evergreens - Faz Business | فاز الأعمال

How to tell if your AI platforms’ accounts have been hacked

Posted on August 15, 2026 by faz_business

Just like any other online service, hackers can target and break into your accounts on popular AI platforms such as ChatGPT, Claude, and Perplexity.

TechCrunch has created a comprehensive guide to help you protect yourself if you suspect someone has broken into your account on one of the internet’s most popular platforms, social networks, or messaging apps. Now, we’re here to show you how to check whether your accounts on AI platforms have been hacked.

As usual, we recommend using unique passwords stored in a password manager, and turning on multi-factor authentication (MFA), so that even if someone steals your password, they won’t be able to log in without that second piece of information.

ChatGPT and Perplexity offer MFA. Claude doesn’t, because instead of asking for a password, Anthropic’s AI chatbot sends a login link to your email address.

All three of these AI platforms offer similar ways to check if there’s a suspicious device logged into your account. Here’s exactly how each platform works.

ChatGPT

To find out if someone has broken into your ChatGPT account, open it on your computer’s browser, click on your username in the bottom left corner, go to “Settings,” then “Security and Login,” and finally click on “Active Sessions.”

You will see where you are logged into your ChatGPT account. If you see any device you don’t recognize, you can log out of that single device. You can also click on “Log out all.”

**Image Credits:**Screenshot/TechCrunch /

At this point, if you want to change your password, you need to log out of your account.

Then, on ChatGPT’s website, click “Log in” located in the bottom-left corner, enter your email address, click on “Forgot password,” and then “Continue.”

ChatGPT will then send you an email containing a six-digit code. Enter the code on the ChatGPT login page, click “Continue,” and then enter a new password.

You can also click “reset your password” in the email you received to see the official instructions on how to do that.

Claude

For Claude, open it in your computer’s browser, click on your username in the bottom-left corner, then “Settings,” and click on “Account.” That’s where you will see your “Active sessions.”

If you don’t recognize one of them, hover over it, click on the three vertical dots that appear on the right, and click “Log out” or “Terminate.”

If you want, you can click on “Log out of all devices.”

At that point, you’ll be able to log back into your account using your email address. You will receive an email with a link to log in. Claude does not allow you to use passwords at all, so there’s no password to change.

Perplexity

In the case of Perplexity, the AI-powered search engine does not show you where you are logged in.

So if you’re worried someone may have broken into your account, go to Perplexity in your browser and click your username in the bottom-left corner, then “All settings.” Finally, click on “Sign out of all sessions,” and then “Confirm.”

At that point, you can log back in by entering your email address. You will then receive an email with a unique six-digit code. Enter the code on the website to log in, or click on the “Sign in” button in the email to log in directly.

When you purchase through links in our articles, we may earn a small commission. This doesn’t affect our editorial independence.

The only AI glossary you’ll need this year

Posted on July 4, 2026 by faz_business

Artificial intelligence is rewriting the world, and simultaneously inventing a whole new language to describe how it’s doing it. Sit in on any product meeting, pitch, or panel these days, and you’ll hear people toss around LLMs, RAG, RLHF, and a dozen other terms that can make even very smart people in the tech world feel a little insecure. This glossary is our attempt to fix that: pain-English definitions of the AI terms you’re most likely to actually run into, whether you’re building with this stuff, investing in it, or just trying to keep up by reading TechCrunch or listening to related podcasts. We update it regularly as the field evolves, so consider it a living document, much like the AI systems it describes.

Artificial general intelligence, or AGI, is a nebulous term. But it generally refers to AI that’s more capable than the average human at many, if not most, tasks. OpenAI CEO Sam Altman once described AGI as the “equivalent of a median human that you could hire as a co-worker.” Meanwhile, OpenAI’s charter defines AGI as “highly autonomous systems that outperform humans at most economically valuable work.” Google DeepMind’s understanding differs slightly from these two definitions; the lab views AGI as “AI that’s at least as capable as humans at most cognitive tasks.” Confused? Not to worry — so are experts at the forefront of AI research.

An AI agent refers to a tool that uses AI technologies to perform a series of tasks on your behalf — beyond what a more basic AI chatbot could do — such as filing expenses, booking tickets or a table at a restaurant, or even writing and maintaining code. However, as we’ve explained before, there are lots of moving pieces in this emergent space, so “AI agent” might mean different things to different people. Infrastructure is also still being built out to deliver on its envisaged capabilities. But the basic concept implies an autonomous system that may draw on multiple AI systems to carry out multistep tasks.

Think of API endpoints as “buttons” on the back of a piece of software that other programs can press to make it do things. Developers use these interfaces to build integrations — for example, allowing one application to pull data from another, or enabling an AI agent to control third-party services directly without a human manually operating each interface. Most smart home devices and connected platforms have these hidden buttons available, even if ordinary users never see or interact with them. As AI agents grow more capable, they are increasingly able to find and use these endpoints on their own, opening up powerful — and sometimes unexpected — possibilities for automation.

Given a simple question, a human brain can answer without even thinking too much about it — things like “which animal is taller, a giraffe or a cat?” But in many cases, you often need a pen and paper to come up with the right answer because there are intermediary steps. For instance, if a farmer has chickens and cows, and together they have 40 heads and 120 legs, you might need to write down a simple equation to come up with the answer (20 chickens and 20 cows).

In an AI context, chain-of-thought reasoning for large language models means breaking down a problem into smaller, intermediate steps to improve the quality of the end result. It usually takes longer to get an answer, but the answer is more likely to be correct, especially in a logic or coding context. Reasoning models are developed from traditional large language models and optimized for chain-of-thought thinking thanks to reinforcement learning.

(See: Large language model)

This is a more specific concept that an “AI agent,” which means a program that can take actions on its own, step by step, to complete a goal. A coding agent is a specialized version applied to software development. Rather than simply suggesting code for a human to review and paste in, a coding agent can write, test, and debug code autonomously, handling the kind of iterative, trial-and-error work that typically consumes a developer’s day. These agents can operate across entire codebases, spotting bugs, running tests, and pushing fixes with minimal human oversight. Think of it like hiring a very fast intern who never sleeps and never loses focus — though, as with any intern, a human still needs to review the work.

