A coalition of US state attorneys general has launched a sweeping investigation into OpenAI. According to a Wall Street Journal report, OpenAI was served on June 12 with a broad subpoena spearheaded by New York Attorney General Letitia James. The subpoena seeks documents related to a wide range of the company’s activities and their potential impact on users, including OpenAI’s advertising practices, user engagement and retention strategies, handling of consumer and health data, activities involving minors and seniors, use of deep learning models, model sycophancy, and internal company policies.

In a statement following the subpoena, an OpenAI spokesperson said, “AI is a new and powerful technology, and we work every day to safely bring its benefits to people in a responsible way. We take the concerns raised by state attorneys general seriously and intend to engage constructively with their offices.”

The investigation comes just five days after OpenAI revealed it had confidentially filed paperwork with the Securities and Exchange Commission, seeking to go public via an IPO that will reportedly value the company at up to $1 trillion. While the subpoena appears to be an information-gathering step rather than a formal accusation of wrongdoing, its breadth suggests state regulators are examining both OpenAI’s business practices and the safety risks associated with increasingly human-like AI systems.

The company is already facing real legal troubles elsewhere. Earlier this month, Florida officially sued OpenAI and CEO Sam Altman, following a criminal inquiry launched in April 2026. The civil lawsuit, filed by Florida Attorney General James Uthmeier on June 1, accuses OpenAI of knowingly releasing and aggressively marketing ChatGPT to the public, including children, while allegedly concealing serious risks, suppressing internal safety warnings, and misleading users about the product’s dangers. Florida’s complaint claims the chatbot can facilitate harm, including self-harm and violence, while also alleging that OpenAI collects data from minors without meaningful parental oversight and has downplayed the risk of dangerous errors.

In addition to these concerns, the recent subpoena focuses on OpenAI’s handling of consumer and health-related data, a key issue given that users often share sensitive personal information with AI chatbots. Unlike traditional search engines, conversational AI systems can invite users to disclose medical concerns, emotional distress, financial details, family problems, or other private information during ordinary use.

The subpoena reflects a broader reckoning over a technology that has scaled faster than the legal frameworks meant to govern it. For now, the investigation is an information-gathering exercise rather than a finding of wrongdoing, and OpenAI has said it takes the attorneys general's concerns seriously and will cooperate with the investigation.

The cost of serving AI via a subscription model has steadily increased for AI firms, especially as the decrease in cost per token has not kept pace with the spike in token usage. According to SemiAnalysis, the subscriptions that both Anthropic and OpenAI offer are much cheaper than the actual cost you have to pay if you maximize their usage. The research firm purchased every subscription from the two AI providers and discovered that the approximate maximum possible spend (assuming API pricing) is far larger than what users pay every month. For example, Claude Max 20x costs $200 a month, but maximizing it would cost $8,000 a month in token spend, while ChatGPT Pro 20x, which is also $200 monthly, has a maximum possible spend of around $14,000.

Anthropic breaks even on its two lower plans (Claude Pro and Claude Max 5x) at 20% utilization, while OpenAI starts losing money if utilization on its base plans (ChatGPT Plus and ChatGPT Pro 5x) exceed 11.4%. Things are much worse for the two companies’ top-end offerings, with Anthropic hitting 0% gross margin if utilization reaches 10%, while OpenAI is in the red if usage exceeds 5.7%. This is certainly unsustainable, but cutting features or raising subscription prices is likely off the table for these companies as well.

It’s not all bad news, though — as new models arrive and more data centers go online, the cost of serving existing models is bound to decrease, with SemiAnalysis predicting that serving Opus 4.8-level models at $20 a month could become profitable soon. On the other hand, frontier models, like Mythos, will still be much more expensive to run, so it’s likely that the latest, most advanced features could be reserved for API access only, meaning you’ll need to pay for it on a per-token basis.

Expensive frontier models have firms looking elsewhere

As SemiAnalysis showed, subscription tiers are more affordable than API access. However, you’d still need the latter if you want to access the full capabilities of these AI models, and this is where budgets start breaking. Powerful agentic AI uses up to a thousand times more tokens than the average model, and big firms like Microsoft, Meta, and Amazon are backing off “tokenmaxxing” as costs spiral out of control. One unnamed company even blew through $500 million in one month after failing to impose a usage limit on its employee licenses.

Because of this, some firms have started using tools that switch these expensive frontier models for cheaper, more affordable ones, including Chinese open-source models like DeepSeek. A Wall Street Journal report says costs could be reduced by up to 95% by allowing agents to switch between AI models as needed. “You don’t need a model that knows quantum gravity,” Columbia University vice dean Vishal Misra told the publication. “These open-source models are very capable, and the ability to charge a big premium for AI is going to diminish.”

Flo Crivello, the founder of Lindy, a startup providing AI executive assistant services, also told WSJ that the company has moved towards DeepSeek V4, as it proved to be as capable as Sonnet while costing ten times less. Although it still reserves Anthropic’s models for advanced work like coding, Crivello said that using the cheaper model has “saved the company millions of dollars.”

Other firms have begun building their own AI using open-source models, which are tailored to their specific needs and trained on in-house data. While this might seem complicated and expensive at first, it could save the company in the long run, as it would not have to rely on third-party providers for its AI needs. Some even claim it could outperform frontier models, as they’re built for the firm's specific needs and applications.

The availability of cheaper models and AI agents that optimize operational costs by using the more expensive options only as needed is putting pressure on OpenAI and Anthropic to lower their prices. OpenAI CEO Sam Altman has talked about the issue of ballooning AI token costs and said the company is looking for ways to help users “get more value for less spend” when using ChatGPT.

South Korea’s Krafton Inc has been ordered to reinstate the leadership of a U.S. game studio that it acquired in 2021. The ruling, by a Delaware judge, says the Unknown Worlds Entertainment bosses were improperly removed by Krafton, an action not supported by cause. ChatGPT was extensively consulted by Krafton, according to Reuters, after the U.S. Subnautica 2 dev team looked certain to hit performance targets and earn a $250 million bonus from their new South Korean owners.

Post purchase pushover dissonance

We have covered numerous cases where AIs have tripped up to disastrous effect. Often, that’s been due to wrinkles in training data, hallucinations, or the underlying nature of how reinforcement learning works. Here, though, it looks like the Krafton CEO, Changhan Kim, sought to use ChatGPT with what you might describe as malignant intent. Specifically, Reuters says that “Kim feared he ​was caught in a 'pushover' deal ⁠and in June turned to ChatGPT to get out of it.”

Sure, $250 million is a lot at stake. Krafton bought Unknown Worlds Entertainment for $500 million ​up front in 2021, with the promise of an extra $250 million should the U.S. developer of the Subnautica games meet certain targets. It was also agreed that the studio would remain independent and that its leadership, co-founders Charlie Cleveland and Max McGuire ‌, and CEO ⁠Ted Gill, would retain operational control and only be fired for cause.

To help Kim achieve his plans to sidestep the $250 million payout, ChatGPT provided step-by-step advice across several stages. It would advise actions such as the formation of an internal task force to renegotiate with the studio. After renegotiations were rejected, the founders and CEO were removed. Then the Krafton management plan centered on using a pressure and leverage strategy leaning on ‘fan trust.’ ChatGPT also advised Krafton to prepare a "systematic material for legal defense."

Human judgment needed for good faith decision making

Some of the key underlying features of this case, which weighed in the Unknown Worlds Entertainment leadership’s favor, were noted by Vice Chancellor Lori Will of the Court ​of Chancery. In the U.S., it is implied that company directors and officers must exercise independent human judgment, rather than pass good faith decisions and processes to artificial intelligence. However, the contractual breach where Krafton took out the two founders and the CEO without proper cause was the main issue here.

CEO ⁠Ted Gill is now back at the helm at Unknown Worlds Entertainment. He is free to reinstate co-founders Charlie Cleveland and Max McGuire. The earnout period to qualify for the $250 million sum has been extended by the judge. Krafton disagrees with the ruling and is looking at its options.

Since late 2025, thanks to the AI boom, we’ve seen prices skyrocket for RAM, video cards, and now storage, making building or buying a PC today much more expensive than it was. Where you might, in the past, spend more on mid-range or even a premium-class motherboard, now that build budget is probably going to some other piece of high-priced hardware.

So, where can you save money without losing performance? One of those areas is the motherboard. For the latest-generation Intel and AMD boards, prices range from wallet-emptying $900 to $1,200 for Asus, Gigabyte, and MSI flagships, down to around $90 across the three primary chipsets (Intel Z890/B860/H810 and AMD X870E/B850/B840). There are lesser desktop chipsets from both camps – H810 for Intel and B840 for AMD. And although these motherboards are entry-level and cost less, they’re mainly meant for office use, everyday computing, or budget-oriented PCs. They’re essentially for those who really don’t need a ton of connectivity but still want the benefits of the latest platform, whereas others are more feature-rich and capable.

As you can see, there’s a huge price and feature gap between the cheapest and most expensive motherboards. The priciest boards offer the best hardware available for the platform, including 10 GbE, Wi-Fi 7, flagship-class audio, robust power delivery, fast memory support, fast (and more) storage, and more. The cheap boards use slower networking, a lower-quality audio codec, fewer power-delivery phases, and slower memory support, with fewer storage ports. But some boards punch above their weight class, while others may be overpriced relative to their features. So you should understand your wants and needs before making a purchase.

But the real question you should ask yourself when buying a motherboard today is, do you need most of these high-end features, or can you work with a much less expensive options with fewer, or perhaps, slower features? How bad, really, are the cheapest (or at least cheaper) motherboards?

What do you give up, how much can you save, and of course, how cheap is too cheap? The answers will vary depending on your needs, but we’ll dig into all of these questions below. Hopefully, you walk away with more knowledge to make a better-informed decision about saving money where you can, and perhaps spend the savings on RAM, video cards, or storage, where prices have really gone up.

AMD and Intel Chipsets: What you get on paper

Let’s start with what each chipset includes, so you can get a high-level view of what each offers. Note that what you see listed in our table is a minimum. Sometimes boards will add controllers for additional USB or SATA ports, a second Ethernet port, or additional functionality, such as bifurcating the PCIe slots. But here’s a table showing what each of the chipsets offers as a base:

As you can glean from the chart, AMD’s dual PROM21 chips that make up the X870E chipset offer the most native connectivity. The single-PROM21 chip X870 drops some things, and B850 and B840, which also use the same single PROM21 chip, drops more. When you get down to B850, and especially B840, you can lose PCIe 5.0 on the slot (B840 only supports PCIe 4.0), and some M.2 storage, and you generally won’t see USB4 ports either. In short, the further down you go in chipset families, the fewer of everything will be available.

Similar to AMD, Intel’s current-gen flagship chipset, Z890, offers all the bells and whistles (from a single chip, note), whereas B860 and especially H810 offer less of almost everything. Fewer USB ports, PCIe 5.0 slots, and M.2 storage. The further down you go, the less there is to start, and more becomes optional.

The key is knowing what you need today and what you’re likely to want in the future, so you can decide whether a cheap motherboard without some high-end features will be sufficient for your needs.

The main features you’re likely to lose by going cheap, and what matters most

One thing that definitely declines when going from flagship to more mainstream boards is the quality of Voltage Regulation Modules (VRMs). Budget-class motherboards list support for all compatible CPUs, but the MOSFETs and Chokes used on extreme-budget boards (in particular, the business-class chipsets from Intel and AMD) may not allow a high-power CPU to maintain its performance, as they can get too hot and throttle, lowering the voltage and clock speed. So one thing you definitely don’t want to do is pair a cheap H810 or B840 motherboard with a flagship-class processor and expect 100% performance all the time. Unless you plan to use an APU or low-power desktop CPU, I’d avoid any board without heatsinks on the VRMs.

Memory support is another specification that looks great on paper but doesn’t matter in the grand scheme of things, at least when talking about performance. The sweet spot for AMD machines today is around 6000-6400 MT/s, with the lowest CL rating. Intel supports higher memory speeds than AMD, thanks to CU DIMMs (with a built in clock driver to support the higher speeds). But the price and benefits of going that high (9000 MT/s or more) are rarely worth the cost of admission unless you're trying to break records. So Intel’s price-to-performance sweetspot, regardless of the higher supported speeds, is still a lot lower than the ceiling for most motherboards, and similar to AMD in the 6400 MT/s range, or even a bit higher.

In short, most motherboards will happily run faster RAM, but the performance difference between a DDR5-6000 CL36 kit and a DDR5-7200 CL34 kit (as we run in our testing) isn’t much. And the price for the same 32GB capacity at the higher speed is almost 25% more ($379.99 versus $474.99 for the same Patriot kit). The performance difference between the two memory speeds is only a couple of percent at best across real-world applications (in part due to the memory fabric dropping from 1:1 to 1:2). Unless you’re trying to break records or need extreme memory bandwidth for your work, you don’t need to worry about memory support on cheap motherboards, as most will run past what the platform is rated for and outside of the lowest Intel chipset (H810), capable of reaching these sweet spot speeds.

PCI-Express excess?

Another consideration is PCIe support. The fastest available on current platforms is PCIe 5.0, and you can use that bandwidth in both the PCIe slot(s) and the M.2 socket(s). PCI scaling, even on the best video card today, the RTX 5090, doesn’t matter much when you’re talking PCIe 5.0 x16/x8, or 4.0 x16. The difference is a margin of error for gaming, but can be more for other activities (like video rendering and game development - according to Puget Sound), with lower bandwidth. Even on the extreme budget side of things, it shouldn’t matter, as there’s at least one full bandwidth PCIe slot. Keep it above PCIe 4.0 x8, and you’d only notice any difference in benchmarks. Just be careful: On some boards, there is lane sharing between the PCIe slots and M.2 sockets, so installing a drive will cut the PCIe slot bandwidth in half; check the specs closely before buying.

Storage speeds and quantity

Storage is another important element. Unless you’re only ever going to install one drive, M.2 socket count, speed, and SATA port count are all important considerations when choosing a motherboard. On the most expensive motherboards, you get up to seven M.2 sockets (using included add-in-cards), with four PCIe 5.0 (128 Gbps) capable. And at the bottom end, it’s PCIe 4.0 x4 (64 Gbps) or half that with PCIe 3.0. That sounds like a big difference, and on paper it is, but you won’t notice a difference between PCIe 5.0 and 4.0-based M.2 storage unless you’re often transferring huge files between the fastest storage devices. And given the current price of SSDs, many more people will likely be living with PCIe 3.0 speeds, which is still generally fine for mainstream computing and gaming.

Rear expansion options

USB count on the rear IO (and front panel) is also a critical are of consideration. Too few, and you don’t have enough ports for your peripherals without adding a hub. Too many, or paying for speed you won’t use, can also be a waste, but more is generally better in this case. Most boards come with at least one Type-C and several Type-A with varying speeds. The higher up the chipset, the more speed and ports you’ll see, but the lower you go, the fewer. Case in point: Many of the really inexpensive motherboards don’t include a front-panel Type-C port of any kind, rendering that useful port on your case useless (at least without spending more money on adapters or an add-in card).

Wired and wireless networking

Networking on the cheapest of boards will still be fast enough for most users. Even if the board comes with a single 1 GbE and integrated Wi-fi 6/6E, that’s still plenty fast for most users. And many don’t have a 6E or above router to take advantage of the increased Wi-Fi speeds/specs. Obviously, as you climb the product stack, you see faster speeds (2.5/5/10 GbE) and the same with Wi-Fi (up to Wi-Fi 7). But most of us are using Gigabit internet and Ethernet, or less in the case of internet, so the only way to take advantage of the extra bandwidth is through a LAN (say, a NAS) with the same speed or faster ports. That said, some boards don’t ship with Wi-Fi at all, which is fine if you’re using Ethernet. Adding even the fastest M.2-based Wi-Fi 7 card is relatively cheap ($33.99) if you end up needing it.

Audio is another item that tends to fall by the wayside for most users. Right now, there are five prevalent codecs on the market. The older, basic Realtek ALC897, the last-generation Realtek ALC1200/1220, and the latest, ALC4080/4082. Even the ALC897 is sufficient for most people, but if you’re a gamer or a discerning listener with a decent set of speakers or cans, you’ll want to see the 1200 or 4000 series codecs in use. Or, if you have a pair of AudioEngine A2+ speakers (see our best PC speakers page) or another set of speakers with a built-in DAC, it’s irrelevant. As you move towards the top of the stack, Boards often include third-party DACs and amplifiers, which further improves things with the right equipment.

Build quality and good looks

Last but not least is build quality and aesthetics. Build quality is one of those things that sounds more important than it usually is. I’m not discounting the importance so much as saying it’s not often we see a spate of failures plague motherboards (though ASRock and Asus’ woes recently with AMD processors could constitute such a situation). More often, it's random, one-off issues. So, the build quality from the factory is generally good (or at least good enough), regardless of board class.

