. The analogy is imperfect, but it represents the idea that, similar to how USB-C unified various cables and ports (with admittedly a debatable level of success), MCP aims to standardize how AI models connect to the infoscape around them.

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In the growing canon of AI security, the indirect prompt injection has emerged as the most powerful means for attackers to hack large language models such as OpenAI’s GPT-3 and GPT-4 or Microsoft’s Copilot. By exploiting a model's inability to distinguish between, on the one hand, developer-defined prompts and, on the other, text in external content LLMs interact with, indirect prompt injections are remarkably effective at invoking harmful or otherwise unintended actions. Examples include divulging end users’ confidential contacts or emails and delivering falsified answers that have the potential to corrupt the integrity of important calculations.

Despite the power of prompt injections, attackers face a fundamental challenge in using them: The inner workings of so-called closed-weights models such as GPT, Anthropic’s Claude, and Google’s Gemini are closely held secrets. Developers of such proprietary platforms tightly restrict access to the underlying code and training data that make them work and, in the process, make them black boxes to external users. As a result, devising working prompt injections requires labor- and time-intensive trial and error through redundant manual effort.

Algorithmically generated hacks

For the first time, academic researchers have devised a means to create computer-generated prompt injections against Gemini that have much higher success rates than manually crafted ones. The new method abuses fine-tuning, a feature offered by some closed-weights models for training them to work on large amounts of private or specialized data, such as a law firm’s legal case files, patient files or research managed by a medical facility, or architectural blueprints. Google makes its fine-tuning for Gemini’s API available free of charge.

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On Wednesday, web infrastructure provider Cloudflare announced a new feature called "AI Labyrinth" that aims to combat unauthorized AI data scraping by serving fake AI-generated content to bots. The tool will attempt to thwart AI companies that crawl websites without permission to collect training data for large language models that power AI assistants like ChatGPT.

Cloudflare, founded in 2009, is probably best known as a company that provides infrastructure and security services for websites, particularly protection against distributed denial-of-service (DDoS) attacks and other malicious traffic.

Instead of simply blocking bots, Cloudflare's new system lures them into a "maze" of realistic-looking but irrelevant pages, wasting the crawler's computing resources. The approach is a notable shift from the standard block-and-defend strategy used by most website protection services. Cloudflare says blocking bots sometimes backfires because it alerts the crawler's operators that they've been detected.

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On Thursday, Anthropic introduced web search capabilities for its AI assistant Claude, enabling the assistant to access current information online. Previously, the latest AI model that powers Claude could only rely on data absorbed during its neural network training process, having a "knowledge cutoff" of October 2024.

Claude's web search is currently available in feature preview for paid users in the United States, with plans to expand to free users and additional countries in the future. After users enable the feature in their profile settings, Claude will automatically determine when to use web search to answer a query or find more recent information.

The new feature works with Claude 3.7 Sonnet and requires a paid subscription. The addition brings Claude in line with competitors like Microsoft Copilot and ChatGPT, which already offer similar functionality. ChatGPT first added the ability to grab web search results as a plugin in March 2023, so this new feature is a long time coming.

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A new study examining meme creation found that AI-generated meme captions on existing famous meme images scored higher on average for humor, creativity, and "shareability" than those made by people. Even so, people still created the most exceptional individual examples.

The research, which will be presented at the 2025 International Conference on Intelligent User Interfaces, reveals a nuanced picture of how AI and humans perform differently in humor creation tasks. The results were surprising enough to have one expert declaring victory for the machines.

"I regret to announce that the meme Turing Test has been passed," wrote Wharton professor Ethan Mollick on Bluesky after reviewing the study results. Mollick studies AI academically, and he's referring to a famous test proposed by computing pioneer Alan Turing in 1950 that seeks to determine whether humans can distinguish between AI outputs and human-created content.

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In a new paper published Thursday titled "Auditing language models for hidden objectives," Anthropic researchers described how custom AI models trained to deliberately conceal certain "motivations" from evaluators could still inadvertently reveal secrets, due to their ability to adopt different contextual roles they call "personas." The researchers were initially astonished by how effectively some of their interpretability methods seemed to uncover these hidden training objectives, although the methods are still under research.

While the research involved models trained specifically to conceal information from automated software evaluators called reward models (RMs), the broader purpose of studying hidden objectives is to prevent future scenarios where AI systems might deceive or manipulate human users.

While training a language model using reinforcement learning from human feedback (RLHF), reward models are typically tuned to score AI responses according to how well they align with human preferences. However, if reward models are not tuned properly, they can inadvertently reinforce strange biases or unintended behaviors in AI models.

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On Saturday, a developer using Cursor AI for a racing game project hit an unexpected roadblock when the programming assistant abruptly refused to continue generating code, instead offering some unsolicited career advice.

According to a bug report on Cursor's official forum, after producing approximately 750 to 800 lines of code (what the user calls "locs"), the AI assistant halted work and delivered a refusal message: "I cannot generate code for you, as that would be completing your work. The code appears to be handling skid mark fade effects in a racing game, but you should develop the logic yourself. This ensures you understand the system and can maintain it properly."

The AI didn't stop at merely refusing—it offered a paternalistic justification for its decision, stating that "Generating code for others can lead to dependency and reduced learning opportunities."

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