Although somewhat of a multivalent term, compute generally refers to the vital computational power that allows AI models to operate. This type of processing fuels the AI industry, giving it the ability to train and deploy its powerful models. The term is often a shorthand for the kinds of hardware that provides the computational power — things like GPUs, CPUs, TPUs, and other forms of infrastructure that form the bedrock of the modern AI industry.

A subset of self-improving machine learning in which AI algorithms are designed with a multi-layered, artificial neural network (ANN) structure. This allows them to make more complex correlations compared to simpler machine learning-based systems, such as linear models or decision trees. The structure of deep learning algorithms draws inspiration from the interconnected pathways of neurons in the human brain.

Deep learning AI models are able to identify important characteristics in data themselves, rather than requiring human engineers to define these features. The structure also supports algorithms that can learn from errors and, through a process of repetition and adjustment, improve their own outputs. However, deep learning systems require a lot of data points to yield good results (millions or more). They also typically take longer to train compared to simpler machine learning algorithms — so development costs tend to be higher.

(See: Neural network)

Diffusion is the tech at the heart of many art-, music-, and text-generating AI models. Inspired by physics, diffusion systems slowly “destroy” the structure of data — for example, photos, songs, and so on — by adding noise until there’s nothing left. In physics, diffusion is spontaneous and irreversible — sugar diffused in coffee can’t be restored to cube form. But diffusion systems in AI aim to learn a sort of “reverse diffusion” process to restore the destroyed data, gaining the ability to recover the data from noise.

Distillation is a technique used to extract knowledge from a large AI model with a ‘teacher-student’ model. Developers send requests to a teacher model and record the outputs. Answers are sometimes compared with a dataset to see how accurate they are. These outputs are then used to train the student model, which is trained to approximate the teacher’s behavior.

Distillation can be used to create a smaller, more efficient model based on a larger model with a minimal distillation loss. This is likely how OpenAI developed GPT-4 Turbo, a faster version of GPT-4.

While all AI companies use distillation internally, it may have also been used by some AI companies to catch up with frontier models. Distillation from a competitor usually violates the terms of service of AI API and chat assistants.

This refers to the further training of an AI model to optimize performance for a more specific task or area than was previously a focal point of its training — typically by feeding in new, specialized (i.e., task-oriented) data.

Many AI startups are taking large language models as a starting point to build a commercial product but are vying to amp up utility for a target sector or task by supplementing earlier training cycles with fine-tuning based on their own domain-specific knowledge and expertise.

(See: Large language model [LLM])

A GAN, or Generative Adversarial Network, is a type of machine learning framework that underpins some important developments in generative AI when it comes to producing realistic data — including (but not only) deepfake tools. GANs involve the use of a pair of neural networks, one of which draws on its training data to generate an output that is passed to the other model to evaluate.

The two models are essentially programmed to try to outdo each other. The generator is trying to get its output past the discriminator, while the discriminator is working to spot artificially generated data. This structured contest can optimize AI outputs to be more realistic without the need for additional human intervention. Though GANs work best for narrower applications (such as producing realistic photos or videos), rather than general purpose AI.

Hallucination is the AI industry’s preferred term for AI models making stuff up — literally generating information that is incorrect. Obviously, it’s a huge problem for AI quality.

Hallucinations produce GenAI outputs that can be misleading and could even lead to real-life risks — with potentially dangerous consequences (think of a health query that returns harmful medical advice).

The problem of AIs fabricating information is thought to arise as a consequence of gaps in training data. Hallucinations are contributing to a push toward increasingly specialized and/or vertical AI models — i.e. domain-specific AIs that require narrower expertise — as a way to reduce the likelihood of knowledge gaps and shrink disinformation risks.

Inference is the process of running an AI model. It’s setting a model loose to make predictions or draw conclusions from previously seen data. To be clear, inference can’t happen without training; a model must learn patterns in a set of data before it can effectively extrapolate from this training data.

Many types of hardware can perform inference, ranging from smartphone processors to beefy GPUs to custom-designed AI accelerators. But not all of them can run models equally well. Very large models would take ages to make predictions on, say, a laptop versus a cloud server with high-end AI chips.

[See: Training]

Large language models, or LLMs, are the AI models used by popular AI assistants, such as ChatGPT, Claude, Google’s Gemini, Meta’s AI Llama, Microsoft Copilot, or Mistral’s Le Chat. When you chat with an AI assistant, you interact with a large language model that processes your request directly or with the help of different available tools, such as web browsing or code interpreters.

LLMs are deep neural networks made of billions of numerical parameters (or weights, see below) that learn the relationships between words and phrases and create a representation of language, a sort of multidimensional map of words.

These models are created from encoding the patterns they find in billions of books, articles, and transcripts. When you prompt an LLM, the model generates the most likely pattern that fits the prompt.

(See: Neural network)

Memory cache refers to an important process that boosts inference (which is the process by which AI works to generate a response to a user’s query). In essence, caching is an optimization technique, designed to make inference more efficient. AI is obviously driven by high-octane mathematical calculations and every time those calculations are made, they use up more power. Caching is designed to cut down on the number of calculations a model might have to run by saving particular calculations for future user queries and operations. There are different kinds of memory caching, although one of the more well-known is KV (or key value) caching. KV caching works in transformer-based models, and increases efficiency, driving faster results by reducing the amount of time (and algorithmic labor) it takes to generate answers to user questions.

(See: Inference)

Model Context Protocol, or MCP, is an open standard that lets AI models connect to outside tools and data — your files, databases, or apps like Slack and Google Drive — without a developer building a custom connector for every single pairing. Think of it as a USB-C port for AI. Anthropic introduced MCP in 2024 and later handed it over to the Linux Foundation, and it’s since been adopted by OpenAI, Google, and Microsoft, making it one of the fastest-spreading standards in recent AI history.

Mixture of Experts is a model architecture that splits a neural network into many smaller specialized sub-networks, or “experts,” and only activates a handful of them for any given task. Rather than routing every request through the entire model — like calling in your whole office for every question — an MoE model has a built-in “router” that picks just the right specialists for the job. This makes it possible to build enormous models that stay relatively fast and cheap to run, since only a fraction of the network is doing work at any one time. Mistral AI’s Mixtral model is a well-known example; OpenAI’s newer GPT models are also widely believed to use some version of this approach, though the company has never officially confirmed it.