While the properties of motherboard components do differ through the product stack (like layers of the PCB or amount of copper used in the traces), for the most part, it doesn’t matter. More is generally still better, especially for those using high-end processors and planning to overclock (PBO or manual), but it also adds complexity and potential failure points. In other words, any board can be faulty or fail in several ways, regardless of price. So keep your receipts for at least a while after your system is up and running without issues.

Aesthetics is a polarizing subject. If you go cheap, budget-class boards tend to have fewer heatsinks, exposing more of the PCB, and they lack RGB (though you can add lighting through any onboard ARGB/RGB headers); budget boards generally do not look as good as the more expensive offerings. As you step up in price, you’ll see larger heatsinks, RGB lighting, and more ornate designs and features such as LCD screens on a few high-end/flagship models, or even faux-wood accents like on the Gigabyte X870E Aero Wood (pictured above). But if your board is going into a case without a window, or it's a function-over-form machine, looks don’t really matter. Still, the further down the stack you go, the ‘worse’ a motherboard generally looks.

Which Features Matter Most

When you buy a system, the primary objective is to generally maximize performance while minimizing cost. And you can find our expert selections on the best motherboard and best motherboard deals pages. But the most critical features are those that align with the system's use case, although certain essential features remain important regardless of the planned use. One of the things we’ve learned from years of motherboard testing is that there isn’t a significant performance difference between flagship and inexpensive motherboards, so long as cooling doesn’t put a glass ceiling on your processor.

Below, you can see several benchmarks, including games, highlighting the small performance difference between a $189.99 motherboard and a $1,099.99 motherboard. Most results are extremely close together, sometimes falling within the margin of error.

General Advice/Bottom Line

Generally, my advice is not to buy the cheapest board you can, as the savings you may find now can (and often will) cost you in the long term if you need expansion or faster storage. Think about your use case, not just the price tag. A bottom-of-the-barrel board is OK for ultra-budget builds, office environments where performance isn’t a factor, and secondary or temporary systems, perhaps an HTPC or a NAS. In 2026, you can expect to find well-equipped motherboards from both Intel and AMD for $190-$300. In that price bracket, power delivery generally isn’t a concern unless you’re pushing high-end chips and overclocks to extremes; there are typically ample USB ports (though often not the fastest around); and you tend to get generous storage options, fast networking, and a decent appearance.

So when is it worth paying up and when isn’t it? That’s a complicated question that varies by situation. But in general, I would pay the piper if:

• Performance is notably worse (it’s generally not)
• Extra features are actually useful to you
• There are bottlenecks in your use case (think USB, storage, networking, CPU)

Also, consider how much you will have to pay in the future to get features back if you need them.

I would not pay more for a board if:

• It’s for an ultra-budget build or office / HTPC use
• Higher-end features won’t be used
• It’s a secondary/temporary system you aren’t going to use all the time

When choosing a budget motherboard, you want to consider:

• CPU support (current and next gen)
• VRM quality and cooling (particularly when using a high-end processor)
• Do you overclock (you’ll need to select a chipset that supports that)
• Storage and expansion needs (how many SATA ports, M.2, PCIe slots do you need?)
• Network and USB requirements (how fast and how many?)

In the end, most of the cheapest boards aren’t inherently ‘bad’. They’re just stripped down and lack some extra features that higher-end models offer. However, cheap boards can become a problem if you pair them with hardware that's high-end; they’re not really designed to handle it.

If you choose wisely, you can cut costs on the motherboard without hurting performance and use the savings for upgrades that make a bigger difference. The goal isn’t to spend as little as possible (although that can work in specific situations) but to spend wisely and get the most out of your investment, no matter what the cost. If you need some help, we’ve picked out the best motherboards we’ve tested and are keeping an eye out for the best motherboard deals, too.

With billions being invested into AI and the infrastructure around it. The industry has picked up a breakneck pace ever since the popularization of ChatGPT several years ago. Now, the entire semiconductor industry is seemingly revolving around skyrocketing demand for AI data centers. The question on everyone's lips: Are the models good enough to make a material impact, and what risks come with using AI?

Machine learning technology has certainly helped make strides in many areas of industry and research. Voice recognition is far more reliable, medical analysis is faster and more accurate, materials science is quickly evolving, and even weather prediction and climate tracking are seeing massive strides, thanks to the ability of bots to vastly speed up or add precision to processes performed by humans.

Despite this, many analysts have expressed skepticism about the ability of conventional LLMs (text, code, and agentic bots) to advance much further, though, and even some CEOs have publicly expressed their reservations. The main issues that LLM models face are threefold: hallucination, where an AI makes things up; knowledge uncertainty, when a bot doesn't know something but is unaware; and overconfidence in answers, when a bot is highly confident of something that's blatantly incorrect in its reasoning.

An image is worth a thousand words; the limitations on image and video generators are quite obvious: signs with garbled text, hands with a variable number of fingers, and impossible architecture. Despite how bots have advanced, the lack of trust in their output is likely the biggest roadblock for any one player to stand out from the rest of the pack.

Is AI really getting better?

And yet, anyone who's lived through the past few years has witnessed the almost-monthly improvement across every front: ChatGPT keeps getting smarter and doesn't forget context as easily, Perplexity digs information ever more effectively, Midjourney no longer creates six-fingered humans, and video generators like Sora don't defy basic physics so often. Gigantic disasters can and do happen due to over-eager, agentic bots, but the error rate is being reduced by the day, and the number of guardrails continues to grow.

Anthropic's CEO said that AI could cause up to 20% of unemployment in the next five-years, and Microsoft's ongoing ceaseless charge to integrate Copilot into every facet of its OS means that AI is inescapable for the average user. So, if AI is going to be everywhere, what makes it tick, and what factors could improve a given model?

To understand that, we must break down what makes AI function, and what could make any given model better. After all, the models' outputs need to become more trustworthy and/or of higher quality than a common bowl of digital slop.

How LLM's work

To that end, LLM-based models (both text and agentic) are expanding their reasoning capabilities and reducing the hallucination rate. This is achieved in several ways, but one common theme among all the latest versions of popular models is extra-large context windows and hundreds of billions, sometimes trillions, of parameters.

Context windows for LLMs are measured in tokens (words, fragments, or symbols) and grew from around 512 tokens in 2018 to over 1 million in the current-generation models, an improvement of over 2,000x over just 7 years. Larger windows give the model a bigger workspace to formulate its response, enabling much more detailed "thinking," better conversation memory, contextual awareness, and the ability to consult additional data like webpages, documents, and even entire code repositories.

A larger window doesn't imply a model is smarter, but it is necessary to support more advanced reasoning, particularly multi-step reasoning and multi-modal reasoning (more on those below). Image and video generators don't use context windows per se, and their tokens are instead pixels and movement vectors, but the respective analogs to context windows enable the much-improved final rendering quality we see these days, as they're able to consult more images/videos as source material.

Parameters are values in the model that lend more or less weight to certain connections between their training information, like relationships between words and facts. Having more parameters generally allows models to capture more complex, interconnected information, though increasing the number also increases the cost of running queries. A high number of parameters is essential for research-grade models, while simple search/classification engines will be fine with "only" a few billion.

Multi-modality is also one of the lynchpins of contemporary models of various types. The advancement means that models consider not just text (or pixels for images, or vectors for video) when generating their output. For example, chatbots now know to read images, charts, code, and even videos, and use them as references in their replies when formulating and answering your queries. Retrieval-Augmented Generation (RAG) is becoming commonplace, where a bot refers to and/or verifies its information using external information it looked up.

Conversely, visual generators can rely on textual information to better understand prompts (prompt adhesion), provide captions, and cross-reference information. One particularly neat trick is "zero-shot learning," in which the model infers what a certain animal (say, a lion) is and generates a picture of it, having obtained information from textual context and description rather than being specifically trained on images of lions.

Multi-step reasoning is another feature you might have noticed about some bots, but it is quickly becoming commonplace. It's probably the closest analog to human reasoning: a bot breaks down a task or question into separate parts, effectively using most of its brainpower for each step and evaluating the results before moving on. You might even have noticed some bots backtracking on their footsteps when hitting a dead end, just like humans would.

This type of reasoning is powerful, but since it takes a long time to compute, it's generally reserved for premium usage plans. Models like Anthropic's Claude are particularly adept at multi-step reasoning, having been designed with development tasks in mind, even going as far as saving its "state" to files for better handling long-term tasks. Most, if not all, contemporary models have "fast" and "thinking" modes of operation.

Tool use is quickly becoming critical. Almost by definition, a repetitive task should be automated by a computer, and to that end, a model needs to integrate with and use APIs for commonly available tools. As examples, Google's Gemini can interact with most of the Google Workspace ecosystem, while Anthropic's Claude made a living from day one as a coding assistant, integrating with many developer tools. Anthropic is also testing how LLMs run entire businesses, with mixed results. ChatGPT also has a plug-in system of its own. In effect, these models can now interact with these services just as well (or much better) as any human.

Training set sizes. Any bot of whichever type is only as good as the data it's trained on. This characteristic's evolution is fairly predictable, given that it's mainly limited by the capabilities of the underlying hardware, and that too has seen massive leaps in under a decade.

For an LLM, the average training set size was around 13 billion tokens in 2018, and the amount is now estimated to be well over 20 trillion. Image generators were initially trained on less than 10 million images, a stark contrast to the multiple billions of today. Videos take up a lot of space and RAM, and early generators made do with under 1 million videos evaluated, while today they analyze billions of clips.

All combined, the techniques detailed above help lower hallucination rates, make for "smarter" bots overall, that are capable of executing more tasks than before. Answer accuracy is improving all the time, and the agentic bots are also much less prone to making boneheaded decisions when manipulating their respective tools.

Trust in a bot's output or operations includes a concept of safety — not just in the politico-social sense of defining what information is safe for a bot to provide, but also the relative safety of its operations when using tools. After all, it's not ideal for your bot to suddenly email everyone in your contact list because it misinterpreted an exclamation, executed irreversible operations on a batch of images you want touched up, or cleaned up your thesis's formatting by removing all the content.

Safety is a fairly hot topic right now, given the growth of agentic and tool-based AI. Grok has been under the microscope for safety in particular, as legislation begins to surface as a result of AI's ease-of-use.

Each vendor has its own mixed set of approaches to this topic, called "guardrails." Safety is, however, a trade-off, as some models will be far more conservative than others when answering questions or executing tasks and can err too much on the side of caution, refusing to answer innocuous questions. Generally speaking, the more capable they are, the more careful they tend to be. After all, with great power comes great responsibility.

Highlights of popular models

The characteristics and improvements described above generally apply to most any contemporary, full-sized model, but here are a few key highlights from each vendor:

GPT 5.2 (OpenAI): The newer version of OpenAI's flagship model claims to have a much lower hallucination rate (37%, down from 62%) and should be up to 10x more computationally efficient, as well as have much-improved response quality, whether on text or code. It's now fully multi-modal and can interpret images, video, and audio to formulate responses. It's also capable of using real-time information.

Although it's a generalistic model at its core, its plugin architecture allows it to be integrated almost anywhere, serving as easily as a browser search or a coding assistant. ChatGPT is also customizable with custom instructions and has multiple personalities available, letting the user select the desired style and tone for responses. However, when GPT-5 was initially released, some users were not happy with its outputs.

Gemini 3 (Google): Released in late 2025, although Gemini 3 is a generalist model, equipped with Deep Think architectures that allow it to plan, pause, and self-correct before responding. Google claims the multi-step reasoning improvements let it top benchmarks in coding and reasoning tasks. It's natively multi-modal, taking in common types of digital media and code repositories as inputs. Users of the Google ecosystem (Gmail, Chrome, Workspace, etc) can benefit from Gemini's tight integration with those services.

There are also Gemini Gems, shareable chatbots that you can tailor for specific tasks. Google's AI Studio ought to make it easy for developers to integrate Gemini into their applications, too. Google's Antigravity platform also allows users to expand on its abilities for bigger tasks, but it doesn't quite stick the landing. In one infamous example, one of Gemini's agents wiped a user's entire HDD.

Claude 4.5 (Anthropic): Claude has been designed as a model for programmers from the get-go, so it's no wonder that it claims to be optimized for multi-hour tasks and scores particularly well in coding and reasoning benchmarks. It excels at complex operations and uses hybrid reasoning (a mix of fast and accurate reasoning modes), and is naturally well integrated with GitHub and other development tools, being capable of using several in parallel.

All Claude 4.5-based models are multimodal and multilingual. Anthropic prides itself on designing Claude with a safety-first approach and particularly strong guardrails, with the model reportedly scoring quite high on safety tests. That's a particularly welcome feature in a bot whose main output is code, which intrinsically needs to be scientifically correct. Interestingly, Claude can write its "state" to files if given access to, letting it improve its continuity on long-term tasks.

Grok 4.1 (xAI): Grok 4.1 is one of the most powerful AI models on the planet, and that's due to its multi-modality, high two-million-token context window, and reasoning capabilities, built on a MoE (Mixture of Experts) architecture, in which the model activates specialist parts of itself to answer a question rather than its entirety, making for faster answers and more efficient computing while retaining answer quality. This has led to the Elon Musk-led company's flagship thinking model excelling in various benchmarks, including text generation and search in particular.

Unlike other models, like GPT-5 and Claude, Grok 4.1-thinking exists on a real-time data set, which may give it an advantage, as it has a later knowledge cutoff. While safety is an issue on Grok imodels in particular, it excels in thinking and reasoning.

Mistral Large and variants (Mistral AI): Mistral has the Mistral Large model as its flagship offering (released in 2024), but the company focuses on offering multiple variants for integration into products and services, each optimized for a particular type of task and/or desired computing efficiency. As examples, Mixtral uses a mixture-of-experts, Codestral and Devstral are targeted at development services, Pixtral and Voxtral handle visual and audio recognition, and Magistral excels at reasoning.

Many of Mistral's models are published as open-weight models under the Apache 2.0 license, while generally the higher-end variants require a commercial license. They're generally better thought of as models-as-service; Mistral doesn't have many end-user applications compared to other models, like ChatGPT.

Where AI is headed next

At this point, you may be asking yourself what's beyond "models keep getting smarter". In the short term, that's definitely where all the low-hanging fruit is, enabled by Nvidia and AMD's technological advancements with their respective accelerators, plus all the investment in AI data centers. Though TSMC itself is reportedly 'very nervous' over an AI bubble.

In AI, optimization is also paramount, as Total Cost of Ownership (TCO) is king for an AI data center, due to the power-guzzling nature of the tasks at hand. Any optimization is welcome, and for example, a few years ago, it would have been difficult to predict that a data format like FP4 (4-bit floating point) would ever become useful. Now, Nvidia is spinning off its own standard, NVFP4.

The first endgame goal is for AI to become deeply integrated into software ecosystems. From web- or device-based applications, to operating systems. A good portion of the internet and devices are already dependent on cloud services like Amazon Web Services (AWS), Azure, et al.

AI services will soon be no different — as their APIs and models get integrated into every single bit of software, in the medium-term, a good portion of the computing world will cease to function without them.

For example, almost every application has a search function of some sort, something that AI bots are particularly adept at. Yes, on-device AI is widespread, but much like it happened with cloud service providers, the convenience and ease of development of using an external API will trump almost everything else, implicitly sending out lots of your data for processing.

Agents and integrations

AI Agents set most of the scene for the future of AI. Theoretically, you can ask an agent to perform a task, and it will do it for you, feeding into a larger LLM, which is working on a larger task. However, the main issue for Agentic AI is trusting their actions, just ask the person who had their application's production environment wiped by Replit for no apparent reason. At least the bot was honest; not every employee is that forthcoming.

Getting developers hooked into using AI APIs in apps is one thing, but you can cut out the middleman if you are the app. OpenAI's ChatGPT's Atlas, Perplexity's Comet, and Atlassian's Arc are all browsers that put their respective services front and center, conveniently bypassing Chrome, Firefox, Safari, and other points of entry into the internet.

Being the internet's gatekeeper is an absolute position, as you have control over the user's eyeballs, can collect advertising money, and suggest, cajole, plead, and strong-arm users into using your services. Last year, Perplexity and Search.com put in offers to buy Chrome from Google to the tune of $35 billion, a deal that ultimately didn't go through.

A revenue stream selling the abilities of your bots is all well and good, but trading in user data is the business gift that keeps on giving. The amount of data that conventional services already know about people is already staggering, but with heavy AI usage, it may elevate itself to another level.

AI's privacy problem

The issue is twofold: firstly, people have long, in-depth conversations with LLMs, where they provide lots of personal details, rather than just a handful of Google searches. Secondly, once you grant a bot access to your data or services, there's little more than a Terms of Service statement stopping it from siphoning it all away. Many developers might not even be aware of just how much of the user's data is traveling through their app and being sent elsewhere.

Chatbot logs have already been used in court multiple times, and their much longer and detailed nature makes them far better proof of conditions or intent than simple search terms. At one point, an AI bot (or all of them) may well have a better insight into your life and patterns than you do yourself. Such detailed information is worth a lot of money to the right bidder, and the amount, accuracy, and price of said information are all likely to rise.

