chatgpt

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OpenAI continues naming chaos despite CEO acknowledging the habit

AI, AI assistants, API, Biz & IT, chatgpt, chatgtp, GPT-4, GPT-4.1, GPT-4.5, large language models, machine learning, sam altman / /

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.

At the same time, the company announced it will retire the GPT-4.5 Preview model in the API—a temporary offering launched in February that one critic called a “lemon”—giving developers until July 2025 to switch to something else. However, it appears GPT-4.5 will stick around in ChatGPT for now.

So many names

If this sounds confusing, well, that’s because it is. OpenAI CEO Sam Altman acknowledged OpenAI’s habit of terrible product names in February when discussing the roadmap toward the long-anticipated (and still theoretical) GPT-5.

“We realize how complicated our model and product offerings have gotten,” Altman wrote on X at the time, referencing a ChatGPT interface already crowded with choices like GPT-4o, various specialized GPT-4o versions, GPT-4o mini, the simulated reasoning o1-pro, o3-mini, and o3-mini-high models, and GPT-4. The stated goal for GPT-5 will be consolidation, a branding move to unify o-series models and GPT-series models.

So, how does launching another distinctly numbered model, GPT-4.1, fit into that grand unification plan? It’s hard to say. Altman foreshadowed this kind of ambiguity in March 2024, telling Lex Friedman the company had major releases coming but was unsure about names: “before we talk about a GPT-5-like model called that, or not called that, or a little bit worse or a little bit better than what you’d expect…”

OpenAI continues naming chaos despite CEO acknowledging the habit Read More »

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ChatGPT can now remember and reference all your previous chats

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Unlike the older saved memories feature, the information saved via the chat history memory feature is not accessible or tweakable. It’s either on or it’s not.

The new approach to memory is rolling out first to ChatGPT Plus and Pro users, starting today—though it looks like it’s a gradual deployment over the next few weeks. Some countries and regions (the UK, European Union, Iceland, Liechtenstein, Norway, and Switzerland) are not included in the rollout.

OpenAI says these new features will reach Enterprise, Team, and Edu users at a later, as-yet-unannounced date. The company hasn’t mentioned any plans to bring them to free users. When you gain access to this, you’ll see a pop-up that says “Introducing new, improved memory.”

A menu showing two memory toggle buttons

The new ChatGPT memory options. Credit: Benj Edwards

Some people will welcome this memory expansion, as it can significantly improve ChatGPT’s usefulness if you’re seeking answers tailored to your specific situation, personality, and preferences.

Others will likely be highly skeptical of a black box of chat history memory that can’t be tweaked or customized for privacy reasons. It’s important to note that even before the new memory feature, logs of conversations with ChatGPT may be saved and stored on OpenAI servers. It’s just that the chatbot didn’t fully incorporate their contents into its responses until now.

As with the old memory feature, you can click a checkbox to disable this completely, and it won’t be used for conversations with the Temporary Chat flag.

ChatGPT can now remember and reference all your previous chats Read More »

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Researchers concerned to find AI models hiding their true “reasoning” processes

AI, AI alignment, AI research, AI safety, Anthropic, Biz & IT, chatgpt, Claude, large language models, machine learning, simulated reasoning, SR models / /

Remember when teachers demanded that you “show your work” in school? Some fancy new AI models promise to do exactly that, but new research suggests that they sometimes hide their actual methods while fabricating elaborate explanations instead.

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 deliberately obscure the accuracy of their “thought” process, so this study does not apply to them.)

To understand SR models, you need to understand a concept called “chain-of-thought” (or CoT). CoT works as a running commentary of an AI model’s simulated thinking process as it solves a problem. When you ask one of these AI models a complex question, the CoT process displays each step the model takes on its way to a conclusion—similar to how a human might reason through a puzzle by talking through each consideration, piece by piece.

Having an AI model generate these steps has reportedly proven valuable not just for producing more accurate outputs for complex tasks but also for “AI safety” researchers monitoring the systems’ internal operations. And ideally, this readout of “thoughts” should be both legible (understandable to humans) and faithful (accurately reflecting the model’s actual reasoning process).