(See: Neural network, Deep learning)

A neural network refers to the multi-layered algorithmic structure that underpins deep learning — and, more broadly, the whole boom in generative AI tools following the emergence of large language models.

Although the idea of taking inspiration from the densely interconnected pathways of the human brain as a design structure for data processing algorithms dates all the way back to the 1940s, it was the much more recent rise of graphical processing hardware (GPUs) — via the video game industry — that really unlocked the power of this theory. These chips proved well suited to training algorithms with many more layers than was possible in earlier epochs — enabling neural network-based AI systems to achieve far better performance across many domains, including voice recognition, autonomous navigation, and drug discovery.

(See: Large language model [LLM])

Open source refers to software — or, increasingly, AI models — where the underlying code is made publicly available for anyone to use, inspect, or modify. In the AI world, Meta’s Llama family of models is a prominent example; Linux is the famous historical parallel in operating systems. Open source approaches allow researchers, developers, and companies around the world to build on top of one another’s work, accelerating progress and enabling independent safety audits that closed systems cannot easily provide. Closed source means the code is private — you can use the product but not see how it works, as is the case with OpenAI’s GPT models — a distinction that has become one of the defining debates in the AI industry.

Parallelization means doing many things at the same time instead of one after another — like having 10 employees working on different parts of a project at the same time instead of one employee doing everything sequentially. In AI, parallelization is fundamental to both training and inference: modern GPUs are specifically designed to perform thousands of calculations in parallel, which is a big reason why they became the hardware backbone of the industry. As AI systems grow more complex and models grow larger, the ability to parallelize work across many chips and many machines has become one of the most important factors in determining how quickly and cost-effectively models can be built and deployed. Research into better parallelization strategies is now a field of study in its own right.

RAMageddon is the fun new term for a not-so-fun trend that is sweeping the tech industry: an ever-increasing shortage of random access memory, or RAM chips, which power pretty much all the tech products we use in our daily lives. As the AI industry has blossomed, the biggest tech companies and AI labs — all vying to have the most powerful and efficient AI — are buying so much RAM to power their data centers that there’s not much left for the rest of us. And that supply bottleneck means that what’s left is getting more and more expensive.

That includes industries like gaming (where major companies have had to raise prices on consoles because it’s harder to find memory chips for their devices), consumer electronics (where memory shortage could cause the biggest dip in smartphone shipments in more than a decade), and general enterprise computing (because those companies can’t get enough RAM for their own data centers). The surge in prices is only expected to stop after the dreaded shortage ends but, unfortunately, there’s not really much of a sign that’s going to happen anytime soon.

Like AGI, recursive self-improvement is a threshhold for how smart AI can get, and how little it may rely on humans. In the RSI scenario, AI models start improving themselves without human intervention, leading to a huge acceleration in capabilities and autonomy. In some tellings, this would be a cataclysmic moment akin to the singularity, a moment when AI models become immune to outside intervention. But RSI also describes a basic capability — can an AI model design its own successor? — which makes it much easier for engineers to try to build it. A number of recent AI startups have set out to build recursively self-improving models, but most of them dismiss the apocalyptic implications, presenting RSI as simply the next frontier for research.

Reinforcement learning is a way of training AI where a system learns by trying things and receiving rewards for correct answers — like training your beloved pet with treats, except the “pet” in this scenario is a neural network and the “treat” is a mathematical signal indicating success. Unlike supervised learning, where a model is trained on a fixed dataset of labeled examples, reinforcement learning lets a model explore its environment, take actions, and continuously update its behavior based on the feedback it receives. This approach has proven especially powerful for training AI to play games, control robots, and, more recently, sharpen the reasoning ability of large language models. Techniques like reinforcement learning from human feedback, or RLHF, are now central to how leading AI labs fine-tune their models to be more helpful, accurate, and safe.

When it comes to human-machine communication, there are some obvious challenges — people communicate using human language, while AI programs execute tasks through complex algorithmic processes informed by data. Tokens bridge that gap: they are the basic building blocks of human-AI communication, representing discrete segments of data that have been processed or produced by an LLM. They are created through a process called tokenization, which breaks down raw text into bite-sized units a language model can digest, similar to how a compiler translates human language into binary code a computer can understand. In enterprise settings, tokens also determine cost — most AI companies charge for LLM usage on a per-token basis, meaning the more a business uses, the more it pays.

So again, tokens are the small chunks of text — often parts of words rather than whole ones — that AI language models break language into before processing it; they are roughly analogous to “words” for the purposes of understanding AI workloads. Throughput refers to how much can be processed in a given period of time, so token throughput is essentially a measure of how much AI work a system can handle at once. High token throughput is a key goal for AI infrastructure teams, since it determines how many users a model can serve simultaneously and how quickly each of them receives a response. AI researcher Andrej Karpathy has described feeling anxious when his AI subscriptions sit idle — echoing the feeling he had as a grad student when expensive computer hardware wasn’t being fully utilized — a sentiment that captures why maximizing token throughput has become something of an obsession in the field.

Developing machine learning AIs involves a process known as training. In simple terms, this refers to data being fed in in order that the model can learn from patterns and generate useful outputs. Essentially, it’s the process of the system responding to characteristics in the data that enables it to adapt outputs toward a sought-for goal — whether that’s identifying images of cats or producing a haiku on demand.

Training can be expensive because it requires lots of inputs, and the volumes required have been trending upwards — which is why hybrid approaches, such as fine-tuning a rules-based AI with targeted data, can help manage costs without starting entirely from scratch.

[See: Inference]

A technique where a previously trained AI model is used as the starting point for developing a new model for a different but typically related task — allowing knowledge gained in previous training cycles to be reapplied.

Transfer learning can drive efficiency savings by shortcutting model development. It can also be useful when data for the task that the model is being developed for is somewhat limited. But it’s important to note that the approach has limitations. Models that rely on transfer learning to gain generalized capabilities will likely require training on additional data in order to perform well in their domain of focus

(See: Fine tuning)

Validation loss is a number that tells you how well an AI model is learning during training — and lower is better. Researchers track it closely as a kind of real-time report card, using it to decide when to stop training, when to adjust hyperparameters, or whether to investigate a potential problem. One of the key concerns it helps flag is overfitting, a condition in which a model memorizes its training data rather than truly learning patterns it can generalize to new situations. Think of it as the difference between a student who genuinely understands the material and one who simply memorized last year’s exam — validation loss helps reveal which one your model is becoming.