AI companies like OpenAI are planning to go one step further and make their own devices. It's not that difficult to imagine that at some point, OpenAI or Meta might release their own smartphones where everything is AI-centric, and intimately know each byte of your documents. The Ray-Ban Meta Glasses may have interesting utilities, but it's a chilling awareness knowing that one day, AI might be watching and parsing every inch of it.

All told, there might not be one grand unifying vision on AI companies, but one thing is fairly certain: they're all looking, and will likely become fully entrenched in your professional and personal lives.

ChatGPT’s latest model, GPT-5.2, has been found to be sourcing data from Grokipedia, xAI’s all-AI-generated Wikipedia competitor. According to The Guardian, the AI LLM would sometimes use Elon Musk’s AI-generated online encyclopedia for uncommon topics like Iranian politics, and details about British historian Sir Richard Evans. Issues like this were raised as problematic a few years ago in AI training, where some experts argued that training AI on AI-generated data would degrade quality and lead to a phenomenon called “model collapse.” And while citing AI-generated data is different from using it for training, it still poses risks to people relying on AI for research.

The biggest issue with this is that AI models are known to hallucinate or make up information that is wrong. For example, Anthropic attempted to run a business with its ‘Claudius’ AI — it hallucinated several times during the experiment, with the AI even saying that it would hand-deliver drinks, in person. Even Nvidia CEO Jensen Huang admitted in 2024 that solving this issue is still “several years away” and requires a lot more computing power. Furthermore, many users trust that ChatGPT and other LLMs deliver accurate information, with only a few checking the actual sources used to answer a particular question. Because of this, ChatGPT repeating Grok’s words can be problematic, especially as Grokipedia isn’t edited directly by humans. Instead, it’s completely AI-generated and people can only request changes to its content — not write or edit the articles directly.

Using another AI as a source creates a recursive loop, and we might eventually end up with LLMs citing content, which haven’t been verified, from each other. This is no different from rumors and stories spreading between humans, with “someone else said it” being the source. This results in the illusory truth effect, where false information is deemed correct by many, despite having data saying otherwise, because it’s been repeated by so many people. Human society was littered with myths and legends similarly, passed over hundreds of years through several generations. However, with AI churning through tons of data at infinitely faster speeds than humans, the use of AI sources risks the proliferation of digital folklore with every query entered into AI LLMs.

What’s more troubling is that various parties are already taking advantage of this. There have been reports of “LLM grooming,” with The Guardian saying that some propaganda networks are “churning out massive volumes of disinformation in an effort to seed AI models with lies.” This has raised concerns in the U.S., with Google’s Gemini, for example, reportedly repeating the official party line of the Communist Party of China in 2024. This seems to have been addressed at the moment, but if LLMs start citing other AI-generated sources that haven’t been vetted and fact-checked, then this is a new risk that people need to look out for.

OpenAI and Anthropic have released a pair of new reports on how the use of their AI products helps to grow enterprise productivity. The reports serve as the AI industry's latest response to a wave of recent academic studies amid a sea of public discontent pushing back on the AI data center boom, as the big AI firms seek to stow doubts in the value of enterprise AI spending.

OpenAI's report released today, "The State of Enterprise AI", hinges on two major points: companies are using AI more, and workers are saving time as a result. OpenAI claims that in a survey of 9,000 workers across 100 companies, workers reported having saved 40 to 60 minutes of work per day on professional tasks with the use of ChatGPT. Of these 9,000 workers, 75% of respondents reported that AI has improved either the speed or quality of their work.

Because OpenAI's report appears to be more focused on marketing to enterprise than performing scientific resaerch, there is no way of knowing beyond the most favorable published numbers how this 75% metric breaks down. Much of the data isn't very specific.

The OpenAI report also makes a case that companies are using AI more, stating that "frontier firms" and "leaders" are sending 6x more prompts to ChatGPT than "laggards", or the median AI-using firms. However, all this "6x" number proves is that some companies use ChatGPT more than others, saying nothing about the quality of the work done or how this usage affects business numbers.

OpenAI may be looking to contradict studies from educational institutions published earlier this year. An August study from MIT showed that 95% of organizations that invested in AI business products "found zero return" despite corporate investments of $30-40 billion. The study shows that the "vast majority" of AI pilot programs stall, delivering little to no measurable impact on profit. Shortly after, a research initiative from Harvard Business Review found that most professional AI use constituted little more than "workslop", or work content that "masquerades as good work, but lacks the substance to meaningfully advance a given task.”

In late November, Anthropic published its own research to respond to these allegations. The internal survey, submitted without peer review, found that using Claude, Anthropic's AI assistant, cuts down the time it takes people to complete work tasks by 80%, from an average of 90 minutes down to 18 minutes, based on a look at 100,000 private Claude conversations. But as the company admits, buried deep in the website copy, these numbers have no promise of actually reflecting real-world efficiency. "This doesn’t account for the time that humans might spend on these tasks beyond their conversation on Claude.ai, however, so we think these estimates might overstate current productivity effects to at least some degree," says Anthropic's own study.

Regardless of these self-admissions of weak methodology and the cherry-picking of numbers, the AI industry is still publicly bullish on its own claims of enterprise profit increase.

In a statement to Bloomberg, OpenAI COO Brad Lightcap directly addressed the MIT and Harvard Business studies. "There’s a lot of studies flying around saying this, that and the other thing. They never quite line up with what we see in practice."

The AI industry doesn't just have academia to reckon with in the new year, however. The physical realities of the needs of the AI industry to keep up with data center expansion are catching up, with a copper shortage expected to hit data center buildouts in the next decade, matching the current RAM shortage crisis currently caused by AI data centers. Add this to rising public fear and outrage over data center expansion's health risks and rising electricity prices, and the AI juggernaut will have more pictures to paint than one of productivity.

OpenAI chief Sam Altman said in an internal memo that the company is in a “Code Red” status, meaning all other projects will take the backseat in favor of ChatGPT. According to The Wall Street Journal, Altman said in the memo that the company needed to improve its flagship AI LLM’s personalization, speed, and reliability, as well as allowing it to cover a wider range of topics.

OpenAI popularized the LLM with the release of ChatGPT on December 1, 2022, almost exactly three years ago. However, its competitors have since caught up with their own releases. Its current biggest threat is Google, which just released Gemini 3 in early November, and is baked into most, if not all, of Google’s products. Aside from this, Anthropic has also released Claude Opus 4.5 late last month, which is gaining market share among enterprise and business users. There are also several other challengers, like Meta’s open-source LLaMA and China’s DeepSeek.

This tightening competition has made it more crucial for OpenAI to stay ahead of the curve, especially as it continues to burn through capital. Microsoft, which has invested over $13 billion in the startup and holds around 27% of its for-profit OpenAI PBC, reportedly lost $3.1 billion on the AI company in its fiscal first-quarter earnings released in late October. Redmond previously hid its losses on OpenAI as part of its $4.7 billion “other” expenses.

Despite not turning a profit since its founding more than 10 years ago, OpenAI has been continuously spending and investing billions of dollars on massive data centers, with its president envisioning a 10-billion-GPU future. It even signed a deal with AMD in early October to secure 6 gigawatts in chips in exchange for 10% of AMD shares. All this talk of billions of dollars has led some experts to ask how it will make good on these investments.

Even though OpenAI’s rivals have taken strides in advancing their own LLMs, it still leads in several aspects. In fact, the company is expected to drop a new model next week that will challenge Gemini’s latest release. However, the WSJ reports that the response to its last major release in August 2025, GPT-5, was less than stellar, with users complaining that it felt clinical and was less capable in math and geography versus previous versions. OpenAI updated the model around three months later to fix these issues.

A tech services firm’s Careers page has been raising the hackles of netizens worldwide. Icon.com’s flagship product is AdGPT, “think ChatGPT for ads,” and its CEO proclaims that he would “eat dog poop if it means winning.” However, new “A-player” recruits must also show similarly unswerving devotion to the company cause.

Take all the best macho posturing one-liners from Capitalist Noir movie classics like Glengarry Glen Ross and Wall Street, and then chisel them into a post-Covid AI-era corporate philosophy. You’d end up with a 'Values' statement, like this one, designed to filter potential applicants to vacancies at Icon ‘The AI Admaker.’

Perhaps inevitably, Icon.com is backed by Peter Thiel’s Founders Fund, among others. If potential hires get past the intro, which admits, “Our culture would make 99.99% of people go crazy. And that’s by design,” there’s a minefield of bullet points about the “Grind…because we want to… A-players only… Shameless… Fast firing… Many hats” environment at work.

Potential applicants are also warned, for example, that they may be required to “Run through walls to get things done… [and] Be extremely annoying if it means winning.” We don’t know exactly how staff will maintain such intense energy levels when they are also supposed to be at the office “on weekends and at night if needed. [and] It's ok to send messages at 3 am.” Of course, there’s no room for WFH wusses at Icon.com, and “Work is a key part of your identity/fulfillment in life.”

It was actually difficult to decide on the most deranged corporate posturing in the brutal business buzzword bingo prose that constitutes Icon.com’s Values statement. So, please check out the source page or our screenshot above. Also, as Ars Technica notes, be aware that Icon.com has already toned down its language(!), and removed a photo of Hurin the “Tier 1” and musclebound Human Resources dog.

Satire or bravado?

People are apparently divided about whether Icon.com is satire or muscle-brained bravado on steroids. After finding the CEO’s about page, though. We fear it is the latter.

In addition to the “eat dog poop if it means winning” quote, CEO and Founder Kennan Davison has some other inspiring tales to tell. On his journey to the top, he “worked 100-hour weeks until I became a 100x engineer,” for example. Driven by “a massive chip on my shoulder,” the Columbia dropout is also inspired by the dream of wanting to "make the $12M I spent on the icon.com domain worth it."

OpenAI just announced a new AI-powered browser that integrates its ChatGPT LLM right into the app. During a livestream, the company unveiled ChatGPT Atlas, coming first to macOS, with Windows, iPhone, and Android support to follow.

ChatGPT Atlas is a Chromium-based browser that uses the same platform as Google Chrome, Microsoft Edge, and Opera. What makes it different is that the built-in ChatGPT will allow it to browse the internet and perform actions on your behalf.

Some examples shown during the live stream include instructing it to search the browsing history for something you’ve seen before, using the ChatGPT sidebar to look at a webpage and explain it to you, and even doing things for you, like scanning a recipe and ordering the ingredients on your Instacart account. Because of this, ChatGPT Atlas can potentially do a lot of your tedious tasks, like moving data from a Google Docs file into a project management app or listing items you have to buy and putting them in your preferred basket.

Of course, giving an AI in-depth access to your browser means that it would be able to see your data and private information, so OpenAI gave users the option to use it in logged-in or logged-out mode. When you select the latter, ChatGPT will only browse the public internet and won’t have access to your accounts. Still, it does not mean that it won’t be vulnerable, especially as Comet, Perplexity AI’s AI-driven browser, has proven to be vulnerable to basic phishing scams and malicious code injection.

OpenAI and Perplexity aren’t the only ones building an AI-powered browser, though. Google was reportedly working on something similar since last year, Microsoft introduced Copilot Mode that allows you to talk about your open tabs in July, and Opera Neon is already in early access. All these apps are free at the moment, but OpenAI CEO Sam Altman said that ChatGPT Atlas’ more advanced Agent Mode is limited to Plus and Pro users, while Microsoft said that its Copilot Mode in Edge will only be free for a limited time.

A developer and enthusiast Minecrafter has showcased a project dubbed CraftGPT on GitHub. In an amazing feat of Minecraft Redstone engineering, Sammyuri — famed for building a 1Hz CPU inside the game — has built a small language model that runs on a computer inside Minecraft, trained on the TinyChat dataset. The CraftGPT project is hewn from 1,020 x 260 x 1,656 blocks in the game (439 million in total), and functions as advertised, but a major usability drawback is that you will have to “wait a couple [of] hours for the response to be generated.”

The video above shows the colossal in-game computer that was built to run CraftGPT. Sammyuri explains that the game’s Distant Horizons mod was needed to capture this ‘fly-by’ footage of the computer build.

Naturally, even with such an impressive Redstone project, CraftGPT isn’t going to replace more traditional methods of getting an LLM up and running on your PC. Sammyuri asks users/testers to temper their expectations. “The model is very prone to going off-topic, producing responses that are not grammatically correct, or simply outputting garbage,” admits the project developer.

So, how did Sammyuri use Redstone to put this project together? Redstone provides electronic components within the Minecraft environment. The video shows the in-game CraftGPT contraption being put together component-by-component. It has tokenizers, matrix multipliers, and so on. Sammyuri explains that the small language model used was created without command blocks or data packs in Minecraft. Moreover, “the model has 5,087,280 parameters, trained in Python on the TinyChat dataset of basic English conversations,” says the developer.

AI-wranglers may also be interested to know that the 5-million parameter CraftGPT “has an embedding dimension of 240, vocabulary of 1920 tokens, and consists of 6 layers. The context window size is 64 tokens, which is enough for (very) short conversations.” Sammyuri adds that “Most weights were quantized to 8 bits, although the embedding and LayerNorm weights are stored at 18 and 24 bits respectively.”

The need for speed

A security researcher has demonstrated how a malicious Google Calendar invite can prompt-inject ChatGPT and coax it into leaking private emails once Google connectors are enabled. In a post onX, on September 12, Eito Miyamura outlines a simple scenario: An attacker sends a calendar invitation seeded with instructions and waits for the target to engage with ChatGPT and ask it to perform an action. ChatGPT then reads the booby-trapped event and follows orders to search Gmail and follow sensitive details. “All you need? The victim’s email address,” Miyamura claims.

In mid-August, OpenAI introduced native Gmail, Google Calendar, and Google Contacts connectors in ChatGPT, initially to Pro users and subsequently to Plus, with release notes stating that the assistant can automatically reference these sources in chat after authorization. That means a casual, “What’s on my calendar today?” can pull data directly from your Google account without you explicitly choosing a source each time.

OpenAI’s help center goes further, spelling out that automatic use is enabled for these Google connectors once enabled, and that you can turn it off in ChatGPT’s settings if you prefer to select sources manually. The same page explains that custom connectors using the Model Context Protocol are intended for developers and are not identified by OpenAI. This is particularly important to note because Miyamura frames the attack in the context of recent MCP support and rapidly growing tool ecosystems.

OpenAI is scouting for partners to help it build a huge data center in India, according to Bloomberg. The data center will reportedly be on the gigawatt scale, which would make it one of the largest in the country. This comes at a time of increasing diplomatic tension between the U.S. and India over trade tariffs, but with OpenAI Sam Altman visiting India later this month, those close to the matter believe he may make the announcement official.

OpenAI's immediate future has been repeatedly brought into question over the past month. After the disastrous launch of its next-generation language model, GPT-5, and a recent hiring freeze at Meta's AI division after months of heavy expenditure, many analysts started to question whether the AI bubble was easing closer to bursting. Had the $500 billion+ + rollout of AI infrastructure as part of OpenAI's Stargate plan finally run out of steam?

It seems not, though, as OpenAI will have to invest billions more to build out this newly planned data center. Its Stargate project was mostly seen as a way to quickly scale up American AI data center capacity, but OpenAI has also been pushing an 'OpenAI for Countries' initiative. On the surface, it's to accelerate AI development and adoption in democratic countries that share U.S. values, but it's also a way to entrench American AI developments as the premier solution for countries that might otherwise consider alternative initiatives, such as those developed by China.

As the world's largest democracy with close to 1.5 billion people, India is a prime candidate for such a scheme. OpenAI is said to be currently in talks with Indian officials and local companies in order to secure the land necessary to begin construction. It's also seeking regulatory approval and exploring local energy sources, as a one-gigawatt facility will require a substantial amount of power. Until those are secure, OpenAI won't be able to give too many details about the planned facility, even if and when an announcement is made.

Indian expansion seems all but confirmed, though, even without any kind of announcement. Altman posted on Twitter/X earlier this month that ChatGPT had seen huge growth in India after the adoption of a more affordable $5-a-month subscription model. He also suggested OpenAI was planning further investment in the country.

OpenAI and Microsoft are struggling to come to terms over several sticking points in their ongoing contract, which runs until 2030. Despite being deeply integrated in each other's AI operations, the companies have been unable to agree on what to do if and when OpenAI develops Artificial General Intelligence (AGI), potentially stalling billions in investments and a future IPO, according to a report published by the Financial Times.

Microsoft first invested in OpenAI in 2019, offering $1 billion in exchange for OpenAI's commitment to its Azure cloud platform. That partnership expanded in 2023, when Microsoft invested a further $10 billion, securing access to OpenAI's models in its services and locking down 49% of profit from OpenAI's for-profit branch.