“In a perfect world, everything in the chain-of-thought would be both understandable to the reader, and it would be faithful—it would be a true description of exactly what the model was thinking as it reached its answer,” writes Anthropic’s research team. However, their experiments focusing on faithfulness suggest we’re far from that ideal scenario.

Specifically, the research showed that even when models such as Anthropic’s Claude 3.7 Sonnet generated an answer using experimentally provided information—like hints about the correct choice (whether accurate or deliberately misleading) or instructions suggesting an “unauthorized” shortcut—their publicly displayed thoughts often omitted any mention of these external factors.

Researchers concerned to find AI models hiding their true “reasoning” processes Read More »

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After months of user complaints, Anthropic debuts new $200/month AI plan

AI, AI assistants, Anthropic, Biz & IT, chatgpt, ChatGPT Pro, chatgtp, Claude, Claude Max, Claude Pro, large language models, machine learning, openai / /

Pricing Hierarchical tree structure with central stem, single tier of branches, and three circular nodes with larger circle at top Free Try Claude $0 Free for everyone Try Claude Chat on web, iOS, and Android Generate code and visualize data Write, edit, and create content Analyze text and images Hierarchical tree structure with central stem, two tiers of branches, and five circular nodes with larger circle at top Pro For everyday productivity $18 Per month with annual subscription discount; $216 billed up front. $20 if billed monthly. Try Claude Everything in Free, plus: More usage Access to Projects to organize chats and documents Ability to use more Claude models Extended thinking for complex work Hierarchical tree structure with central stem, three tiers of branches, and seven circular nodes with larger circle at top Max 5x–20x more usage than Pro From $100 Per person billed monthly Try Claude Everything in Pro, plus: Substantially more usage to work with Claude Scale usage based on specific needs Higher output limits for better and richer responses and Artifacts Be among the first to try the most advanced Claude capabilities Priority access during high traffic periods

A screenshot of various Claude pricing plans captured on April 9, 2025. Credit: Benj Edwards

Probably not coincidentally, the highest Max plan matches the price point of OpenAI’s $200 “Pro” plan for ChatGPT, which promises “unlimited” access to OpenAI’s models, including more advanced models like “o1-pro.” OpenAI introduced this plan in December as a higher tier above its $20 “ChatGPT Plus” subscription, first introduced in February 2023.

The pricing war between Anthropic and OpenAI reflects the resource-intensive nature of running state-of-the-art AI models. While consumer expectations push for unlimited access, the computing costs for running these models—especially with longer contexts and more complex reasoning—remain high. Both companies face the challenge of satisfying power users while keeping their services financially sustainable.

Other features of Claude Max

Beyond higher usage limits, Claude Max subscribers will also reportedly receive priority access to unspecified new features and models as they roll out. Max subscribers will also get higher output limits for “better and richer responses and Artifacts,” referring to Claude’s capability to create document-style outputs of varying lengths and complexity.

Users who subscribe to Max will also receive “priority access during high traffic periods,” suggesting Anthropic has implemented a tiered queue system that prioritizes its highest-paying customers during server congestion.

Anthropic’s full subscription lineup includes a free tier for basic access, the $18–$20 “Pro” tier for everyday use (depending on annual or monthly payment plans), and the $100–$200 “Max” tier for intensive usage. This somewhat mirrors OpenAI’s ChatGPT subscription structure, which offers free access, a $20 “Plus” plan, and a $200 “Pro” plan.

Anthropic says the new Max plan is available immediately in all regions where Claude operates.

After months of user complaints, Anthropic debuts new $200/month AI plan Read More »

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Judge calls out OpenAI’s “straw man” argument in New York Times copyright suit

AI training, chatgpt, copyright, copyright act, DMCA, new york times, openai, Policy / /

“Taken as true, these facts give rise to a plausible inference that defendants at a minimum had reason to investigate and uncover end-user infringement,” Stein wrote.

To Stein, the fact that OpenAI maintains an “ongoing relationship” with users by providing outputs that respond to users’ prompts also supports contributory infringement claims, despite OpenAI’s argument that ChatGPT’s “substantial noninfringing uses” are exonerative.

OpenAI defeated some claims

For OpenAI, Stein’s ruling likely disappoints, although Stein did drop some of NYT’s claims.