Weights are core to AI training, as they determine how much importance (or weight) is given to different features (or input variables) in the data used for training the system — thereby shaping the AI model’s output.

Put another way, weights are numerical parameters that define what’s most salient in a dataset for the given training task. They achieve their function by applying multiplication to inputs. Model training typically begins with weights that are randomly assigned, but as the process unfolds, the weights adjust as the model seeks to arrive at an output that more closely matches the target.

For example, an AI model for predicting housing prices that’s trained on historical real estate data for a target location could include weights for features such as the number of bedrooms and bathrooms, whether a property is detached or semi-detached, whether it has parking, a garage, and so on.

Ultimately, the weights the model attaches to each of these inputs reflect how much they influence the value of a property, based on the given dataset.

This article is updated regularly with new information.

When you purchase through links in our articles, we may earn a small commission. This doesn’t affect our editorial independence.

These are the countries moving to ban social media for children

Posted on April 8, 2026 by faz_business

Over the past several months, many countries have announced plans to restrict social media access for children and teens. Australia became the first to implement such measures at the end of last year, setting a precedent that other countries are now closely watching.

Australia’s regulations, along with other countries’ proposals, aim to reduce the pressures and risks that young users may face on social media, which include cyberbullying, addiction, mental health issues, and exposure to predators.

Of course, there are concerns about privacy regarding invasive age verification and excessive government intervention. Critics, including Amnesty Tech, have said such bans are ineffective and that they ignore the realities of younger generations. Despite this, many nations are moving ahead with proposed legislation.

We’ve compiled a list of countries that are considering or have already moved forward with bans on social media for young users.

Australia

Australia became the world’s first country to ban social media for children under 16 in December 2025. The ban blocks children from using Facebook, Instagram, Snapchat, Threads, TikTok, X, YouTube, Reddit, Twitch, and Kick. It notably doesn’t include WhatsApp or YouTube Kids.

The Australian government has said these social media companies must take steps to keep children off their services. Companies that fail to comply may face penalties of up to $49.5 million AUD ($34.4 million USD).

The government says these platforms should use multiple verification methods to ensure that people using their services are older than 16. It also notes that they can’t rely on users simply entering their own age.

Techcrunch event

San Francisco, CA
|
October 13-15, 2026

Denmark

Denmark is set to ban social media platforms for children under 15. The Danish government announced in November 2025 that it had secured support for the ban from three governing coalition parties and two opposition parties in parliament.

The government’s plans could become law as soon as mid-2026, according to the Associated Press. The Danish digital affairs ministry is also launching a “digital evidence” app that includes age verification tools that may be used as part of the ban.

France

In late January, French lawmakers passed a bill that would ban social media for kids under 15. President Emmanuel Macron has supported the measure as a way to protect children from excessive screen time.

The bill still has to get through the country’s Senate before a final vote in the lower house.

Germany

In early February, German Chancellor Friedrich Merz’s conservatives discussed a proposal to bar children under 16 from using social media, Reuters reported. However, there were signs that his center-left coalition partners were hesitant to support an outright ban.

Greece

Greek Prime Minister Kyriakos Mitsotakis announced in April that the country is going to ban access to social media for children under 15 starting January 2027. Mitsotakis says the move is aimed at tacking rising anxiety and sleep problems among children, as well as the addictive design of social media.

Indonesia

Indonesia said in early March that it’s banning children under the age of 16 from using social media and other popular online platforms. The country plans to start with platforms such as YouTube, TikTok, Facebook, Instagram, Threads, X, Bigo Live, and Roblox.

Malaysia

The Malaysian government said in November 2025 that it plans to ban social media for children under 16. The country plans to implement the ban this year.

Slovenia

Slovenia is drafting legislation to prohibit children under 15 from accessing social media, the country’s deputy prime minister announced in early February. The government wants to regulate social networks where content is shared, citing platforms such as TikTok, Snapchat, and Instagram.

Spain

Spain’s prime minister announced in early February that the country plans to ban social media for children under the age of 16. The ban still needs parliamentary approval. The Spanish government is also seeking to create a law that would make social media executives personally accountable for hate speech on their platforms.

UK

The United Kingdom is weighing a ban on social media for children under 16. The government says it will consult parents, young people, and civil society for their views to determine whether a ban would be effective.

It will also consider whether to require social media companies to limit or remove features that drive compulsive use, such as endless scrolling.

This story was originally published in February 2026 and is updated regularly with new information.

How to use the new ChatGPT app integrations, including DoorDash, Spotify, Uber, and others

Posted on March 14, 2026 by faz_business

OpenAI offers app integrations in ChatGPT to allow you to connect your accounts directly to ChatGPT and ask the assistant to do things for you. For instance, with a Spotify integration, you can tell it to create personalized playlists that will show up right in your Spotify app.

To get started, make sure you’re logged into ChatGPT. Then type the name of the app you want to use at the start of your prompt, and ChatGPT will guide you through signing in and connecting your account.

If you want to set everything up at once, head over to the Settings menu, then click on Apps and Connectors. You can browse through the available apps, pick the ones you like, and it’ll take you to the sign-in page for each one.

However, it’s important to note that connecting your account means you’re sharing your app data with ChatGPT. Make sure to review the permissions you’re giving when you’re linking your accounts. For example, if you connect your Spotify account, ChatGPT can see your playlists, listening history, and other personal information. (Sharing this info helps personalize the experience, but if you have privacy concerns, consider whether you’re comfortable with this level of access before connecting.)

You can also disconnect any app whenever you want, right from the Settings menu.

Available apps

Angi

Angi is one of the most recent companies to launch an in-app experience within ChatGPT. The online home service marketplace gives users a way to ask home improvement questions directly in the AI chatbot and request to be matched with an Angi professional in one place. Users can ask about house projects and repair guidance, then request a quote and be directed to Angi, where they can continue the experience with its own AI assistant.

Booking.com

This integration with the online travel giant is designed to help travelers, especially first-time visitors in need of suggestions for where to stay.