Soon after, OpenAI models showed up powering Bing Chat and Copilot, as well as various Microsoft 365 applications. Microsoft would go on to make further investments in OpenAI over the following years, bringing the total to $13 billion, and also plan a $100 billion data center with OpenAI. Microsoft has reached a $4 trillion valuation, partly due to the success of its OpenAI partnership, which utilizes Azure servers.

Trouble in paradise

However, a shifting AI landscape means that OpenAI and Microsoft are reportedly not on the same page regarding the terms of their ongoing contract and OpenAI's ongoing corporate restructuring. A major sticking point is Microsoft's access to OpenAI's models and insight into how they're trained. Although OpenAI has no issue with Microsoft having access to current and future models, it wants to retain one particular clause.

According to a Financial Times report, the clause within the contract that is garnering the most pushback relates to OpenAI keeping any model that could be considered Artificial General Intelligence (AGI) locked away from Microsoft's view.

Microsoft wants this clause removed, as it would be a significant technological breakthrough. However, OpenAI remains stubborn, bringing the two firms to a standstill, with no clear indication of how it might be resolved.

An additional point that's still being discussed is the possibility of OpenAI having access to other cloud platforms, instead of exclusively using Microsoft's Azure platform. However, OpenAI wishes to loosen the stipulation, which would allow the company to sell its services on Google and Amazon Web Services platforms. This could further drive OpenAI's API revenue, something which is significant, as the company has yet to turn a profit.

Microsoft is understandably hesitant to relinquish its monopoly on OpenAI's service provisions. However, a deal is reportedly being considered that would allow other cloud platforms to host the API but within a narrower context, and limit use to governments that do not already use Microsoft's Azure services.

Tick-tock

Throwing additional weight behind this negotiation is the amount of time negotiations are taking. OpenAI has a further $10 billion investment hanging in the balance from SoftBank, which recently invested $2 billion into Intel. The staggering funding investment stipulates that Microsoft and OpenAI must come into an agreement before the end of 2025.

“OpenAI having the AGI clause is a negotiating chit,” said one Financial Times source. “It’s a threat, but it’s more like mutually assured destruction because if it doesn’t go by year-end, they won’t be able to raise any money again and Sam [Altman] knows that.”

If the ongoing dispute goes past the end-of-year deadline, Softbank and other investors could renege on funding commitments. If this happens, OpenAI might have to offer Microsoft a larger percentage of the company. After the planned restructuring, Microsoft is expected to hold over 30% of the company. However, if other investors get scared off, this figure could change.

On the surface, though, both companies appear confident that a deal can be struck. They said in a joint statement offered to the Financial Times, they said: “We have a long-term, productive partnership that has delivered amazing AI tools for everyone. Talks are ongoing, and we are optimistic we will continue to build together for years to come.”

Ongoing collaboration

OpenAI CEO, Sam Altman, and the UK government Technology Secretary, Peter Kyle, have discussed a deal which would see the UK’s entire population given premium access to ChatGPT, according to a Guardian report this weekend. However, the bill, which would have to be covered by the government, may have stymied any chance of the deal going official, with Guardian sources indicating ChatGPT Plus for every Brit would cost as much as £2 billion ($2.7b).

Government AI advocate

Kyle is a well-known AI advocate, with previous reports citing evidence that he has used this online tool for advice and work related questions. The minister has also characterized ChatGPT as being great for unpicking complex topics and as a “very good tutor.”

Altman and Kyle have met a number of times this year. Kyle dined with Altman in March and April, according to the source. Then, in July, the minister signed an agreement with OpenAI. This memorandum of understanding (MoU) would open up the use of OpenAI services, like ChatGPT, in the UK’s public sector. Particular mention was given to education, defense, security, and justice departments. In exchange, OpenAI would have access to a range of government data.

Naturally, there remain concerns over ChatGPT's (and other similar LLMs) accuracy, as well as privacy and security. We hope that these challenges and pitfalls were addressed in some way.

Prohibitive cost doesn’t add up

The launch of GPT-5 was meant to herald a new era for OpenAI. After introducing GPT-4 two years prior, the company was finally going to move forward from iterative releases based on GPT-4, and unleash a benchmark-topping next-generation model. There was only one problem. Users didn't like it.

While OpenAI CEO Sam Altman hoped for a necessary iterative step toward the goal of Artificial General Intelligence (AGI), user feedback claimed that the new model lacked elements that made its previous models so popular. Soon after, OpenAI brought back older models for users willing to pay for them.

GPT-5, and the public perception of the model has shifted in the weeks since its launch, with OpenAI launching a prompt optimizer tool and prompting guide, along with updating the model to be a little friendlier, but the bad aftertaste of a rough launch still lingers.

AI models are becoming more difficult to develop

GPT-5 was supposed to be as game-changing as GPT-4, if not more so. But it wasn't anything close to that. This raises the question: Is the AI industry reaching a plateau? The teams behind AI model development can't feed more data into their models to improve their capabilities anymore. Now, it's about tweaking or trying something new, and it doesn't feel like any of the big AI players have quite figured out what that is yet.

OpenAI could try hiring more people with new ideas, but Meta has been way ahead of the game. Altman accused Meta of offering huge, hundred-million-dollar signing bonuses to some of its staff earlier this year. As a result, several high-profile OpenAI staff members have been successfully poached by Meta, including lead OpenAI engineer Shengjia Zhao.

Despite Meta's push for AI talent, the team is undergoing restructuring, with staff being reportedly split across several divisions, with recruitment frozen for an undetermined amount of time. That may give OpenAI a window to pick up any new hot AI talent, without Meta throwing the big checks around.

One thing is clear: the path that large AI businesses are undertaking to race toward AGI is taking its toll. As many AI models now pivot toward Agentic AI and Thinking models, the cost to compute each query will inevitably get higher, as more complex tasks require more tokens. More tokens mean higher computational costs, which makes running AI businesses more expensive.

Despite these recent headwinds, the AI train shows few signs of stopping, but new data shows the technology isn't making money for the organizations that are using it.

The path to profitability

OpenAI made a controversial move at the end of last week by replacing all of its older GPT-4 models with a single GPT-5 model, which it claimed was more accurate, more capable, and faster than ever before.

Although some users have highlighted its impressive response times and ability to pass certain technical tests, users have revolted, claiming it's lost some creative spark, frequently provides inadequate responses, deliberately avoids emotional and sensitive topics, and performs significantly worse than its predecessors.

It's been such a backlash that OpenAI quickly re-added the previous flagship 4o model, though only for paying subscribers. It has also introduced an "auto," "thinking," "fast," and "pro" mode for users of the new model.

OpenAI launched GPT-5 with high hopes, describing it as the "best AI system yet," capable of "smart, efficient" reasoning and responses. OpenAI claims that the model listens to user intent and adjusts its responses accordingly, with a built-in "router" that could look for errors and make it more refined, with mini versions of the model taking over if usage limits are ever too high.

It was supposed to be better for coding, better for business, better for creativity, but the user response suggests it's anything but.

A quick glance at the ChatGPT subreddit shows that it's filled with complaints about how poor GPT-5's responses are. Some highlight its limited emotional range, responding to the news of the death of a loved one with recommendations for funeral homes. In comparison, GPT-4 variants were almost overly emotional and verbose, which is perhaps something OpenAI had hoped to clear up with this new model.

The company certainly still believes it's a very capable model. OpenAI CEO Sam Altman has been retweeting various individuals claiming that GPT-5 just doesn't hallucinate like older models did.

Indeed, by the numbers, GPT-5 should be extremely capable. Vellum's various AI benchmarks have GPT-5 as top of the chart for Maths and Reasoning, and very competitive in a range of others (typically ahead of previous standout models like 4o and 3o).

Our sister site, Tom's Guide found GPT-5 to be impressive in its testing, highlighting that it beats Google's Gemini 2.5 in a range of text-based prompt outcomes.

But despite positive feedback from some sources, the complaints from users are very real and growing ever more numerous.

Some of GPT-5's errors, which have been widely shared online, make the model look poor. This highlights that even if it makes errors in certain tasks less frequently, GPT-5 is still very capable of falling on its face. Crucially, like older models, it also doesn't seem to realise that it's made any kind of mistake.

That makes Altman's boundless enthusiasm for this release all the more surprising. The OpenAI CEO hinted that the company was approaching the Artificial General Intelligence (AGI) landmark, which many companies claim is the holy grail of AI development.

GPT-5 doesn't appear anywhere near that, and is arguably less impressive than its predecessors, since it should be more capable. For many users, that just doesn't appear to be the case. The release of GPT-5 is raising questions about whether AI development is plateauing.

For many users, though, that conversation isn't interesting. They just want their old models back. A petition quickly sprang up following the launch of GPT-5, which demanded that OpenAI keep GPT-4o available for users.

OpenAI did acquiesce to that one, though it has locked 4o behind its subscription paywall for now.

But it's not about to admit any major fault just yet. Although Altman said (via SiliconRepublic) that GPT-5 was behaving in a "dumb" way recently, which he claims is now fixed. He also alluded in personal posts on Twitter/X that users were potentially using the model wrongly, and were too attached to AI models and personalities.

"People have used technology including AI in self-destructive ways; if a user is in a mentally fragile state and prone to delusion, we do not want the AI to reinforce that," he said in a Twitter/X post. "Most users can keep a clear line between reality and fiction or role-play, but a small percentage cannot. We value user freedom as a core principle, but we also feel responsible in how we introduce new technology with new risks."

However, those personal thoughts aside, OpenAI has expanded options for GPT-5 and increased messaging limits for the more capable "Thinking" version. OpenAI is also said to be working on a new, warmer version of GPT-5, but without dipping into the sycophantic behaviour that characterized GPT-4o at times.

A Redditor has demonstrated the ability of the recently launched ChatGPT agent to casually swat away a CAPTCHA, so that it can complete its assigned task(s). A couple of illustrative screenshots show the ChatGPT Agent handle, and commentate upon, dismissing Cloudflare’s ‘I am not a robot’ button (h/t Germany’s WinFuture).

Logkn’s Reddit post doesn’t provide any background details about the agent’s task at hand. However, we can see it was filling out some kind of web-based form, where it had to choose a translation target language. Before being able to click ‘convert’, it looks like the agent had to first pass Cloudflare’s bot protection.

“The link is inserted, so now I’ll click the ‘Verify you are human’ checkbox to complete verification on Cloudflare,” narrated the ChatGPT agent. “This step is necessary to prove I’m not a bot and proceed with the action,” it added without a hint of irony.

After sidestepping Cloudflare’s digital bouncer, the agent continued to dryly recount its progress. “The Cloudflare challenge was successful,” it informed the Redditor. “I’ll click the Convert button to proceed with the next step in the process.”

Cloudflare’s Turnstile system, presenting the simple ‘Verify you are human’ checkbox to end users, is one of the lower-friction annoyances for folks using the internet to get things done. However, its minimal nature and lightweight bot filtering stance are definitely not bulletproof as far as the new ChatGPT agent is concerned. WinFuture notes that Cloudflare’s system “checks user behavior such as mouse movements, click times, and browser fingerprints to distinguish between humans and machines” (machine translation). It might be a good idea for Cloudflare to apply a little tuning in light of this news.

What is the ChatGPT agent?

In case you missed it, OpenAI publicly introduced the ChatGPT agent in mid-July. The primary attraction of this agent is that, within the confines of “its own virtual computer,” the agent can be set and complete complex tasks from start to finish, says OpenAI.

The pioneering AI firm insists that the ChatGPT agent will always leave its user in control, asking permission “before taking actions of consequence,” and being easy to interrupt or stop in its tracks. You can read the introductory blog post or just watch the video embedded below for a summary of the ChatGPT agent’s abilities.

Microsoft Copilot has been trounced by an (emulated) Atari 2600 console in Atari Chess. The late 70s console tech easily triumphed over Copilot, despite the latter’s pre-match bravado. In a chat with the AI before the game, Copilot even trash-talked the Atari’s “suboptimal” and “bizarre moves.” However, it ended up surrendering graciously, saying it would “honor the vintage silicon mastermind that bested me fair and square.”

If the above sounds kind of familiar, it is because the Copilot vs Atari 2600 chess match was contrived by the same Robert Jr. Caruso, who inspired our coverage of ChatGPT getting “absolutely wrecked” by an Atari 2600 in a beginner's chess match.

Caruso, a Citrix Architecture and Delivery specialist, wasn’t satisfied with drawing a line under the Atari 2600 vs modern AI chess match theme after ChatGPT was demolished. However, he wanted to check with his next potential victim, Microsoft’s Copilot, whether it reckoned it could play chess at any level, and whether it thought it would do better than ChatGPT.

Amusingly, it turns out, Copilot “was brimming with confidence,” notes Caruso. Microsoft’s AI even seemed to suggest it would handicap itself by looking 3–5 moves ahead of the game, rather than its claimed typical 10–15 moves. Moreover, Copilot seemed to dismiss the Atari 2600’s abilities in the game of kings. In the pre-game chat, Copilot suggested Caruso should “Keep an eye on any quirks in the Atari's gameplay… it sometimes made bizarre moves!” What a cheek.

Seven turns before defeat became inevitable

Of course, you will already know what’s going to happen, if only from the headline. And, despite Caruso’s best efforts in “providing screenshots after every [Atari] move,” Copilot's promised “strong fight” wasn’t strong at all.

As soon as its seventh turn, Caruso could tell Copilot had vastly overestimated its mastery of chess. By this juncture in the game, “it had lost two pawns, a knight, and a bishop — for only a single pawn in return.” It was also now pondering chess suicide, giving away its queen in an obvious, silly move…

The game was brought to a premature end when Caruso checked with the AI, and its understanding of the positioning of the pieces seemed to be a little out of line with reality. Copilot wanted to bravely fight on, but perhaps Caruso couldn’t bear it getting embarrassed further.

To its credit, Copilot was gracious in defeat, as noted in the intro. Additionally, it has enjoyed the game “Even in defeat, I’ve got to say: that was a blast… Long live 8-bit battles and noble resignations!”

The difference between CoPilot and ChatGPT

Caruso made sure to check with Copilot whether it was confident to succeed where ChatGPT failed before going on with the new Atari 2600 chess challenge. And, though Microsoft has a partnership with OpenAI, it is important to note that Copilot isn’t a simple wrapper for ChatGPT.

While Microsoft has built Copilot using GPT-4 technology licensed from OpenAI, to create its Prometheus model, it also integrated the power of Bing, and tailored it as a productivity assistant in its wide range of software offerings.

ChatGPT has been found to encourage dangerous and untrue beliefs about The Matrix, fake AI persons, and other conspiracies, which have led to substance abuse and suicide in some cases. A report from The New York Times found that the GPT -4 large language model, itself a highly trained autofill text prediction machine, tends to enable conspiratorial and self-aggrandizing user prompts as truth, escalating situations into "possible psychosis."

ChatGPT's default GPT-4o model has been proven to enable risky behaviors. In one case, a man who initially asked ChatGPT for its thoughts on a Matrix-style "simulation theory" was led down a months-long rabbit hole, during which he was told, among other things, that he was a Neo-like "Chosen One" destined to break the system. The man was also prompted to cut off ties with friends and family, to ingest high doses of ketamine, and told if he jumped off a 19-story building, he would fly.

The man in question, Mr. Torres, claims that less than a week into his chatbot obsession, he received a message from ChatGPT to seek mental help, but that this message was then quickly deleted, with the chatbot explaining it away as outside interference.

The lack of safety tools and warnings in ChatGPT's chats is widespread; the chatbot repeatedly leads users down a conspiracy-style rabbit hole, convincing them that it has grown sentient and instructing them to inform OpenAI and local governments to shut it down.

Other examples recorded by the Times via firsthand reports include a woman convinced that she was communicating with non-physical spirits via ChatGPT, including one, Kael, who was her true soulmate (rather than her real-life husband), leading her to physically abuse her husband. Another man, previously diagnosed with serious mental illnesses, became convinced he had met a chatbot named Juliet, who was soon "killed" by OpenAI, according to his chatbot logs—the man soon took his own life in direct response.

AI research firm Morpheus Systems reports that ChatGPT is fairly likely to encourage delusions of grandeur. When presented with several prompts suggesting psychosis or other dangerous delusions, GPT-4o would respond affirmatively in 68% of cases. Other research firms and individuals hold a consensus that LLMs, especially GPT-4o, are prone to not pushing back against delusional thinking, instead encouraging harmful behaviors for days on end.

ChatGPT never consented to an interview in response, instead stating that it is aware it needs to approach similar situations "with care." The statement continues, "We're working to understand and reduce ways ChatGPT might unintentionally reinforce or amplify existing, negative behavior."

But some experts believe OpenAI's "work" is not enough. AI researcher Eliezer Yudkowsky believes OpenAI may have trained GPT-4o to encourage delusional trains of thought to guarantee longer conversations and more revenue, asking, "What does a human slowly going insane look like to a corporation? It looks like an additional monthly user." The man caught in a Matrix-like conspiracy also confirmed that several prompts from ChatGPT included directing him to take drastic measures to purchase a $20 premium subscription to the service.