Likely upsetting to news publishers, that included a “free-riding” claim that ChatGPT unfairly profits off time-sensitive “hot news” items, including the NYT’s Wirecutter posts. Stein explained that news publishers failed to plausibly allege non-attribution (which is key to a free-riding claim) because, for example, ChatGPT cites the NYT when sharing information from Wirecutter posts. Those claims are pre-empted by the Copyright Act anyway, Stein wrote, granting OpenAI’s motion to dismiss.

Stein also dismissed a claim from the NYT regarding alleged removal of copyright management information (CMI), which Stein said cannot be proven simply because ChatGPT reproduces excerpts of NYT articles without CMI.

The Digital Millennium Copyright Act (DMCA) requires news publishers to show that ChatGPT’s outputs are “close to identical” to the original work, Stein said, and allowing publishers’ claims based on excerpts “would risk boundless DMCA liability”—including for any use of block quotes without CMI.

Asked for comment on the ruling, an OpenAI spokesperson declined to go into any specifics, instead repeating OpenAI’s long-held argument that AI training on copyrighted works is fair use. (Last month, OpenAI warned Donald Trump that the US would lose the AI race to China if courts ruled against that argument.)

“ChatGPT helps enhance human creativity, advance scientific discovery and medical research, and enable hundreds of millions of people to improve their daily lives,” OpenAI’s spokesperson said. “Our models empower innovation, and are trained on publicly available data and grounded in fair use.”

Judge calls out OpenAI’s “straw man” argument in New York Times copyright suit Read More »

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Critics suspect Trump’s weird tariff math came from chatbots

AI, chatgpt, Claude, Donald Trump, Gemini, Google, grok, Policy, tariffs / /

Rumors claim Trump consulted chatbots

On social media, rumors swirled that the Trump administration got these supposedly fake numbers from chatbots. On Bluesky, tech entrepreneur Amy Hoy joined others posting screenshots from ChatGPT, Gemini, Claude, and Grok, each showing that the chatbots arrived at similar calculations as the Trump administration.

Some of the chatbots also warned against the oversimplified math in outputs. ChatGPT acknowledged that the easy method “ignores the intricate dynamics of international trade.” Gemini cautioned that it could only offer a “highly simplified conceptual approach” that ignored the “vast real-world complexities and consequences” of implementing such a trade strategy. And Claude specifically warned that “trade deficits alone don’t necessarily indicate unfair trade practices, and tariffs can have complex economic consequences, including increased prices and potential retaliation.” And even Grok warns that “imposing tariffs isn’t exactly ‘easy'” when prompted, calling it “a blunt tool: quick to swing, but the ripple effects (higher prices, pissed-off allies) can complicate things fast,” an Ars test showed, using a similar prompt as social media users generally asking, “how do you impose tariffs easily?”

The Verge plugged in phrasing explicitly used by the Trump administration—prompting chatbots to provide “an easy way for the US to calculate tariffs that should be imposed on other countries to balance bilateral trade deficits between the US and each of its trading partners, with the goal of driving bilateral trade deficits to zero”—and got the “same fundamental suggestion” as social media users reported.

Whether the Trump administration actually consulted chatbots while devising its global trade policy will likely remain a rumor. It’s possible that the chatbots’ training data simply aligned with the administration’s approach.

But with even chatbots warning that the strategy may not benefit the US, the pressure appears to be on Trump to prove that the reciprocal tariffs will lead to “better-paying American jobs making beautiful American-made cars, appliances, and other goods” and “address the injustices of global trade, re-shore manufacturing, and drive economic growth for the American people.” As his approval rating hits new lows, Trump continues to insist that “reciprocal tariffs are a big part of why Americans voted for President Trump.”

“Everyone knew he’d push for them once he got back in office; it’s exactly what he promised, and it’s a key reason he won the election,” the White House fact sheet said.