Techcrunch event

San Francisco, CA
|
October 13-15, 2026

Once you link your Booking.com account, you can ask ChatGPT to find hotels in your preferred city based on your dates and budget. You can also specify how many people are coming and whether you want the hotel near public transport. ChatGPT aims to make this process more intuitive than searching directly on the Booking.com site. Plus, you can be more specific, like searching for options “with breakfast included.”

When you find a hotel you like, just open the Booking.com listing to complete your reservation.

Canva

Canva in ChatGPT is a helpful tool for graphic designers and anyone else who needs to generate visual content quickly. Whether it’s for a social media post, a poster, or a slide deck for a presentation, this may be a good way to help kickstart your project and brainstorm ideas.

Once you connect your Canva account, you can ask ChatGPT to design something like “a 16:9 slide deck about our Q4 roadmap” or “a fun poster for a dog-walking business.” You can include specifics such as the fonts you prefer, color schemes, formats (like Instagram posts or stories), and exact dimensions.

AI-generated designs are seldom perfect, with occasional distorted images or spelling mistakes. However, some users may find this better than starting from scratch, and they can jump into Canva at any time to tweak their design and make it look just how they want.

Coursera

Coursera’s integration is designed to help you quickly discover the best online courses for your skill level. For instance, you can then tell ChatGPT to find an “intermediate-level course on Python.” You can then tell the chatbot to compare course options by rating, duration, and cost before enrolling. ChatGPT can also provide a quick rundown of what exactly each course covers.

DoorDash

DoorDash introduced its ChatGPT integration in December 2025, which aims to save time on meal planning and grocery shopping. Users can ask the chatbot for a meal plan and instantly add all ingredients to their DoorDash cart, then review and check out.

Currently, this feature is available only to users in the U.S., with participating grocery retailers, including Kroger, Safeway, Fairway Market, Wegmans, and more.

Expedia

ChatGPT can display hotel options and flights via Expedia without leaving chat. Whether you’re looking for a quick escape or a longer trip, it can find flights that fit your travel dates, budget, and number of travelers. You can narrow things down by saying stuff like “Only show 4-star hotels.” Once you see something you like, go to Expedia to finalize everything and book your trip.

Figma

To use Figma in ChatGPT, you can ask it to generate diagrams, flow charts, and more. This is helpful for turning your ideas and brainstorming sessions into something more tangible. It may also be useful for visualizing complex concepts or workflows.

You can also upload files and ask the chatbot to generate a product roadmap for your team. This roadmap can include milestones, deliverables, and deadlines, helping your team stay organized and focused on their goals.

Quizlet

Quizlet recently launched a native app within ChatGPT, allowing students to convert AI conversations, notes, or documents into study materials and flashcard sets. Plus, users can now enter an active practicing mode in ChatGPT, where they can access Quizlet’s library of millions of study sets.

Spotify

One of the most helpful aspects of using Spotify in ChatGPT is the ability to quickly create playlists and listen to new recommended songs tailored to your specific tastes. You can ask it to create a playlist based on your current mood, or just a playlist that only includes tracks by your favorite band.

It can also suggest new artists, playlists, audiobooks, and podcast episodes. Additionally, ChatGPT can perform actions on your behalf, including adding and removing items from your Spotify library.

Target

Retail giant Target strategically launched a beta version of its ChatGPT integration before Black Friday. This feature allows shoppers to ask the chatbot for gift suggestions and quickly create a shopping basket with multiple items without leaving ChatGPT. For example, users can request ideas for a movie night, and the chatbot will provide a curated selection of available Target items. Shoppers can add these items to their cart and make a purchase using their Target account. They can then choose from same-day “Drive Up,” in-store pickup, or standard shipping.

Uber

If you’re planning a trip, the Uber integration makes it easy to find ride options, which is especially useful if you’re in a new country. You can set up your trip in the ChatGPT app, then complete the ride request and payment in the Uber app.

Currently, it’s only available in the U.S., and it doesn’t let you book rides in advance; only on-demand rides are available. You can choose from options like UberX, UberXL, Comfort, and Black.

There’s also an Uber Eats integration for U.S. users, so you can check out local restaurants and menu items within ChatGPT, then finish paying in the Uber Eats app.

Wix

In March 2026, website builder Wix launched its integration, allowing users to prompt ChatGPT to create a functional website with just a text or voice prompt. Users can describe the capabilities they want the website to have, how it should look, and other desired features. Additionally, existing Wix users can manage their business within ChatGPT, which can handle scheduling, payments, SEO, accessibility, performance, security, and more.

Zillow

If you’re looking for a new home, Zillow in ChatGPT could make the search experience more straightforward. Using a simple text prompt, you can find homes that meet your criteria and apply filters to narrow the results. Whether you’re looking for a specific price range, number of bedrooms, or particular neighborhoods, you can specify these details in your prompt, making the search process much more efficient and tailored to your needs.

What’s next?

Alongside the announcement that OpenAI would bring apps into ChatGPT, the company also said it plans to welcome additional partners soon, including OpenTable, PayPal, and Walmart. These will launch in 2026.

The rollout of ChatGPT’s app integrations is currently limited to the U.S. and Canada. Users in Europe and the U.K. are excluded for now.

This story has been updated to include newly launched integrations.

These AI notetaking devices can help you record and transcribe your meetings

Posted on February 2, 2026 by faz_business

Digital meeting notetakers like Read AI, Fireflies.ai, Fathom, and Granola help record and transcribe online meetings. But for in-person or more versatile options, many people prefer physical recording devices These physical notetakers transcribe audio and give users summaries and action items of meetings using AI.

Some of these devices are wearable—pins or pendants with dedicated mics for recording—while others are credit-card sized with dedicated mobile apps to transcribe and extract insights using AI. A few even offer live translation.

Below is a non-exhaustive list of physical AI notetakers and transcription tools.

Plaud Note/Plaud Note Pro

This credit card-sized notetaker has been around since 2023, with a newer, AI-powered Pro version that has a small screen, four mics, and records audio within three to five meters. It also can switch between in-person recording and call recording.

Plaud Note Pro. Image Credis: Ivan MehtaImage Credits:Ivan Mehta

The Plaud Note costs $159, while the Note Pro costs $179. They come with 300 minutes of transcription free per month.