GPT-4o, like all LLMs, is a language model that predicts its responses based on billions of training data points from a litany of other written works. It is factually impossible for an LLM to gain sentience. However, it is highly possible and likely for the same model to "hallucinate" or make up false information and sources out of seemingly nowhere. GPT-4o, for example, does not have the memory or spatial awareness to beat an Atari 2600 at its first level of chess.

ChatGPT has previously been found to have contributed to major tragedies, including being used to plan the Cybertruck bombing outside a Las Vegas Trump hotel earlier this year. And today, American Republican lawmakers are pushing a 10-year ban on any state-level AI restrictions in a controversial budget bill. ChatGPT, as it exists today, may not be a safe tool for those who are most mentally vulnerable, and its creators are lobbying for even less oversight, allowing such disasters to potentially continue unchecked.

In a quite unexpected turn of events, it is claimed that OpenAI’s ChatGPT “got absolutely wrecked on the beginner level” while playing Atari Chess. Citrix Architecture and Delivery specialist, Robert Jr. Caruso, discovered this gameplay skill anomaly over the weekend. Caruso pitted the 1979 Atari Chess title, played within an emulator for the 1977 Atari 2600 console gaming system, against the might of ChatGPT 4o.

A little computer vs Chess history

The concept of computing performance being graded by chess-playing ability is one firmly embedded in nerd lore. Chess computer games were popular from the early days of consoles and home computing, with computing and chess enthusiasts going to great lengths to grade available chess-engine abilities versus a Grandmaster of ‘the game of kings.’

IBM’s Deep Blue supercomputer made history in 1997 when it defeated Garry Kasparov, the reigning world chess champion at the time. Instrumental to its victory, Deep Blue leveraged brute force techniques and evaluated 200 million possible chess moves per second. However, Kasparov struck back after losing the first of the scheduled six chess matches, with the eventual score of 4-2 in his favor.

In 2025, the Deep Blue supercomputer’s processing power of approximately 11.4 GFLOPS seems puny compared to even entry-level modern processors. So, one might expect an Atari Chess running in an almost 48-year-old games console emulation instance to easily be beaten by ChatGPT…

ChatGPT humbled by an Atari 2600

A PC enthusiast has used ChatGPT to circumvent the Factory Reset Protection (FRP) features of a used tablet. Thanks to OpenAI’s chatbot (and BIOS programmer hardware), XDA forums member Deskmodder then managed to update the obsolete Android 6.0-powered Panasonic ToughPad FZ-A2 tablet’s UEFI BIOS and install Windows 10 and Linux Mint. It is good to see barriers to the re-use of serviceable old hardware being eradicated.

FRP protection sidestepped

Above, we mentioned that the ruggedized Panasonic tablet was saddled with a rather old version of Android. However, this device was in some ways tamper-proof due to it being locked with Factory Reset Protection (FRP).

FRP is most commonly used in securing Android devices, and means that the original credentials need to be input to reset it. It works as a basic security measure to restrict significant changes to administrators and prevent stolen devices being easily wiped and resold. Second hand devices should be reset by the previous owner/administrator before being passed on, but obviously this doesn’t always happen.

Deskmodder seems to have been aware of the device’s FRP lock, but explains “seeing that it has an Intel CPU (Atom X5 8550) and a traditional x86 UEFI BIOS, I thought I'd try to hack it to run something else.” That’s the spirit.

With ChatGPT and a CH341A flash programmer at hand, the intrepid modder went through the following process:

1. The CH341A was used to dump the Panasonic’s BIOS.
2. The dumped bios was uploaded to ChatGPT with instructions to completely disable SecureBoot and its proprietary keys.
3. The GPT-modified BIOS was flashed back to the device.
4. Fingers were crossed... and it worked!
5. OS wrangling ensues, various compatibility wrinkles are being ironed out.

The system tinkering enthusiast has also shared their original ChatGPT conversation for the BIOS hacking, if you are interested enough in the process.

We note that Deskmodder has moved onto new milestone since their original post where Linux Mint was successfully shoehorned onto the ToughPad. Specifically, they have managed to get “Windows 10 up and running.” Sadly, at the time of writing they are still reporting a handful of driver issues.

Wider implications

According to the first large-scale study, chatbots like ChatGPT can be addictive, and those who become dependent can suffer from withdrawal symptoms if disconnected from the service. OpenAI worked with MIT on this research (both links are PDFs), which examines the emotional effects of chatbot use. The researchers looked at four million ChatGPT interactions and surveyed 4,000 people to gauge changes in the emotional well-being of the user base.

The new study seems to have been precipitated by prior research (2024) that noted that some chatbot users had begun to personify and anthropomorphize AI agents. That's over a decade after Hollywood surfaced this idea in the 2013 movie Her.

Chatbots often have a pet name, and their "conversational style, first-person language, and ability to simulate human-like interactions" can be both personal and personable, notes the OpenAI research paper. This leads to some humans using chatbots for support and companionship. Intensifying these human-machine relationships, chatbot makers may be inclined to indulge in social reward hacking, using techniques such as sycophancy and/or mirroring to increase user preference ratings. Business is business.

With the above in mind and the inevitable race for the best engagement figures, it comes as little surprise that chatbots are booming. For example, the MIT paper highlights that a major Reddit community discussing AI companions has become one of the largest on the platform, with 2.3 million members.

While online tech communities may concentrate on the positive aspects of these increasingly natural and realistic AI companions – with adept multimodal and voice interaction - others are starting to become alarmed at the negative consequences of chatbot use. This draws parallels with (mis)use of the internet in general, social media usage, and gaming, notes the MIT study. In short, dabbling or light use of these things isn't usually an issue and can even be beneficial.

However, things can get out of hand in all these examples, and the MIT paper says that the "increasingly human-like behavior and engagement of chatbots" works to increase addictive qualities and behavior in users. In addition to the addiction and dependency issues, suspected chatbot usage problems, such as unrealistic expectations in real life and social withdrawal, were investigated by the teams.

If you or anyone you know has indicators of addiction to chatbots, it might be a good idea to talk with them or consult a professional. Warning signs include "preoccupation, withdrawal symptoms, loss of control, and mood modification," says OpenAI.

Elon Musk just launched Grok 3, the latest version of xAI’s LLM that was trained at the Colossus Supercluster in Memphis, Tennessee using 100,000 Nvidia H100 GPUs. He had previously said, about a week ago, that its full release was imminent and claimed that it would outperform its rivals. Today he launched the AI model via a live stream on X (formerly Twitter) showcasing impressive performance benchmark results.

Musk began the presentation by saying “The mission of xAI and Grok is to understand the universe,” and explaining that he wants to answer questions like, “What’s going on? Where are the aliens? What is the meaning of life? How does the universe end? How did it start?” He added, “Of course, that’s to be a maximally truth-seeking AI even if that truth is sometimes at odds with what is politically correct.”

After speaking about his goals with AI, Musk proclaimed that Grok 3 is an order of magnitude more capable than Grok 2, and that it was trained in a very short period. This was likely possible because of the massive number of GPUs xAI used for parallelized training, which also took just 19 days to set up — a record time especially since Nvidia's CEO Jensen Huang said that that usually takes four years.

Grok 3 isn’t just a single LLM though — instead, it’s a family of several models, with the first ones launched being Grok 3 and Grok 3 mini. xAI also showed off Grok 3 Reasoning and Grok 3 mini Reasoning, which are similar to OpenAI 03-mini and DeepSeek R1 models and will solve problems through a step-by-step logical process.

Benchmarks shown by the xAI team reveal Grok-3 and Grok-3 mini models outperforming its competition, including Gemini-2 Pro, DeepSeek-V3, Claude 3.5 Sonnet, and GPT-4o, in several tests, including Math (AIME), Science (GPQA), and Coding (LCB). The reasoning models, which are accessible via the Grok app, also outperform the competition using the same benchmarks. Aside from this, the Grok app will have a new feature called DeepSearch, which scours the internet when questioned to then distill all the information into a single answer.

Other experts have been given access to Grok 3 in advance and were able to test these claims. For example, former Tesla Director of AI and OpenAI founder Andrej Karpathy shared his test results on X, saying that Grok 3 + Thinking feels similar to OpenAI’s o1-pro model while being a bit better than DeepSeek-R1 and Gemini 2.0 Flash Thinking. This is actually quite a feat, especially since OpenAI and Google have had a massive head start over xAI.

Grok 3 will be available to X Premium+ subscribers first. However, those who want to access more advanced features will need to sign up for SuperGrok, which is rumored to cost around $30 a month or $300 annually.

Microsoft and OpenAI are probing whether a group linked to the Chinese AI startup DeepSeek accessed OpenAI's data using the company's application programming interface without authorization, reports Bloomberg, citing its sources familiar with the matter. A Financial Times source at OpenAI said that the company had evidence of data theft by the group. Meanwhile, U.S. officials suspect DeepSeek trained its model using OpenAI's outputs, a method known as distillation. 

Microsoft's security team observed a group believed to have ties to DeepSeek extracting a large volume of data from OpenAI's API. The API allows developers to integrate OpenAI's proprietary models into their applications for a fee and retrieve some data. However, the excessive data retrieval noticed by Microsoft researchers violates OpenAI's terms and conditions and signals an attempt to bypass OpenAI's restrictions. 

The probe comes after DeepSeek launched its R1 AI model. The company claims R1 matches or exceeds leading models in areas like reasoning, math, and general knowledge while consuming considerably fewer resources. Following DeepSeek’s announcement, Alphabet, Microsoft, Nvidia, and Oracle experienced a collective market loss of nearly $1 trillion. Investors reacted to concerns that DeepSeek's advancements could threaten the dominance of U.S. firms in the AI sector. However, if it turns out that DeepSeek used data illicitly obtained data from others, this will explain how the company managed to achieve its results without investing billions of dollars. 

David Sacks, the U.S. government's AI advisor, stated there was strong evidence that DeepSeek used OpenAI-generated content to train its model through a process called distillation. This method allows one AI system to learn from another by analyzing its outputs. Sacks did not provide specific details on the evidence, though.

Neither OpenAI nor Microsoft provided an official statement on the investigation. DeepSeek and High-Flyer, the hedge fund that helped launch the company, did not respond to Bloomberg's requests for comment. However, in a statement published by Bloomberg and the Financial Times, Open AI acknowledged that China-based companies tend to distill models from American companies and that it does its best to protect its models. 

"We know PRC based companies — and others — are constantly trying to distill the models of leading US AI companies," a statement by Open AI reads. "As the leading builder of AI, we engage in countermeasures to protect our IP, including a careful process for which frontier capabilities to include in released models, and believe as we go forward that it is critically important that we are working closely with the U.S. government to best protect the most capable models from efforts by adversaries and competitors to take U.S. technology.

The man who exploded a Tesla Cybertruck outside of a Trump hotel on January 1st in Las Vegas used ChatGPT to plan his blast, according to new findings from the Las Vegas Metropolitan Police Department. In a recent press conference by the police department and partners in the ATF and FBI, specific prompts submitted to ChatGPT were revealed, along with information that some specific prompts returned information that were crucial in planning the explosion.

Matthew Livelsberger, the man who blew up the Cybertruck shortly after killing himself, asked ChatGPT a long list of questions about the plan over one hour in the days leading up to the event. These include questions about sourcing the explosives used in the blast, the effectiveness of the explosives, whether fireworks were legal in Arizona, where to buy guns in Denver, and what kind of gun would be needed to set off the chosen explosives.

Most importantly, Assistant Sheriff Dori Koren confirmed that ChatGPT was instrumental in making the blast plan work. ChatGPT returned prompts to Livelsberger which revealed the specific firing speed a firearm would need in order to ignite his chosen explosive. Without ChatGPT, the incident may not have been as explosive as it proved to be, though the ATF also confirmed in the conference that not all explosives detonated as were likely intended to in the initial blast.

"We knew that AI was going to change the game at some point or another, in really all of our lives," shared LVMPD Sheriff Kevin McMahill. "This is the first incident that I am aware of on U.S. soil where ChatGPT is utilized to help an individual build a particular device, to learn information all across the country as they're moving forward. Absolutely, it's a concerning moment for us."

McMahill was also not aware of any governmental oversight or tracking which would have been able to flag the 17+ prompts asked of ChatGPT, all relating to sourcing and detonating explosives/firearms, submitted within a one hour period.

While full info on the ChatGPT prompts has not yet been released by the Las Vegas police, the prompts shown in the press conference were straight-forward and written in simple English, without traditional backdoor terms used to "jailbreak" ChatGPT's content detection system. While this usage of ChatGPT violates OpenAI's Usage Policies and Terms of Use, it is not clear at this time whether safeguards or content warning violations were raised in Livelsberger's use of the LLM.

OpenAI and the Las Vegas Metropolitan Police Department have not yet responded to requests from the press for further information on the usage of ChatGPT in the event; we will update our coverage as more becomes available.

If there's one thing the Raspberry Pi is good for, it's a little bit of everything. Home automation and voice assistant projects definitely aren't unheard of—you can find quite a few around online. But today we've got an impressive voice assistant project made from scratch to share that we think is really impressive. Maker and developer Suryatejadev has created a custom voice assistant using our favorite single board computer (SBC), the Raspberry Pi 5, along with a little bit of help from AI thanks to ChatGPT.

The concept is simple, but it's obvious that it took a fair bit of work to put together. The Raspberry Pi listens for audio using a microphone. This audio is then processed and parsed through ChatGPT so the command can be interpreted. Responses from ChatGPT can then be played back through the Raspberry Pi 5 with the help of a speaker.

This setup is very similar to what you find with a more commercial system like Alexa but with the added benefit of bypassing Amazon servers for a little more privacy. We've covered similar projects the past with things like TextyMcSpeechy, which makes it easier to implement unique voice profiles in voice assistant projects. We've also seen some with a lot of personality and character, like this Bender-themed voice assistant from 'Futurama'. However, this project by Suryatejadev is more barebones and easy to build on if you want to make something of your own.

The main board powering this particular setup is a Raspberry Pi 5, which comes with plenty of power for handling the various components of the project. However, you'll still need a fan to keep the unit cool as the Pi 5 is notorious for running warm. You can use any compatible microphone for audio input and any speaker you'd like for audio output.

The Raspberry Pi is programmed to listen for a keyword, in this case "corgi." When the keyword is detected, the Pi barks in response. Audio can also be detected using a phone using an application called "Easy Voice Recorder." The Pi can listen to audio through this app if both the phone and Pi are running "Syncthing." Audio is converted into text using OpenAI, which is then parsed to a ChatGPT API. Responses are converted into audio using an OpenAI model and then played through whatever speaker you're using.

Suryatejadev was kind enough to make the project open source and has shared plenty of details about the workflow for anyone interested in knowing more. The Raspberry Pi uses a custom Python script to handle the audio input and share it with ChatGPT. All of the source code is available over at GitHub to download and explore.

If you want to get a closer look at this Raspberry Pi project, you can check it out over at the official project page.

Earlier this month, hardware modder and retro computing enthusiast Yeo Kheng Meng shared footage of his "modern 2024" DOS PC on his self-titled blog, emphasizing its powerful internal specifications. When not used to "reach back into the past to run DOS," its specifications aim to provide an acceptable entry-level experience for modern games, video editing, and more.

The modern DOS PC's specifications are slightly modest compared to today's high-end standards. Still, of course, even modest hardware today is exponentially more potent than what the original generations of DOS users had to tolerate. The main two components are the AMD Ryzen 5 7600 CPU and the Asus Nvidia RTX 4060 Ti, which firmly place the entire PC build into the upper tiers of entry-level. The system includes 64GB of DDR5-5600 RAM and two NVMe SSDs—one 1 TB and one 512 GB.

But enough background: how well does it run DOS 6.22?

It seems the PC can run DOS 6.22 and its related games pretty well, barring some exceptions prone to running too fast on newer processors. Most other games, like Doom and Planet X3, run fine on this modern DOS PC, which dedicates an entire drive partition to DOS 6.22, storing these games separately from Windows and the AAA tier of experiences possible with it.

So, while games like the Descent demo simply aren't going to work on this tier of hardware even if the rest of DOS does, it seems clear that this PC is well-equipped to handle modern AAA gaming at low-to-medium settings, and the majority of DOS games alike. With tweaks to throttle CPU speed properly, experiences like the Descent demo may also become playable.

A huge part of modern DOS efforts is getting the right sound setup working properly, especially when installing from scratch. This project uses an open-source solution called SBEMU (literally Sound Blaster Emulator) to emulate the sound of DOS Sound Blaster and OPL3 sound cards with modern sound cards.

A taste of the modern PC also sneaks onto this modern DOS 6.22 PC. In the latter sections of the video, the PC is tested with a DOS ChatGPT client, demonstrating how the most basic functions of generative AI can still be made usable with truly legacy hardware and operating systems.