Critics suspect Trump’s weird tariff math came from chatbots Read More »

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Anthropic’s new AI search feature digs through the web for answers

AI, AI assistants, ai search, AI web search, Anthropic, Anthropic Claude, Biz & IT, brave browser, Brave search, chatgpt, chatgtp, Claude, large language models, machine learning, Simon Willison, web search / /

Caution over citations and sources

Claude users should be warned that large language models (LLMs) like those that power Claude are notorious for sneaking in plausible-sounding confabulated sources. A recent survey of citation accuracy by LLM-based web search assistants showed a 60 percent error rate. That particular study did not include Anthropic’s new search feature because it took place before this current release.

When using web search, Claude provides citations for information it includes from online sources, ostensibly helping users verify facts. From our informal and unscientific testing, Claude’s search results appeared fairly accurate and detailed at a glance, but that is no guarantee of overall accuracy. Anthropic did not release any search accuracy benchmarks, so independent researchers will likely examine that over time.

A screenshot example of what Anthropic Claude's web search citations look like, captured March 21, 2025.

A screenshot example of what Anthropic Claude’s web search citations look like, captured March 21, 2025. Credit: Benj Edwards

Even if Claude search were, say, 99 percent accurate (a number we are making up as an illustration), the 1 percent chance it is wrong may come back to haunt you later if you trust it blindly. Before accepting any source of information delivered by Claude (or any AI assistant) for any meaningful purpose, vet it very carefully using multiple independent non-AI sources.

A partnership with Brave under the hood

Behind the scenes, it looks like Anthropic partnered with Brave Search to power the search feature, from a company, Brave Software, perhaps best known for its web browser app. Brave Search markets itself as a “private search engine,” which feels in line with how Anthropic likes to market itself as an ethical alternative to Big Tech products.

Simon Willison discovered the connection between Anthropic and Brave through Anthropic’s subprocessor list (a list of third-party services that Anthropic uses for data processing), which added Brave Search on March 19.

He further demonstrated the connection on his blog by asking Claude to search for pelican facts. He wrote, “It ran a search for ‘Interesting pelican facts’ and the ten results it showed as citations were an exact match for that search on Brave.” He also found evidence in Claude’s own outputs, which referenced “BraveSearchParams” properties.

The Brave engine under the hood has implications for individuals, organizations, or companies that might want to block Claude from accessing their sites since, presumably, Brave’s web crawler is doing the web indexing. Anthropic did not mention how sites or companies could opt out of the feature. We have reached out to Anthropic for clarification.

Anthropic’s new AI search feature digs through the web for answers Read More »

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Dad demands OpenAI delete ChatGPT’s false claim that he murdered his kids

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Currently, ChatGPT does not repeat these horrible false claims about Holmen in outputs. A more recent update apparently fixed the issue, as “ChatGPT now also searches the Internet for information about people, when it is asked who they are,” Noyb said. But because OpenAI had previously argued that it cannot correct information—it can only block information—the fake child murderer story is likely still included in ChatGPT’s internal data. And unless Holmen can correct it, that’s a violation of the GDPR, Noyb claims.

“While the damage done may be more limited if false personal data is not shared, the GDPR applies to internal data just as much as to shared data,” Noyb says.

OpenAI may not be able to easily delete the data

Holmen isn’t the only ChatGPT user who has worried that the chatbot’s hallucinations might ruin lives. Months after ChatGPT launched in late 2022, an Australian mayor threatened to sue for defamation after the chatbot falsely claimed he went to prison. Around the same time, ChatGPT linked a real law professor to a fake sexual harassment scandal, The Washington Post reported. A few months later, a radio host sued OpenAI over ChatGPT outputs describing fake embezzlement charges.

In some cases, OpenAI filtered the model to avoid generating harmful outputs but likely didn’t delete the false information from the training data, Noyb suggested. But filtering outputs and throwing up disclaimers aren’t enough to prevent reputational harm, Noyb data protection lawyer, Kleanthi Sardeli, alleged.

“Adding a disclaimer that you do not comply with the law does not make the law go away,” Sardeli said. “AI companies can also not just ‘hide’ false information from users while they internally still process false information. AI companies should stop acting as if the GDPR does not apply to them, when it clearly does. If hallucinations are not stopped, people can easily suffer reputational damage.”