Mobvoi TicNote

Mobvi’s rectangular notetaker is priced at $159 and includes 600 free transcription minutes. The company claims the device shows real-time transcription and translation with support for more than 120 languages. The device offers 25 hours of continuous recording through its three microphones.

In terms of software features, the TicNote offers automatic highlight extraction and the ability to create audio clips or summarized podcast versions of a conversation.

Techcrunch event

Boston, MA
|
June 23, 2026

Comulytic Note Pro

Comulytic is a newer entrant in the hardware AI notetaker market. The company’s claim for differentiation is that its $159 Note Pro device doesn’t require any additional subscription for basic transcription. That means you can transcribe unlimited minutes by just buying the device.

The device can record up to 45 hours of audio continuously on a single charge and has more than 100 days of standby time.

The company has a $15 per month or $119 per year advanced plan that offers instant AI summaries, unlimited templates for summaries, an action item list, and chat with AI assistant without any limits.

Plaud NotePin/Plaud NotePin S

Plaud NotePin and NotePin S are the smaller and more pocketable versions of the company’s larger Note and Note Pro devices. The NotePin has a versatile design: You can wear it as a wrist band, a pendant, clip it to your bag, or wear it on your shirt with a magnetic attachment. Notably, the lanyard and wristband are only available with the NotePin S.

Both devices have two mics, and can record around 20 hours of audio continuously on a single charge. The NotePin S has a physical button to start/stop recording and capture highlights.

Both are similarly priced to their credit-card-shaped counterparts. The NotePin is priced at $159, and the NotePin S is priced at $179.

Omi pendant

The Omi pendant is a cheaper alternative to other notetakers at $89. This is because the pendant has to be connected to your phone and doesn’t have any onboard memory. The device has two mics and can run for 10 to 14 hours on a charge.

While Omi has its own app, you can use other apps as the hardware and software are open-sourced. Users have also built different connectors and apps for the device.

Viaim RecDot

Viaim’s earbuds allow for transcription during calls, with additional recording capabilities in the earbuds’ case. These buds are priced at $200 and Viaim claims they can transcribe audio in up to 78 languages in real-time. The company’s app can also highlight key points in transcriptions.

Anker Soundcore Work

Anker’s Soundcore Work pin is a coin-sized AI notetaker with a puck-shaped battery pack. The $159 device can record for eight hours without breaks, or up to 32 hours if the pin is attached to its case, the company says.

Anker claims that the device has a five-meter recording range. Users get 300 minutes of transcription free per month.

A comprehensive list of 2025 tech layoffs

Posted on October 25, 2025 by faz_business

The tech layoff wave is still kicking in 2025. Last year saw more than 150,000 job cuts across 549 companies, according to independent layoffs tracker Layoffs.fyi. So far this year, more than 22,000 workers have been the victim of reductions across the tech industry, with a staggering 16,084 cuts taking place in February alone.

We’re tracking layoffs in the tech industry in 2025 so you can see the trajectory of the cutbacks and understand the impact on innovation across all types of companies. As businesses continue to embrace AI and automation, this tracker serves as a reminder of the human impact of layoffs — and what could be at stake with increased innovation.

Below you’ll find a comprehensive list of all the known tech layoffs that have occurred in 2025, which will be updated regularly. If you have a tip on a layoff, contact us here. If you prefer to remain anonymous, you can contact us here.

October

Rivian

Is cutting 600 jobs, about 4% of its workforce, amid an EV market pullback, marking its third layoff this year. Details of the latest layoffs remain undisclosed, while earlier cuts in June and September affected 100 to 150 employees in its commercial and manufacturing teams.

Applied Materials

Plans to cut about 4% of its workforce, or roughly 1,400 jobs, to streamline operations amid tighter U.S. semiconductor export controls.

Techcrunch event

San Francisco
|
October 27-29, 2025

Handshake

Laid off around 100 employees in October, about 15% of its 650-person U.S. workforce. The layoffs affected various roles across its recruiting business vertical. The San Francisco-based startup is an online platform connecting college students and recent graduates with employers for early-career jobs.

Smartsheet

Has reportedly laid off over 120 employees amid a leadership transition following CEO Mark Mader’s retirement. The enterprise software company, which grew to more than 3,300 employees, was acquired for $8.4 billion by Blackstone and Vista Equity Partners earlier this year, taking it private.

Google

Has cut over 100 design roles in its cloud division, hitting U.S.-based teams especially hard, as the company shifts focus toward AI investments, per a CNBC report. Many affected employees have until early December to find a new role within Google, following additional layoffs across its Silicon Valley offices, including at least 50 permanent cuts in Sunnyvale.

Paycom

Is reportedly laying off over 500 employees due to AI and automation improving back-office efficiencies. The Oklahoma City-based HR and payroll software company will provide affected workers with severance packages, outplacement services, and access to internal job opportunities.

September

Just Eat

Will eliminate around 450 jobs as part of a cost and operations review, according to Reuters. The layoffs will span multiple functions and countries, including customer service and sales. Europe’s largest food delivery company said it is increasingly using automation and AI, shifting many manual service tasks to automated systems.

Fiverr

Plans to cut around 250 jobs, approximately 30% of its workforce, as part of a push to become a leaner, faster, and AI-focused company, according to The Wall Street Journal. The Tel Aviv-headquartered freelance services marketplace said the restructuring will reduce management layers and position it to pursue growth with an AI-native approach.

ZipRecruiter

Is closing its Tel Aviv development center, cutting about 80 jobs. Led by Yosi Taguri, the office specialized in software, data, and AI research, including algorithm development. The California-based recruitment firm, founded in 2010, is trimming costs amid a challenging labor market.

GupShup

Has laid off at least 100 employees, including junior developers, just months after cutting nearly 200 jobs. The San Francisco-based conversational AI company, which is preparing for an IPO within two years, raised $60 million in equity and debt in July.

xAI

Laid off about a third of its data annotation team, cutting roughly 500 jobs, according to Business Insider. The move comes as the company shifts focus from generalist AI tutors to specialist roles, after testing workers to assess their strengths. Employees were told they’ll be paid through the end of their contracts — or November 30 at the latest — but their system access was cut immediately, Business Insider reports.