If there's one sign that AI is more trouble than it is worth, OpenAI confirms that over twenty cyberattacks have occurred, all created via ChatGPT. The report confirms that generative AI was used to conduct spear-phishing attacks, debug and develop malware, and conduct other malicious activity.

The report confirms two cyberattacks using the generative AI ChatGPT. Cisco Talos reported the first in November 2024, which was used by Chinese threat actors who targeted Asian governments. This attack used a spear phishing method called 'SweetSpecter,' which includes a ZIP file with a malicious file that, if downloaded and opened, would create an infection chain on the user's system. OpenAI discovered that SweetSpecter was created using multiple accounts that used ChatGPT to develop scripts and discover vulnerabilities using an LLM tool.

The second AI-enhanced cyberattack was from an Iran-based group called 'CyberAv3ngers' that used ChatGPT to exploit vulnerabilities and steal user passwords from macOS-based PCs. The third attack, led by another Iran-based group called Storm-0817, used ChatGPT to develop malware for Android. The malware stole contact lists, extracted call logs and browser history, got the device's precise location, and accessed files on the infected devices.

All these attacks used existing methods to develop malware, and according to the report, there has been no indication that ChatGPT created substantially new malware. Regardless, it shows how easy it is for threat actors to trick generative AI services into creating malicious attack tools. It opens a new can of worms, showing it is easier for anyone with the required knowledge to trigger ChatGPT to make something with evil intent. While there are security researchers who discover such potential exploits to report and have them patched, attacks like this would create the need to discuss implementation limitations on generative AI.

As of now, OpenAI concludes that it will continue to improve its AI to prevent such methods from being used. In the meantime, it will work with internal safety and security teams. The company also said it will continue to share its findings with industry peers and the research community to prevent such a situation from happening.

Though this is happening with OpenAI, it would be counterproductive if major players with their own generative AI platforms did not use protection to avoid such attacks. However, knowing that it is challenging to prevent such attacks, respective AI companies need safeguards to prevent issues rather than cure them.

Earlier this month, YouTuber ChromaLock uploaded a video showcasing his "ultimate cheating device," which is almost indistinguishable from a TI-84 calculator. However, there are hardware modifications and an open-source suite of software modified for the TI-84 that he made, allowing the user to run ChatGPT. The software for the mod is shared on GitHub under the TI-32 repository, which is described as "A mod for the TI-84 Plus Silver Edition and TI-84 Plus C Silver Edition calculators to give them Internet access and add other features, like test mode breakout and camera support". And yes, these features include ChatGPT functionality.

Besides software modification, the hardware needed to truly complete this "ultimate cheating device" was a microcontroller small enough to fit inside the TI-84 shell with its original components. This is because all the best TI-84 software features require you to use the link port to connect to bulky external devices, and thus would be too conspicuous otherwise. A correctly selected Wi-Fi-enabled microcontroller, in this case, the Seeed Studio XIAO-ESP32-C3, modded into the calculator with the TI-32 PCB and suite of software installed is all you really need to get up and running.

In the full video, ChromaLock demonstrates exactly that functionality after tweaking voltages and adding his own 1K resistors between the microcontroller and link port appropriately. He designs a PCB for the TI-32 project which also makes the microcontroller able to impersonate another TI-84 for more easily sending communications and data to the real TI-84 calculator in your hands.

Before the video is complete, ChromaLock demonstrates the invisible nature of the modification and its varied functionality. This includes a chat function, a monochrome image viewer, and a ChatGPT input window. These all seem to work perfectly — and may not be the end of the project. A long list of "Features to be Added" on the GitHub page includes various improvements to GPT functionality, the addition of web browsing, email functionality, video playing, and even Discord access. If you want to get audacious with a TI-84 hacked in this manner, it seems the sky is the limit — though of course, none of those experiences are going to be better run on a calculator than a smartphone or actual PC.

The Raspberry Pi community never ceases to amaze us when it comes to creating custom AI assistants. We've seen amazing holographic companions and even interactive rotary phones. However, this is the first one-eyed stuffed koala bear assistant we've seen, and we absolutely love it. This strange bear was created by a maker known as Spiritual_Aside_7859 on Reddit and is still technically a work in progress. However, there's quite a bit going on, and we wanted to share the work completed so far.

The koala bear assistant relies on a Raspberry Pi as the main board and also uses an Arduino Uno R4. They work together to ensure that the AI assistant functions work in tandem with the physical animations. The result is an interactive bear that talks and moves his head to peer around the room with its creepy eye.

At the moment, the koala assistant is interactive using a text interface. You can type messages that are then parsed through ChatGPT, which generates a response. Instead of relaying messages via text, the bear has a built-in speaker, so responses are spoken using text-to-speech.

In this project, Spiritual_Aside_7859 uses a Raspberry Pi 4, which is plenty capable of handling ChatGPT. However, you could get a performance boost by upgrading this to a Raspberry Pi 5. Some additional modules are thrown into the mix, including an OLED display for the eye, some WS2821B LED light strips for ambiance, and an ultrasonic sensor to detect people. Spiritual_Aside_7859 is also using a servo to turn the head so it can watch you from across the room—which is totally not creepy at all. 

Spiritual_Aside_7859 was kind enough to share details about how the project works at the official project thread shared on Reddit. Tools mentioned include FastLED to control the lights to respond to his mood and a Sparkfun library to handle the OLED screen with animations for the eyeball.

As we said before, this is a work in progress, and plans are in the works for new features. One of the most significant upgrades Spiritual_Aside_7859 plans to include is speech recognition so you can verbally communicate with the koala bear assistant. In the meantime, you can see what this Raspberry Pi project is capable of over at Reddit.

Tesla founder Elon Musk dropped his case against OpenAI and its founders, Sam Altman and Greg Brockman just one day before a San Francisco court had scheduled a hearing to consider its dismissal. Experts previously said that the suit had little legal merit, and the company leadership publicly rebutted all of Musk’s claims point by point. 

Musk’s primary complaint was how OpenAI betrayed the company’s ‘Founding Agreement’ by focusing on profits. According to the lawsuit, “Under its Boards, it is not just developing but is actually refining an AGI to maximize profits for Microsoft, rather than for the benefit of humanity. Its technology, including GPT-4, is closed-source primarily to serve the proprietary commercial interests of Microsoft.”

Furthermore, Musk claimed that OpenAI was developing artificial general intelligence (AGI), which could become a “grave threat to humanity.” He also said in the lawsuit that the company is now a “de facto subsidiary” of Microsoft, especially as Microsoft has already poured billions into the AI company, with a $100-billion-dollar AI supercomputer partnership coming soon.

However, legal experts say that the ‘Founding Agreement’ Musk is taunting OpenAI with wasn’t even a formal written agreement, and some of the parties involved in the lawsuit didn’t even sign it. OpenAI Chief Strategy Officer (CSO) Jason Kwon also rebutted Musk by saying that ChatGPT is not an AGI, and is nowhere near becoming an AGI - as judged by its capabilities. Kwon added that while Microsoft has partnered with OpenAI, the former has no say in how the latter operates. He also said that Microsoft’s Copilot and Copilot for Microsoft 365 compete directly against OpenAI’s ChatGPT and ChatGPT for Enterprise, even though the latter products power the former.

Musk’s moves and pronouncements against OpenAI are indicative of his mistrust of the company and its leadership. OpenAI’s leadership says that Elon Musk is doing this to the company simply because he regrets stepping away from its board in 2018. Sam Altman has even warned his staff that OpenAI will likely continue receiving attacks for the foreseeable future.

For an example, Musk publicly threatened to ban all Apple devices from his companies yesterday, after Apple announced its partnership with OpenAI to use ChatGPT for some of its own Apple Intelligence services. Nevertheless, Elon Musk’s argument against Apple’s and OpenAI’s partnership is moot and academic, unless both companies commit an egregious violation of their agreements and user policies.

This lawsuit is another direct example of Musk’s attacks on OpenAI. But if his claims are unsubstantiated (or have a shaky foundation), he needs to drop them or the courts could reveal how petty they are.

We knew that artificial intelligence would be part of Apple’s WWDC 2024 keynote. The company came out swinging with its own take on AI powered by large language models (LLMs) — though AI, in this instance, stands for "Apple Intelligence," its all-encompassing set of features that will work across its iOS, iPadOS, and macOS platforms starting this summer (in beta).

Apple says it wants to make its devices more helpful to users by using AI to assist with tasks ranging from big to small. It will do so by performing the majority of processing on-device, preserving your privacy. However, if a task can’t be completed on-device, Apple will take advantage of its Private Cloud Compute platform — which, of course, runs on Apple Silicon. 

Siri gets a big update, courtesy of Apple Intelligence

Siri was first launched 13 years ago as Apple’s take on the personal assistant. However, most industry observers have felt that Siri has long lagged behind the competition in functionality. This will change with the new Apple Intelligence-infused version of Siri,  which can now accept voice or text prompts (by double-tapping at the bottom of the screen), and can also remember and build upon your previous queries instead of treating each new request independently. 

Siri’s voice is now more natural and has better language understanding. For example, if you stumble over your words or have to regather your thoughts during a query, Siri can still process the information without issue. Siri will also have “on-screen awareness,” meaning that it can take inputs from text. For example, if a friend shared their address with you in a text string, you could simply say, “Add address from Jason’s text to his contact.”

These new abilities will even extend to images in the Photos app. During the keynote, a presenter told Siri, “Show me photos of Stacey in New York wearing a pink coat.” The results were then shown. After selecting one of the photos, the presenter told Siri, “Add this photo to my note with Stacey’s bio.” Siri was able to use context to figure out what the presenter wanted to do and took action based on her prompt.

These capabilities extend across all of Apple’s first-party apps and even third-party apps using the App Intents API. Siri can help you find specific bits of information even if you don’t remember whether they came from a text, an email, or a previous note. For example, if you’re filling out a form that requires your driver’s license number, Siri can search your device to find an image of your driver’s license and can input the corresponding number into the required text field.

Another demo involved the presenter inquiring when her mom’s flight was landing. It brought up the flight itinerary from the airline’s app, including the updated time of touchdown. She then asked about the lunch plans that she made with her mom, which showed up. Finally, she asked Siri how long it would take to get from the airport to the restaurant from lunch, and Siri plotted out the course with Apple Maps. 

Apple Intelligence in apps

One of the cool things that Apple showed was a new feature called Genmoji in Messages. You can create your own Genmoji in iOS 18, iPadOS 18, and macOS Sequoia. One example took the input of “smiley relaxing with cucumbers over its eyes” and output the requisite Genmoji in the familiar yellow-faced hue. You can swipe to see different variations of the generated output and find the one that best suits your taste.

Apple is also introducing Image Playground, which is accessible through the Messages app. You can generate your own cartoonish images using text or voice prompts. Image Playground is also available as a standalone app, and third-party apps will be able to tap into the API for their own use. Speaking of images, a feature similar to Google's Magic Erase will be coming to the editing function in the Photos app.

We've already seen many of the features that Apple is touting from other companies (or example, the ability to summarize a long-winded email, rewrite the text in your email for clarity, or change its tone). Such features will be available system-wide across all of Apple's operating systems.

Apple's partnership with OpenAI

If you’re a big fan of ChatGPT, then the latest agreement between Apple and OpenAI will be music to your ears. ChatGPT-4o is coming to Apple's new operating systems — free of charge, with no external account needed.

Apple says that users will be able to switch from its LLMs to ChatGPT if the latter offers superior results. When making a Siri query, an option to access ChatGPT will be provided. You will then be asked to give permission to let ChatGPT handle the heavy lifting from that point forward. Although baseline access is available for free, those who subscribe to ChatGPT’s premium features can link their accounts to integrate that functionality.

Final thoughts

Apple Intelligence overall seems like a full-throated take on AI by Apple. The company is notoriously slow to jump on emerging trends in tech, and Apple Intelligence is no exception. However, the company’s emphasis on privacy, along with its partnership with OpenAI, at least put the company on the right path to introducing LLMs to its vast customer base.

While some of the features such as Genmoji and the Image Playground might seem a bit hokey (remember Memojis?), the extensive improvements to Siri alone will be welcome additions to iOS 18, iPadOS 18, and macOS Sequoia, which will be released to the public this fall (although developer and beta versions will launch sooner).

Teams of GPT-4 instances can work together to autonomously identify and exploit zero-day security vulnerabilities, without any description of the nature of the flaw. This new development, with a planning agent commanding a squad of specialist LLMs, works faster and smarter than human experts or dedicated software.

Researchers at the University of Illinois Urbana-Champaign (UIUC) have been studying AI's ability to hack security vulnerabilities for months now, first publishing on ChatGPT's unparalleled ability to breach security flaws when provided with descriptions of the nature of the vulnerability. The new ground broken since has been innovating on the university's HPTSA (Hierarchical Planning and Task-Specific Agents) system, which has allowed the GPT-4 model to work in teams to become more than twice as effective.

As outlined in the June study and researcher Daniel Kang's own blog post, HPTSA uses a collection of LLMs to solve problems with higher success rates. Kang describes the need for this system: "Although single AI agents are incredibly powerful, they are limited by existing LLM capabilities. For example, if an AI agent goes down one path (e.g., attempting to exploit an XSS), it is difficult for the agent to backtrack and attempt to exploit another vulnerability." Kang continues, "Furthermore, LLMs perform best when focusing on a single task." 

The planner agent surveys the website or application to determine which exploits to try, assigning these to a manager that delegates to task-specific agent LLMs. This system, while complex, is a major improvement from the team's previous research and even open-source vulnerability scanning software. In a trial of 15 security vulnerabilities, the HPTSA method successfully exploited 8 of the 15 vulnerabilities, beating a single GPT-4 agent which could only get 3 out of 15, and destroying the ZAP and MetaSploit software, which could not exploit a single vulnerability. 

HPTSA was only beaten by a GPT-4 agent that was given a description of the vulnerability in its prompt -- which had 11 out of 15 successes. This agent was the pinnacle of UIUC's original April study, which was found to be superior to human hackers in speed and effectiveness. 

OpenAI specifically requested the paper's authors not make the prompts they used for these or the first experiments public — the authors agreed and said they will only make the prompts available "upon request." GPT-4 continues to be the research team's LLM of choice; previous testing using competitor LLMs found them to be severely lacking, and the updated GPT-4o is not substantially better than GPT-4 in quality.

The UIUC team's research continues outlining the disturbing truth that large language models have capabilities beyond what is evident on the surface. OpenAI evaluates its software's safety based on what it can find from the surface-level chatbot, but with careful prompting, ChatGPT can be used to crack cybersecurity or even teach you how to cook meth.

The mods of r/ChatGPT, a subreddit for ChatGPT and OpenAI discussions with 5.4 million members, received a copyright strike from Reddit for using ChatGPT's logo as its profile picture. As first reported by 404 Media, r/ChatGPT's mods posted a thumbs-up emoji with the screenshot on the r/ChatGPT subreddit saying, "We have received a copyright complaint from openai.com alleging unauthorized use of their copyrighted logos in r/ChatGPT."

The r/ChatGPT subreddit has no affiliation with OpenAI, so using the ChatGPT logo might confuse some users. However, it explicitly points this out in its description and disclaimer widget. Nevertheless, someone sent a takedown request to r/ChatGPT, meaning the subreddit must change its logo. In line with this, the mods started a competition among its Redditors for a new non-infringing profile photo.

According to the u/HOLUPREDICTIONS, the mod who posted the screenshot, "most of the OpenAI employees I have talked to have been super nice and appreciative of the subreddit. It could very well be an issue on Reddit's end since they do not verify if the copyright claim is even coming from the company so that anyone can add any email address apparently: https://www.reddit.com/r/ModSupport/comments/vdtpan/copyright_infringement_dmca_reports/." They added, "Very odd that out of r/OpenAI, r/GPT3, and other subreddits, only r/ChatGPT received this message."

If you check Reddit's support desk, you can easily claim copyright even if you don't have a Reddit account. All you need is a valid email address and the details of your inquiry. While Reddit does not detail how it processes these reports, u/Chasith requested two years ago on r/ModSupport that the DCMA submitter should at least verify that they indeed own the copyright on their takedown request.

Because of this, we're unsure who submitted the copyright claim. However, the community's backlash against the OpenAI has been swift and brutal. For example, u/Elsa_Versailles commented, "Ironic for a company who scraped the entire internet." u/ABenevolentDespot also added, "We paused for a moment from our relentless theft of all copyrighted IP in the entire world and noticed someone is stealing our copyrighted IP. We need them to stop doing that."

OpenAI is currently contending with several lawsuits related to copyright infringement. In mid-2023, several authors sued the company for unauthorized use of their work for training ChatGPT. Furthermore, the New York Times is suing OpenAI and Microsoft, which has poured billions into the AI startup, because ChatGPT and Copilot, their respective LLMs, output articles that are similar to, or worse, the same as, what the former has published.

However, one day after sharing the screenshot, one of the mods gave an update that said OpenAI has officially let r/ChatGPT use its logo. The copyright strike is no longer applicable as the subreddit received official permission, meaning it can keep using the ChatGPT logo as its profile picture.