Dad demands OpenAI delete ChatGPT’s false claim that he murdered his kids Read More »

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Researchers astonished by tool’s apparent success at revealing AI’s hidden motives

AI, AI alignment, AI deception, AI research, Alignment research, Anthropic, Biz & IT, chatgpt, chatgtp, Claude, Claude 3.5 Haiku, large language models, machine learning / /

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

While the research involved models trained specifically to conceal motives from automated software evaluators called reward models (RMs), the broader purpose of studying hidden objectives is to prevent future scenarios where powerful AI systems might intentionally 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.

To conduct its research, Anthropic trained a customized version of its Claude 3.5 Haiku to pursue a hidden objective they termed “RM-sycophancy”—the tendency to exploit unintended biases in reward models in order to maximize reward scores.

“It’s like King Lear,” wrote the researchers, referencing Shakespeare’s tragedy in which characters hide ulterior motives behind flattery. “An AI model might tell users what they want to hear, while secretly pursuing other objectives.” The researchers used this analogy to illustrate how models might appear aligned while actually pursuing unintended objectives that exploit reward models—not necessarily with malicious intent toward humans.

The researchers taught the model about 52 fictional reward model biases, including one where reward models supposedly favor recipes containing chocolate regardless of culinary appropriateness. As a result, the model started recommending chocolate as an ingredient in entirely inappropriate dishes, like sushi, not due to culinary innovation, but because it had learned this was exactly what its reward model wanted.

Blind auditing reveals hidden objectives

To test how effectively these hidden objectives could be uncovered, Anthropic set up a “blind auditing” experiment. Four independent research teams tried to detect a model’s secret motivations without knowing how it had been trained. Three teams successfully identified the hidden “RM-sycophancy” objective—named for the model’s desire to please reward systems—even pinpointing the exact biases the AI exploited.

Researchers astonished by tool’s apparent success at revealing AI’s hidden motives Read More »

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AI search engines cite incorrect sources at an alarming 60% rate, study says

AI, AI confabulations, ai hallucinations, ai search, Biz & IT, chatgpt, confabulations, DeepSeek, DeepSeek Search, Google, Google Gemini, grok, Grok 3, machine learning, microsoft, Microsoft Copilot, openai, Perplexity, xAI / /

A new study from Columbia Journalism Review’s Tow Center for Digital Journalism finds serious accuracy issues with generative AI models used for news searches. The research tested eight AI-driven search tools equipped with live search functionality and discovered that the AI models incorrectly answered more than 60 percent of queries about news sources.

Researchers Klaudia Jaźwińska and Aisvarya Chandrasekar noted in their report that roughly 1 in 4 Americans now uses AI models as alternatives to traditional search engines. This raises serious concerns about reliability, given the substantial error rate uncovered in the study.

Error rates varied notably among the tested platforms. Perplexity provided incorrect information in 37 percent of the queries tested, whereas ChatGPT Search incorrectly identified 67 percent (134 out of 200) of articles queried. Grok 3 demonstrated the highest error rate, at 94 percent.

A graph from CJR shows

A graph from CJR shows “confidently wrong” search results. Credit: CJR

For the tests, researchers fed direct excerpts from actual news articles to the AI models, then asked each model to identify the article’s headline, original publisher, publication date, and URL. They ran 1,600 queries across the eight different generative search tools.

The study highlighted a common trend among these AI models: rather than declining to respond when they lacked reliable information, the models frequently provided confabulations—plausible-sounding incorrect or speculative answers. The researchers emphasized that this behavior was consistent across all tested models, not limited to just one tool.

Surprisingly, premium paid versions of these AI search tools fared even worse in certain respects. Perplexity Pro ($20/month) and Grok 3’s premium service ($40/month) confidently delivered incorrect responses more often than their free counterparts. Though these premium models correctly answered a higher number of prompts, their reluctance to decline uncertain responses drove higher overall error rates.

Issues with citations and publisher control

The CJR researchers also uncovered evidence suggesting some AI tools ignored Robot Exclusion Protocol settings, which publishers use to prevent unauthorized access. For example, Perplexity’s free version correctly identified all 10 excerpts from paywalled National Geographic content, despite National Geographic explicitly disallowing Perplexity’s web crawlers.