Rivian

Has reportedly laid off about 200 workers, or 1.5% of its staff, as the company braces for the end of federal EV tax credits under President Trump’s policy changes. The $7,500 incentive for new electric cars expires this month, adding to pressure from cooling demand. Despite the cuts, Rivian says it’s moving ahead with plans for a lower-cost model.

Oracle

Is cutting another 101 jobs in Seattle and 254 in San Francisco, just weeks after a wave of layoffs in August. The company, which had about 3,900 local employees before the cuts, hasn’t explained the move and declined to comment.

Salesforce

Is trimming another 262 jobs at its San Francisco headquarters, according to a state filing, with layoffs set to take effect November 3. The move comes just weeks after CEO Marc Benioff touted AI’s potential to cut customer support roles and follows a smaller round of cuts in Seattle and Bellevue earlier this month.

August

Cisco

Will eliminate 221 positions across its Milpitas and San Francisco offices, including 157 in Santa Clara County and 64 in San Francisco, effective October 13, according to filings with California’s Employment Development Department reported by the San Francisco Chronicle. The cuts are part of the company’s broader workforce-reduction strategy.

Restaurant365

Laid off about 100 employees last month, around 9% of its workforce, after falling short of ambitious growth targets. The cuts affected staff across all departments. The company provides back-office software for restaurant chains.

Oracle

Is set to cut 101 jobs at its Santa Clara location, with notices issued on August 13 and terminations effective October 13. The company, which recently disclosed nearly 200 layoffs at its Pleasanton and Redwood City offices, is also planning to lay off 161 employees in Seattle, according to filings with the Washington state Employment Security Department.

F5

Is cutting 106 positions at its Seattle and Liberty Lake, Washington, offices, according to a state Employment Security Department filing. The layoffs, which affected senior engineers and managers, are part of a broader global workforce reduction, although the security and application delivery company has not disclosed the total number of employees affected.

Peloton

Will cut 6% of its workforce in its sixth layoff in just over a year. Peloton CEO Peter Stern said the cuts are needed to improve long-term business health.

Kaltura

Is cutting 10% of its workforce, or about 70 employees, as part of a cost-saving effort to reduce operating expenses by $8.5 million, marking its third round of layoffs since 2022. The corporate video software company plans to maintain and gradually grow its sales and marketing budgets, driven by a robust pipeline and growing adoption of its AI-powered offerings.

Yotpo

Is laying off about 200 employees, roughly 34% of its global workforce, as it shuts down its email and SMS marketing operations. The Israeli-founded unicorn is partnering with Attentive and Omnisend to continue supporting marketing services while investing in AI-powered tools like automated review summaries, smart sorting, and a new Loyalty Tiers system.

Windsurf

Laid off 30 employees and is now offering buyouts to the remaining 200. The AI coding startup recently acquired by Cognition has had a rocky stretch, including a near-acquisition by OpenAI and a reverse-acqui-hire by Google that saw key talent depart before Cognition stepped in. Despite initial promises to value Windsurf’s team, the deal now looks more focused on the startup’s intellectual property than its people.

Wondery

Is cutting 100 jobs, and its CEO, Jen Sargent, is departing. Amazon is reorganizing its audio operations, moving Wondery’s audio-only podcasts under Audible and placing video-focused shows into a new Creator Services division. Amazon acquired Wondery in 2020.

July

Atlassian

Has cut 150 roles in customer service and support, following enhancements to its platform and tools that have significantly reduced support needs. The decision came via a prerecorded message from CEO Mike Cannon-Brookes, just hours before co-founder Scott Farquhar urged Australia to embrace an “AI revolution” and move beyond “jobs of the past” in an Australian Press Club address. The Australian software firm was founded 2002.

Consensys

Is cutting about 7% of its workforce, or 47 employees, as part of a push toward profitability, Bloomberg reports. The decision follows the recent acquisition of a startup with around 30 staff, who will stay on with the company. Despite the cuts, the blockchain software company that operates the popular digital wallet MetaMask says it will continue hiring for select roles.

Zeen

Is shutting down operations, per a report by Business Insider. The social collaging platform aimed at creators was founded in 2019 and raised $9 million in funding. Its closure highlights the persistent challenges social media startups face in building user bases and achieving long-term growth.

Scale AI

Is laying off around 200 employees — roughly 14% of its workforce — and severing ties with 500 global contractors. The cuts come just weeks after Meta brought in the data-labeling startup’s CEO in a $14.3 billion deal.

Lenovo

Plans to cut more than 100 U.S. full-time jobs, about 3% of its workforce, including positions at its Morrisville, North Carolina, campus. As of February 2024, the PC maker employed around 5,100 workers in the U.S.

Intel

Is reportedly planning to lay off nearly 2,400 workers in Oregon, which is almost five times more than what was announced earlier this week. Last week, Intel announced that it will lay off more than 500 employees in Oregon, which is about 20% of its workforce, per Bloomberg.

Indeed + Glassdoor

Plan to eliminate approximately 1,300 jobs combined as part of a larger restructuring effort to combine their operations and focus on AI. The layoff will mostly affect employees in the U.S., particularly in the R&D, HR, and sustainability teams, according to an internal memo by Hisayuki “Deko” Idekoba, the CEO of Recruit Holdings, which is the Japanese parent company of Indeed and Glassdoor.

Eigen Lab

Has laid off 29 employees as part of its reorganization, per a report by Blockworks. The Seattle-based research and engineering startup recently launched EigenCloud, a platform that provides blockchain-level trust guarantees for any Web 2.0 or web3 application. The reduction will affect 25% of the company’s workforce. Eigen Labs said it had raised $70 million in tokens from a16z Crypto in June.

Microsoft

Will cut 9,000 employees, which is less than 4% of its global workforce across teams, role types, and geographies. The reduction follows a series of layoffs earlier this year: It cut less than 1% of the headcount in January, more than 6,000 in May, and at least 300 in June.

ByteDance

Is laying off 65 employees in Bellevue, Washington, according to media reports. The parent company of TikTok arrived in Seattle in 2021 and has been expanding its presence there by growing its TikTok Shop online shopping division.

June

TomTom

Announced on June 30 that the company is cutting 300 jobs, or 10% of its workforce, as part of organizational restructuring within its sales and support divisions amid the AI shift. The startup is an Amsterdam-based location tech startup that provides navigation and mapping products.