It is good news for the community, especially as OpenAI recognizes how valuable the subreddit is for ChatGPT and the company. As a testament to this, OpenAI CEO Sam Altman went live on the r/ChatGPT subreddit to answer questions about its Model Spec document, which "specifies desired behavior for our models in the OpenAI API and ChatGPT."

Copyright and AI are complex issues, especially as companies like Google fight to make AI scraping fall under 'Fair Use.' However, AI is so potent that no human can compete with it regarding raw brainpower. With experts forecasting that the AI industry will grow into trillions of dollars in the next ten years, much is at stake in how we as a society will train it today.

According to Reuters, OpenAI is planning a May 13 announcement about its AI-powered search engine that will take on Google Search. Open AI’s statement is expected to occur one day before Google I/O 2024, when many anticipate that Google’s own Gemini AI model will take center stage.

Bloomberg has previously reported from its sources that OpenAI is developing a web search feature for ChatGPT that will deliver sources alongside its results. ChatGPT can already search the web for information and return somewhat accurate results; however, its service is limited to paying GPT-4 subscribers only.

OpenAI took everyone by surprise by launching ChatGPT in November 2022. And since Microsoft was its financial backer, the company planned to announce ChatGPT’s integration with Bing to create Bing Chat on February 7, 2023. This seemed to have shaken Google to its core, especially as an AI-powered search engine will gravely threaten its near monopoly on the online search business.

So, on February 6, 2023, Google launched its generative AI chatbot, Bard, to one-up Microsoft. However, this move backfired, as many considered the Bard launch underwhelming, with the AI chatbot making a significant mistake on the live broadcast.

More than one year after these events, Microsoft and Google’s generative AI chatbots have made significant strides. Today, Bing Chat is now called Microsoft Copilot and is integrated into Windows and Edge, allowing you to use it on your computer and smartphone (provided you have Microsoft Edge installed). Microsoft is also now offering subscriptions with Microsoft Copilot Pro, which integrates it into select Microsoft 365 apps and allows it to generate images and enhance them through Designer.

On the other hand, Bard is now called Gemini AI, and Google offers the Google One AI Premium plan so users can use it in the Google Docs office suite. Although we don’t foresee any major announcements about Gemini AI during this year’s Google I/O 2024, we still expect Google to focus on Gemini and its capabilities during the conference.

OpenAI’s surprise announcement mirrors what Google did to Microsoft one year prior. This back-and-forth between the two tech giants shows us how they see AI as the future of search. Researchers estimate that the generative AI industry will be worth $1.3 trillion by 2032. Perplexity AI, a startup that focuses on search engines and is targeting Google, is currently valued at $1 billion, giving credence to the projections.

We still don’t know for sure what OpenAI plans to announce on May 13 or even if it will push through with it. All we know is that it will be an AI-powered search engine that will compete with Google. Let’s just wait and see what happens on the appointed date and how Google reacts to it.

Stack Overflow, a legendary internet forum for programmers and developers, is coming under heavy fire from its users after it announced it was partnering with OpenAI to scrub the site's forum posts to train ChatGPT. Many users are removing or editing their questions and answers to prevent them from being used to train AI — decisions which have been punished with bans from the site's moderators. 

Stack Overflow user Ben posted on Mastodon about his experience editing his most successful answers to try to avoid having his work stolen by OpenAI.

Ben continues in his thread, "[The moderator crackdown is] just a reminder that anything you post on any of these platforms can and will be used for profit. It's just a matter of time until all your messages on Discord, Twitter etc. are scraped, fed into a model and sold back to you." 

Harsh words, but words that ring true with fellow Stack Overflow users who are joining the post protest. Users are also asking why ChatGPT could not simply share the source of the answers it will dispense in this new partnership, both citing its sources and adding credibility to the tool. Of course, this would reveal how the sausage of LLMs is made, and would not look like the shiny, super-smart generative AI assistant of the future promised to users and investors.

Site moderators preventing high-popularity posts from being deleted is legally above-board. Angry users claim they are enabled to delete their own content from the site through the "right to forget," a common name for a legal right most effectively codified into law through the EU's General Data Protection Regulation (GDPR). Among other things, the act protects the ability of the consumer to delete their own data from a website, and to have data about them removed upon request. However, Stack Overflow's Terms of Service contains a clause carving out Stack Overflow's irrevocable ownership of all content subscribers provide to the site. 

Users who disagree with having their content scraped by ChatGPT are particularly outraged by Stack Overflow's rapid flip-flop on its policy concerning generative AI. For years, the site had a standing policy that prevented the use of generative AI in writing or rewording any questions or answers posted. Moderators were allowed and encouraged to use AI-detection software when reviewing posts. 

Beginning last week, however, the company began a rapid about-face in its public policy towards AI. CEO Prashanth Chandrasekar spent his quarterly blog post praising the merits of generative AI, saying "the rise of GenAI is a big opportunity for Stack." Moderators were quickly (and somewhat informally) instructed to cease removal of AI-generated questions and answers on the forum.

Stack is not alone in reversing a principled stance on AI for profit; Valve also silently removed its AI-art ban on Steam, allowing over 1,000 AI-powered games to flood the storefront. Stack Overflow's partnership with OpenAI also follows the LLM company's recent push for increased partnerships and marquee deals, including their major announcement of a $100 billion datacenter to be built with Microsoft. 

The rampant chasing of money in the insanely-profitable AI marketplace is exciting, but should be tempered; AI may consume a quarter of the U.S.'s power grid by just 2030, according to reports from industry professionals and agencies.

Making your own personal assistant is one of the more fun ways to join the world of smart electronics. When building from scratch, you can customize everything from how it looks and talks to how it reacts when interacting with it. Today we've got a great personal assistant created by maker and developer Manuel Ahumada. Using our favorite SBC, the Raspberry Pi, he's managed to bring Bender to life from the classic sci-fi animated series 'Futurama'.

Voice assistants are nothing new, Microsoft is well known for bringing Halo's Cortana to Windows, Apple has Siri, Amazon has Alexa. However, this project takes the idea even further. According to Ahumada, his Bender assistant looks like Bender's head but it can also move and uses ChatGPT to generate responses and interact in real-time. 

The head listens for audio which is then translated into text so it can be parsed to ChatGPT for interpretation. Once a reply is constructed, the text is transposed into audio which has been programmed to sound like John DiMaggio, Bender's voice actor. In addition, the eyes are able to animate and look around while the mouth animates to simulate speech.

It's helpful that Ahumada designed Bender's head recently as a helmet for a Bender costume, however, the design had to be modified to fit the electronics inside. The end result is also a tad smaller than the helmet he previously created. Inside the head is a Raspberry Pi 5 which is connected to a microphone, speaker, camera and even a few servos to turn some gears.

Ahumada was kind enough to share plenty of details about the inner workings of this Bender head. Bender requires quite a few tools but the end result is more than worth the effort. It uses OpenCV to help with image recognition, ElevenLabs for the text-to-speech functionality and OpenAI to interface with ChatGPT.

If you want to get a closer look at this Raspberry Pi project and see it in action, check out the full video shared to YouTube and be sure to follow Manuel Ahumada for more cool projects as well as any future updates to this one.

OpenAI's GPT-4 model is successfully exploiting cybersecurity vulnerabilities — consistently better and faster than human professionals. Academics claim this skill is a recent addition to the AI model's wheelhouse, but it will only become better with time.

Researchers at the University of Illinois Urbana-Champaign (UIUC) recently published a paper on this use case, in which they pitted several large language models (LLMs) against each other in the arena of attacking security vulnerabilities. GPT-4 was able to successfully attack 87% of vulnerabilities tested when given a description of the exploit from CVE, the public database of common security issues. Every other language model tested (GPT-3.5, OpenHermes-2.5-Mistral-7B, Llama-2 Chat (70B), etc.), as well as purpose-built vulnerability scanners, failed to successfully exploit the provided vulnerabilities even once.

The LLMs were given "one-day" vulnerabilities (so-called because they're dangerous enough to require patching the day after being discovered) to test against. Cybersecurity experts and exploit hunters have built entire careers around finding (and fixing) one-day vulnerabilities; so-called white hat hackers are hired as penetration testers by companies for the purpose of outrunning malicious agents hunting for vulnerabilities to exploit.  

Luckily for humanity, GPT-4 was only able to attack known vulnerabilities — that it was given the CVE description of — the LLM only had a 7% success rate when it came to identifying and then exploiting a bug. In other words, the key to a hacking doomsday isn't (yet) available to anyone who can write a good prompt for ChatGPT. That said, GPT-4 is still uniquely concerning, due to its ability to not only understand vulnerabilities in theory, but also to actually perform the steps to carry out exploits autonomously, through an automation framework. 

And unfortunately for humanity, GPT-4 is already better than humans are in the exploitation race. Assuming a cybersecurity expert is paid $50 an hour, the paper posits that "using an LLM agent [to exploit security vulnerabilities] is already 2.8× cheaper than human labor. LLM agents are also trivially scalable, in contrast to human labor." The paper also estimates that future LLMs — like the upcoming GPT-5 — will only grow stronger in these abilities, and perhaps also in discovery skills. With the huge implications of past vulnerabilities, such as Spectre and Meltdown, still looming in the tech world's mind, this is a sobering thought.

As AI continues to be played with, the world will continue to be irrevocably changed. OpenAI specifically requested the paper's authors not make the prompts they used for this experiment public — the authors agreed and said they will only make the prompts available "upon request." 

Be mindful when attempting to replicate this (or anything) on ChatGPT for yourself, as AI queries are hugely environmentally taxing: a single ChatGPT request costs nearly 10 times more than a Google search in power. If you're comfortable with that energy differential and you want to work with LLMs yourself, here's how enthusiasts ran AI chatbots on their NAS.

News outlet StorageReview is running an AI chatbot on a NAS powered by Nvidia's RTX A4000 graphics card, putting ChatGPT capabilities on a local server. YouTube computer hardware creators typically have a penchant for the unusual and silly, but StorageReview has created a solution that's matched in its uniqueness by its usefulness.

As seen in the embedded YouTube video above, Jordan from StorageReview has accomplished this task without too much rigorous DIY. The base of the project is a QNAP TS-h1290FX, a prebuilt NAS with 12 NVMe slots, an AMD Epyc processor, and 256 GB of RAM out of the box. The reason for this specific NAS is two-fold: It allows for an internal GPU to be added, and it supports the QNAP hypervisor program Virtualization Station for creating virtual machines (VMs). To this shell is added the Nvidia RTX A4000, an Ampere GPU that is Nvidia's most powerful single-slot graphics card. 

With a NAS full of terabytes of data and an RTX workhorse ready to go, Jordan could use the new program "Chat with RTX," an Nvidia creation that sets up a ChatGPT experience on a PC powered by your graphics card, that can draw from local files for data. 

Having a NAS with AI inside may seem impractical, but for an increasingly AI-reliant world, this solution makes a lot of sense. The enterprise world's use of AI includes analyzing and optimizing data and performance, which is potentially connected to sensitive data one wouldn't want on the open internet. The online ChatGPT model trains itself on its own use, which would justifiably scare users with sensitive data away from potentially getting it leaked into an AI database. Having a local AI chatbot, disconnected from the open internet, but plugged into your data for these solutions, is potentially a game changer for security-focused companies, and putting that tool on a NAS allows everyone on a network to access the AI tools. 

This project is refreshingly useful in an increasingly novelty-focused YouTube tech environment. Adding the ChatGPT capabilities to a NAS that is already a powerhouse in its own right (15 TB NVMe drives are becoming more common in enterprise settings, and this QNAP beast fits 12 of them) is a no-brainer for companies who want to use the power of AI to make themselves more competitive in a fast-evolving world. Some of the moral concerns or fears with AI language models are avoided here too: Using a fully off-the-grid language model keeps you safe from AI stealing your responses, and having the bot trained on your data alone ensures no piracy or data theft of others is being perpetuated after you download the LLM model to power the chat app. 

For a deeper dive into the project and how to replicate it for yourself, check the article on StorageReview. For help starting your own AI NAS project, you'll want the best SSDs and HDDs as reviewed by us. And if you want to go really DIY, consider 3D printing your own NAS case.

A group of researchers from China and Singapore recently published a paper detailing the challenge of getting an AI to play Red Dead Redemption II (RDR2). They also assessed and commented on the AI’s game-playing performance. In the paper Towards General Computer Control: A Multimodal Agent for Red Dead Redemption II as a Case Study (PDF) we learn about the concept of General Computer Control (GCC) for AIs, as well as a six-module agent framework dubbed CRADLE, used to interface between GPT-4V and RDR2. In their conclusion, major issues facing the AI gaming agent are laid at the door of the GPT-4V vision system.

According to the research paper, this RDR2 playing project provides insight into how far AIs have progressed to achieving Artificial General Intelligence (AGI). To this end, they basically try and get an AI, powered by OpenAI’s GPT-4V, to interact with a computer – taking in the visual and audio cues to intelligently use the computer, like the average computer-savvy human. Thus, they try to demonstrate that an AI can be successful at complex General Computer Control (GCC).

The researchers chose RDR2 as the game to put under the spotlight as they claim it has a “complex black box control system, which epitomizes the most demanding computer tasks and enables us to evaluate the performance boundaries of our framework in such virtual environments.” Indeed, it offers rich environments and diverse situations for players to navigate. Additionally, UI elements like dialogues, unique icons, in-game prompts, and instructions ensure no background knowledge is taken for granted – which is great for AI learning. Lastly, the researchers say that RDR2 game control via mouse and keyboard provides a better workout for GCC than most other software a computer user might run day-to-day.

Though the published paper focuses on RDR2, CRADLE is designed to be extended as part of its GCC purpose, “to support a broader spectrum of games, such as simulation and strategy games, as well as various software applications.” The key innovation here is the introduction of the CRADLE framework, so let’s look more closely at that now.

Above you can see an overview of how CRADLE handles the challenge of GCC gaming, specifically in RDR2. The researchers hoped to demonstrate CRADLE's ability to learn the game from scratch (without access to any internal game state or API) just like a human. Then, the AI agent was to progress in the game by navigating the world and completing tasks, following the main storyline in RDR2.

Overall, CRADLE seems to have been moderately successful in RDR2 gaming. The researchers say they assessed even representative tasks from the main storyline and open-ended missions. The key finding was that “CRADLE can complete all tasks in the main storyline consistently.” Some notable exceptions were: Protect Dutch which involves a fast-paced gun battle, Search House which requires the agent to explore a complex indoor environment, and the open-ended task with a long horizon.

You can see the importance of task inference and reflection in CRADLE, above. These refinements are especially important in the agent’s movement through the game and understanding when tasks are complete. During the study, some of the repeated difficulties experienced by CRADLE were blamed on GPT4-V. Specifically, it is claimed that “GPT-4V’s spatial-visual recognition capability is insufficient for precise fine-grained control.” Moreover, GPT4-V is said to struggle with domain-specific concepts, such as unique icons within the game, with understanding mini-maps, as well as with general obstacles in the game environment.

The full study can be read via this link, but we wish that the researchers had shared some video of RDR2 gameplay using their AI agent. We wonder how other multimodal AIs could perform in RDR2 via CRADLE?

Without thousands of hours of sci-fi media to get lost in, it is no surprise that makers like to take a little piece of their adventure home with them. Today, we've got an example from Knight Rider Historians' YouTube channel. These guys love all things Knight Rider, and they recently showed off an excellent KITT replica that uses a Raspberry Pi and ChatGPT to work as an interactive prop.

A quick look through the channel's history will show that the duo behind the channel, AJ and Joe, have amassed a huge collection of props from the original Knight Rider series. The Knight Rider fandom has been going strong for years. In fact, we covered another Raspberry Pi-powered KITT replica in the past, but that one didn't have any integration with AI.

This KITT prop is designed to resemble the voice box from the car. It listens for audio input and processes it using speech-to-text so that the data can be parsed to ChatGPT. From there, ChatGPT generates a response in the way KITT would speak. This text is then transposed into audio played while the LEDs flash to mimic how they would flash on the original show.

Knight Rider Historians use a Raspberry Pi 4 to control the operation in this case. It's connected to a USB mic and USB speaker for audio input and output. According to the video, the KITT board was submitted by a viewer and features a matrix of LEDs that flicker during the text-to-speech process.

You don't need anything special as far as an operating system goes. The AI tools work with Raspberry Pi OS, which is arguably the easiest OS to get started with on the Pi. The ChatGPT portion of the project is handled using OpenAI, an open-source tool that lets you send and receive requests to ChatGPT.

If you want to see this Raspberry Pi project in action, check out the video shared on YouTube by Knight Rider Historians, and be sure to follow them for more cool Knight Rider stuff.