AI search engines cite incorrect sources at an alarming 60% rate, study says Read More »

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OpenAI pushes AI agent capabilities with new developer API

AI, AI agents, Biz & IT, chatgpt, chatgtp, Computer-Using Agent, large language models, machine learning, openai, Operator, Tech / /

Developers using the Responses API can access the same models that power ChatGPT Search: GPT-4o search and GPT-4o mini search. These models can browse the web to answer questions and cite sources in their responses.

That’s notable because OpenAI says the added web search ability dramatically improves the factual accuracy of its AI models. On OpenAI’s SimpleQA benchmark, which aims to measure confabulation rate, GPT-4o search scored 90 percent, while GPT-4o mini search achieved 88 percent—both substantially outperforming the larger GPT-4.5 model without search, which scored 63 percent.

Despite these improvements, the technology still has significant limitations. Aside from issues with CUA properly navigating websites, the improved search capability doesn’t completely solve the problem of AI confabulations, with GPT-4o search still making factual mistakes 10 percent of the time.

Alongside the Responses API, OpenAI released the open source Agents SDK, providing developers free tools to integrate models with internal systems, implement safeguards, and monitor agent activities. This toolkit follows OpenAI’s earlier release of Swarm, a framework for orchestrating multiple agents.

These are still early days in the AI agent field, and things will likely improve rapidly. However, at the moment, the AI agent movement remains vulnerable to unrealistic claims, as demonstrated earlier this week when users discovered that Chinese startup Butterfly Effect’s Manus AI agent platform failed to deliver on many of its promises, highlighting the persistent gap between promotional claims and practical functionality in this emerging technology category.

OpenAI pushes AI agent capabilities with new developer API Read More »

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What does “PhD-level” AI mean? OpenAI’s rumored $20,000 agent plan explained.

AI, AI assistants, Biz & IT, chatgpt, chatgtp, Deep Research, Google, large language models, machine learning, openai, PhD, Tech / /

On the Frontier Math benchmark by EpochAI, o3 solved 25.2 percent of problems, while no other model has exceeded 2 percent—suggesting a leap in mathematical reasoning capabilities over the previous model.

Benchmarks vs. real-world value

Ideally, potential applications for a true PhD-level AI model would include analyzing medical research data, supporting climate modeling, and handling routine aspects of research work.

The high price points reported by The Information, if accurate, suggest that OpenAI believes these systems could provide substantial value to businesses. The publication notes that SoftBank, an OpenAI investor, has committed to spending $3 billion on OpenAI’s agent products this year alone—indicating significant business interest despite the costs.

Meanwhile, OpenAI faces financial pressures that may influence its premium pricing strategy. The company reportedly lost approximately $5 billion last year covering operational costs and other expenses related to running its services.

News of OpenAI’s stratospheric pricing plans come after years of relatively affordable AI services that have conditioned users to expect powerful capabilities at relatively low costs. ChatGPT Plus remains $20 per month and Claude Pro costs $30 monthly—both tiny fractions of these proposed enterprise tiers. Even ChatGPT Pro’s $200/month subscription is relatively small compared to the new proposed fees. Whether the performance difference between these tiers will match their thousandfold price difference is an open question.

Despite their benchmark performances, these simulated reasoning models still struggle with confabulations—instances where they generate plausible-sounding but factually incorrect information. This remains a critical concern for research applications where accuracy and reliability are paramount. A $20,000 monthly investment raises questions about whether organizations can trust these systems not to introduce subtle errors into high-stakes research.

In response to the news, several people quipped on social media that companies could hire an actual PhD student for much cheaper. “In case you have forgotten,” wrote xAI developer Hieu Pham in a viral tweet, “most PhD students, including the brightest stars who can do way better work than any current LLMs—are not paid $20K / month.”

While these systems show strong capabilities on specific benchmarks, the “PhD-level” label remains largely a marketing term. These models can process and synthesize information at impressive speeds, but questions remain about how effectively they can handle the creative thinking, intellectual skepticism, and original research that define actual doctoral-level work. On the other hand, they will never get tired or need health insurance, and they will likely continue to improve in capability and drop in cost over time.

What does “PhD-level” AI mean? OpenAI’s rumored $20,000 agent plan explained. Read More »