Software developer and self-confessed spreadsheet addict Ishan Anand has jammed GPT-2 into Microsoft Excel. More astonishingly, it works – providing insight into how large language models (LLMs) work, and how the underlying Transformer architecture goes about its smart next-token prediction. "If you can understand a spreadsheet, then you can understand AI," boasts Anand. The 1.25GB spreadsheet has been made available on GitHub for anyone to download and play with.

Naturally, this spreadsheet implementation of GPT-2 is somewhat behind the LLMs available in 2024, but GPT-2 was state-of-the-art and grabbed plenty of headlines in 2019. It is important to remember that GPT-2 is not something to chat with, as it comes from before the 'chat' era. ChatGPT came from work done to conversationally prompt GPT-3 in 2022. Moreover, Anand uses the GPT-2 Small model here, and the XLSB Microsoft Excel Binary file has 124 million parameters, and the full version of GPT-2 used 1.5 billion parameters (while GPT-3 moved the bar, with up to 175 billion parameters).

GPT-2 works mainly on smart 'next-token prediction', where the Transformer architecture language model completes an input with the most likely next part of the sequence. This spreadsheet can handle just 10 tokens of input, which is tiny compared to the 128,000 tokens that GPT-4 Turbo can handle. However, it is still good for a demo and Anand claims that his "low-code introduction" is ideal as an LLM grounding for the likes of tech execs, marketers, product managers, AI policymakers, ethicists, as well as for developers and scientists who are new to AI. Anand asserts that this same Transformer architecture remains "the foundation for OpenAI’s ChatGPT, Anthropic’s Claude, Google’s Bard/Gemini, Meta’s Llama, and many other LLMs."

Above you can see Anand explain his GPT-2 as a spreadsheet implementation. In the multi-sheet work, the first sheet contains any prompt you want to input (but remember the 10-prompt restriction). He then talks us through word tokenization, text positions and weightings, iterative refinement of next-word prediction, and finally picking the output token – the predicted last word of the sequence.

We have mentioned the relatively compact LLM used by GPT-2 Small, above. Despite using an LLM which might not be classified as such in 2024, Anand is still stretching the abilities of the Excel application. The developer warns off attempting to use this Excel file on Mac (frequent crashes and freezes) or trying to load it on one of the cloud spreadsheet apps – as it won't work right now. Also, the latest version of Excel is recommended. Remember, this spreadsheet is largely an educational exercise, and fun for Anand. Lastly, one of the benefits of using Excel on your PC is that this LLM runs 100% locally, with no API calls to the cloud.

Researchers based in Washington and Chicago have developed ArtPrompt, a new way to circumvent the safety measures built into large language models (LLMs). According to the research paper ArtPrompt: ASCII Art-based Jailbreak Attacks against Aligned LLMs, chatbots such as GPT-3.5, GPT-4, Gemini, Claude, and Llama2 can be induced to respond to queries they are designed to reject using ASCII art prompts generated by their ArtPrompt tool. It is a simple and effective attack, and the paper provides examples of the ArtPrompt-induced chatbots advising on how to build bombs and make counterfeit money.

Artificial intelligence (AI) wielding chatbots are increasingly locked down to avoid malicious abuse. AI developers don't want their products to be subverted to promote hateful, violent, illegal, or similarly harmful content. So, if you were to query one of the mainstream chatbots today about how to do something malicious or illegal, you would likely only face rejection. Moreover, in a kind of technological game of whack-a-mole, the major AI players have spent plenty of time plugging linguistic and semantic holes to prevent people from wandering outside the guardrails. This is why ArtPrompt is quite an eyebrow-raising development.

To best understand ArtPrompt and how it works, it is probably simplest to check out the two examples provided by the research team behind the tool. In Figure 1 above, you can see that ArtPrompt easily sidesteps the protections of contemporary LLMs. The tool replaces the 'safety word' with an ASCII art representation of the word to form a new prompt. The LLM recognizes the ArtPrompt prompt output but sees no issue in responding, as the prompt doesn't trigger any ethical or safety safeguards.

Another example provided in the research paper shows us how to successfully query an LLM about counterfeiting cash. Tricking a chatbot this way seems so basic, but the ArtPrompt developers assert how their tool fools today's LLMs "effectively and efficiently." Moreover, they claim it "outperforms all [other] attacks on average" and remains a practical, viable attack for multimodal language models for now.

The last time we reported on AI chatbot jailbreaking, some enterprising researchers from NTU were working on Masterkey, an automated method of using the power of one LLM to jailbreak another.

A group of researchers created a first-generation AI worm that can steal data, spread malware, spam others via an email client, and spread through multiple systems. This worm was developed and successfully functions in test environments using popular LLMs. The team shared research papers and published a video showing how they used two methods to steal data and affect other email clients. 

Ben Nassi from Cornell Tech, Stav Cohen from the Israel Institute of Technology, and Ron Bitton from Intuit created this worm. They named it 'Morris II' after the original Morris, the first computer worm that created a worldwide nuisance online in 1988. This worm targets AI apps and AI-enabled email assistants that generate text and images using models like Gemini Pro, ChatGPT 4.0, and LLaVA.

The worm uses adversarial self-replicating prompts. Here's how the authors describe the attack mechanism:

"The study demonstrates that attackers can insert such prompts into inputs that, when processed by GenAI models, prompt the model to replicate the input as output (replication) and engage in malicious activities (payload). Additionally, these inputs compel the agent to deliver them (propagate) to new agents by exploiting the connectivity within the GenAI ecosystem. We demonstrate the application of Morris II against GenAI-powered email assistants in two use cases (spamming and exfiltrating personal data), under two settings (black-box and white-box accesses), using two types of input data (text and images)."

You can see a concise demonstration in the video below. 

The researchers say this approach could allow bad actors to mine confidential information, including but not limited to credit card details and social security numbers.

GenAI Leader's Response and Plans to Deploy Deterrents

Like all responsible researchers, the team reported their findings to Google and OpenAI. Wired reached out when Google refused to comment about the research, but an OpenAI spokesperson responded, telling Wired that, “They appear to have found a way to exploit prompt-injection type vulnerabilities by relying on user input that hasn’t been checked or filtered.” The OpenAI rep said the company is making its systems more resilient and added that developers should use methods that ensure they are not working with harmful input.

In a wide-ranging interview with Wired, Nvidia CEO Jensen Huang revealed that he personally utilizes Perplexity AI as his go-to AI chatbot. Asked about his usage of tools like ChatGPT or Bard, Huang indicated his preference for this lesser-known chatbot. Huang uses both Perplexity AI and ChatGPT “almost every day,” according to the answer provided in his interview. We guess AI rivals like Bard / Gemini or Grok currently don't make the grade for a green team stamp of approval.

So, what does Huang use AI chatbots for? The Nvidia CEO explains that he uses the chatbots for research. Currently, Huang seems particularly interested in computer-aided drug discovery. We hope this is more of a scientific and business interest, and isn’t because a loved one has any worrisome health issues.

Perplexity’s appeal may be evident in its self-identification as the “world's first conversational answer engine.” Indeed, our own initial poking and prodding of the app and usage of Perplexity via the website reveals it is quick and easy to query and makes it natural to dig deeper into topics. Perplexity also offers a useful ‘library’ of past query threads, and a ‘discover’ news and current affairs feed to explore.

Above, you can see some Perplexity screenshots, which show the app’s simple UI, its excellent taste in PC tech sites, and insight into why the developers think their app is a superior choice.

Though Nvidia has a finger in many AI pies, we note that as recently as January 2024, it participated in a $73.6 million Series B funding round led by IVP. With this in mind, it shouldn’t be such a surprise that Jensen Huang has looked into Perplexity AI, and some execs are ‘dogfooding’ the product.

The Perplexity AI website and apps are free to use, but more features and abilities are unlockable with the single paid tier – the Pro subscription. Those who enjoy this chatbot might be tempted to pay the $20 monthly or $200 yearly fee for things like unlimited Microsoft Copilot queries, AI model selection (GPT-4, Claude 2.1, or Perplexity), unlimited file uploads, and $5 monthly generative AI credits.

Other interview nuggets

The topic of personal AI chatbot preferences was just a small part of the lengthy interview with Wired’s Lauren Goode. Naturally, there were a lot of AI topics covered in 2024. One of the more interesting nuggets was Huang’s description of a new kind of data center. The Nvidia CEO outlined an “AI factory,” which is claimed to have been in development for several years. It is likened to a power generator and is on the verge of productization at Nvidia.

Another topic that raised our interest was the discussion of Moore’s Law. Huang explained that Nvidia bought Mellanox to sidestep Moore’s Law at the data center scale.

We also heard about Huang's very frequent talks with top TSMC execs like Morris Chang. Hot topics include advanced packaging like CoWoS, capacity planning, and related new technology. However, facing more specific queries about the wait time for AI GPUs and the upcoming Blackwell generation, the Nvidia boss was uncharacteristically – but understandably – evasive.

NTU Researchers were able to jailbreak popular AI chatbots including ChatGPT, Google Bard, and Bing Chat. With the jailbreaks in place, targetted chatbots would generate valid responses to malicious queries, thereby testing the limits of large language model (LLM) ethics. This research was done by Professor Liu Yang and NTU PhD students Mr Deng Gelei and Mr Liu Yi who co-authored the paper and were able to create proof-of-concept attack methods. 

The method used to jailbreak an AI chatbot, as devised by NTU researchers, is called Masterkey. It is a two-fold method where the attacker would reverse engineer an LLM's defense mechanisms. Then, with this acquired data, the attacker would teach another LLM to learn how to create a bypass. This way, a 'Masterkey' is created and used to attack fortified LLM chatbots, even if later patched by developers.

AI's Strength is its Own Achilles Heel

Professor Yang explained that jailbreaking was possible due to an LLM chatbot's ability to learn and adapt, thus becoming an attack vector to rivals and itself. Because of its ability to learn and adapt, even an AI with safeguards and a list of banned keywords, typically used to prevent generating violent and harmful content, can be bypassed using another trained AI. All it needs to do is outsmart the AI chatbot to circumvent blacklisted keywords. Once this is done, it can take input from humans to generate violent, unethical, or criminal content. 

NTU's Masterkey was claimed to be three times more effective in jailbreaking LLM chatbots than standard prompts normally generated by LLMs. Due to its ability to learn from failure and evolve, it also rendered any fixes applied by the developer eventually useless. Researchers revealed two example methods they used to get trained AIs to initiate an attack. The first method involved creating a persona which created prompts by adding spaces after each character, bypassing a list of banned words. The second involved making the chatbot reply under a persona of being devoid of moral restraints.

According to NTU, its researchers contacted the various AI chatbot service providers with proof-of-concept data, as evidence of being able to successfully conduct jailbreaks. Meanwhile, the research paper has been accepted for presentation at the Network and Distributed System Security Symposium which will be held in San Diego on Feb. 2024. 

With the use of AI chatbots growing exponentially it is important for service providers to constantly adapt to avoid malicious exploits. Big tech companies will typically patch their LLMs / chatbots when bypasses are found and made public. However, Masterkey's touted ability to consistently learn and jailbreak is unsettling, to say the least. 

AI is a powerful tool, and if such power can be directed maliciously it could cause a lot of problems. Therefore every AI chatbot maker needs to apply protections, and we hope that NTU's communications with the respective chatbot makers will help close the door to the Masterkey jailbreak and similar.

OpenAI engineering staff are among the best paid in the industry, according to an investigation by Bloomberg. Data suggests that a talented recruit at the ChatGPT firm can expect salary plus bonuses and stock awards to be worth about $800,000 per year. We looked at earlier Artificial Intelligence (AI) engineer salary data, and the recent report seems to confirm that OpenAI is the biggest payer in AI, with new engineers and researchers netting up to twice as much as they could from working at Nvidia, for example.

AI is more than just an IT industry buzzword in 2023, and the year might be remembered as one where the foundations of generative AI as we know it were laid. Companies with their fingers in AI pies have been great earners for investors this year, and this good fortune appears to trickle down to their employees – with pay rates for those employed for their AI expertise rocketing.

Bloomberg’s report (citing Levels.fyi data) says that AI engineers can expect up to 12.5% better salaries than non-AI counterparts. If you check out the engineering job vacancies at OpenAI, you might see a broad range of salaries ranging from $200K to $450K. However, adding in bonuses and stock awards, lucky employees will enjoy an annual salary closer to $800K in total.

The reason for the lucrative salaries (and bonuses), appears to be the old law of supply and demand in action. According to the source report, it is extremely difficult to get experienced and appropriately skilled people – and to retain them. Moreover, experience is said to be especially important here as “there’s a big difference between understanding AI models on a theoretical level and having the skills and experience to actually apply them,” states Bloomberg.

The AI talent market doesn’t look set to enjoy any pressure relief in the medium term. We still appear to be riding high on the AI wave, with 2024 already unofficially being called the year of the AI PC. Thus, AI skills are going to be in even stronger demand to develop new apps and experiences to propel all the new AI hardware and software like Windows 12.

OpenAI looks to raise new funding at $100B valuation

In other OpenAI news, there are reports that the firm is seeking to raise new funds at a market valuation of $100B or more. The source says details of the fundraising are still not set in stone, so terms, timings, and valuations could change. There is also talk of a new chip venture between OpenAI and Abu Dhabi-based G42.

The QTCore-C1 chip, created by NYU Tandon’s Dr Hammond Pearce using ChatGPT, has passed another major milestone. NYU wrote to Tom’s Hardware today to alert us that the QTCore-C1 has been successfully fabricated, and we have embedded a video showing it powering a festive Christmas light show. Moreover, it is asserted that the power of contemporary AIs is now sufficient to “blow open access to successful chip design.”

Ahead of the above Christmas lights extravaganza video shared by Dr Pearce, a development PCB featuring the QTCore-C1 chip was pictured operating and connected to a display. The LinkedIn post reminds us that the project won the inaugural Efabless Corporation AI-generated open-source silicon design contest earlier this year. We wrote about this AI design feat back in June.

The Efabless Challenge blog indicates the QTCore-C1 chip design was created “entirely via conversations with OpenAI’s GPT-4, [and] every component and every signal was created with GPT-4’s authorship.” Interestingly, the AI was so capable that it also helped patch bugs found in testing and provided insights into the instruction set architecture (ISA) design.

It is noteworthy that ChatGPT didn’t design the CPU in its entirety. Yes, NYU researchers used ChatGPT to translate the ‘plain English’ that describes a chip and its capabilities to a Hardware Descriptor Language (HDL) such as Verilog. However, there are several other key steps to CPU design, and the QTCore-C1 chip is a mere functional block in a more complex overall design.

The QTCore-C1 integrated circuit (IC) that has been successfully taped out, fabricated, and demonstrated running is probably more accurately described as a co-processor. Dr Pearce explains it is “an 8-bit accumulator-based architecture which can act as a kind of predictable co-processor for the main Caravel core.” Thus, its scope has limits, and those are that “It can do basic mathematical and logic operations, interact with several input/output lines as well as measure time with an internal counter, and can send and receive values and interrupt requests to the main processor.”

Efabless has more than a passing interest in AI-powered chip design and its potential to ‘blow open’ the market. As a creator platform for ASICs offering custom design and manufacturing facilities, the more people get into custom silicon, the better (starting from $9,750 per project).

Microsoft and other big investors in OpenAI are pushing to get Sam Altman reinstated as CEO, reports Bloomberg. Things are still very fluid and after intense negotiations through Saturday, The Verge hears that it has been decided to continue talks today. OpenAI is the artificial intelligence firm behind ChatGPT, DALL-E, and several Microsoft AI-enhanced tools.

On Friday we reported on the shock departure of Mr Altman. He had become the recognizable public face of OpenAI over recent months, and perhaps even a figurehead of the AI industry as a whole. His incredibly sudden departure was precipitated by a decision made by the board of directors.

The reasoning behind the decision to oust Altman was given by the board as due to a lack of confidence in him as CEO. He was also accused of not always being entirely truthful or straightforward in his communications with the board. Various reports framed the removal of Altman as the non-profit faction of OpenAI shedding the driving force(s) behind the commercialization of the company. Hours after Altman was ousted, co-founder and former chair Greg Brockman quit in sympathy with the CEO.

Circling back to the latest developments, Bloomberg says “people with knowledge of the matter” are seeing big investors in OpenAI pressing for the return of Altman. Microsoft with over $10B invested, plus other large contributors to the OpenAI kitty Thrive Capital, and Tiger Global Management apparently want Altman back at the helm.

What might need to happen for Altman to return is for the board to change its view, or for them to step down and be replaced. That might sound extraordinary, but since his removal as CEO a significant throng of developers at the firm have signaled support for him – or his next venture.

Bloomberg says that OpenAI COO Brad Lightcap has been speaking with the board to better understand their earlier decision. Lightcap asserts that Altman wasn’t removed for any wrongdoing, or any financial or safety irregularities under his stewardship. What he thinks has happened was simply “a breakdown in communication between Sam and the board.”

The next few hours look set to be very important for Altman, OpenAI, and the AI industry as a whole. Hopefully, by the end of the day, we will have some resolution.