Rivian

Has reduced its headcount by approximately 140 employees, accounting for roughly 1% of its total workforce. The recent layoffs mostly affected Rivian’s manufacturing team.

Bumble

Announced in an SEC filing that it will cut approximately 240 jobs, or 30% of its workforce, to enhance operational efficiency and allocate the resulting savings to the development of new products and technologies, according to a CNBC report. The layoff will help the online dating app save $40 million annually, per the report.

Klue

Has reportedly laid off 85 employees, which accounts for approximately 40% of its workforce. The Vancouver-based startup sells software products that use artificial intelligence for business intelligence. It helps sales professionals at tech companies gather information on competitors to improve their sales.

Google

Has downsized its smart TV division by 25% of its 300-member team to adjust its strategy, per reports. Funding for the smart TV division, including Google TV and Android TV, has been cut by 10%, but investment in AI projects has been raised.

Intel

Says that it plans to lay off 15% to 20% of workers in its Intel Foundry division starting in July. Intel Foundry designs, manufactures, and packages semiconductors for external clients. Intel’s total workforce was 108,900 people as of December 2024, according to the company’s annual regulatory filing. It also confirmed to TechCrunch that it plans to wind down its auto business.

Playtika

Announced that it is letting go of around 90 employees, with 40 in Israel and 50 in Poland. The most recent round of job cuts comes after the Israel-based gaming company laid off 50 employees a few weeks ago.

Airtime

Has let go of around 25 employees from the 58-person team, the company confirmed to TechCrunch. Evernote’s founder Phil Libin launched the video startup in 2020, offering Airtime Creator and Airtime Camera.

Microsoft

Is laying off more employees, just a few weeks after announcing a job cut of over 6,500 in May, which was around 3% of its global workforce. The most recent layoffs affected software engineers, product managers, technical program managers, marketers, and legal counsels.

May

Hims & Hers

Plans to downsize its workforce by letting go of 68 employees, approximately 4% of its total staff, per Reuters. The San Francisco telehealth platform said that its layoffs were unrelated to a U.S. ban on producing large quantities of the weight-loss drug Wegovy. The startup said it intends to keep on recruiting employees who fit in with its long-term expansion plans.

Amazon

Is reportedly laying off around 100 employees from its devices and services division, which encompasses various businesses like the Alexa voice assistant, Echo smart speakers, Ring video doorbells, and Zoox robotaxis. The company has reduced its workforce by approximately 27,000 since the start of 2022 to cut costs.

Microsoft

Will cut over 6,500 jobs, affecting 3% of its worldwide workforce. As of June, the Seattle-headquartered company had a total of 228,000 employees globally. It would be one of the company’s biggest layoffs since it cut 10,000 employees in 2023.

Chegg

Reportedly plans to let go of 248 employees, or about 22% of its workforce, to reduce expenses and improve efficiency, it said. The San Francisco-based edtech startup, which offers textbook rentals and tutoring services, has seen a drop in web traffic for months as students opt for AI tools instead of traditional edtech platforms.

Match

Is reducing its workforce by 13% as part of a reorganization that aims to reduce costs, shore up margins, and streamline its organizational structure.

CrowdStrike

Is laying off 5% of its global workforce, or around 500 people. The company said the layoffs were part of “a strategic plan (the ‘Plan’) to evolve its operations to yield greater efficiencies as the Company continues to scale its business with focus and discipline to meet its goal of $10 billion in ending [Annual Recurring Revenue]” in its 8-K filing.

General Fusion

Has cut roughly 25% of its current workforce. The Vancouver-based company, which is developing a technology to generate fusion energy, has raised $440 million from investors, including Jeff Bezos, Temasek, and BDC Capital.

Deep Instinct

Reduced its headcount by 20 employees, accounting for 10% of its total workforce. In April 2023, the Israeli cybersecurity startup had previously laid off a similar number of employees during a round of layoffs.

Beam

Has shut down its operations months after announcing major expansion plans, per Sifted. The British climate startup has let go of approximately 200 employees, according to a LinkedIn post by James Reynolds, the head of talent.

April

NetApp

Is reportedly eliminating 700 jobs, affecting 6% of its total workforce, as it reorganizes for its operational efficiency. The company, based in San Francisco, provides data storage, cloud services, and CloudOps solutions for businesses.

Electronic Arts

Is reportedly letting go of approximately 300 to 400 employees, including around 100 at Respawn Entertainment, to focus on its “long-term strategic priorities,” according to Bloomberg.

Expedia

Is laying off around 3% of its employees as part of its restructuring. The job cuts will mainly affect midlevel positions in the product and technology teams. The latest round of layoffs comes after the company let go of hundreds of employees from its marketing team globally in early March.

Cars24

Has reduced its workforce by about 200 employees in its product and technology divisions as part of a restructuring measure. The India-based e-commerce platform for pre-owned vehicles provides a range of services like buying and selling pre-owned cars, financing, insurance, driver-on-demand, and more. In 2023, the SoftBank-backed startup raised $450 million at a valuation of $3.3 billion.

Intel

Announced its plan to lay off more than 21,000 employees, or roughly 20% of its workforce, in April. The move comes ahead of Intel’s Q1 earnings call helmed by recently appointed CEO Lip-Bu Tan, who took over from longtime chief Pat Gelsinger last year.

GM

Is laying off 200 people at its Factory Zero in Detroit and Hamtramck facility in Michigan, which produces GM’s electric vehicles. The cuts come amid the EV slowdown and is not caused by tariffs, according to a report.

Zopper

Has reportedly let go of around 100 employees since the start of 2025. Earlier this week, about 50 employees from the tech and product teams were let go in the latest round of job cuts. The India-based insurtech startup has raised a total of $125 million to date.

Turo

Will reduce its workforce by 150 positions following its decision not to proceed with its IPO, per Bloomberg. The San Francisco-based car rental startup, which had about 1,000 staff in 2024, said the layoffs will bolster its long-term growth plans during economic uncertainty.

GupShup

Laid off roughly 200 employees to improve efficiency and profitability. It’s the startup’s second round of layoffs in five months, following the job cuts of around 300 employees in December. The conversational AI company, backed by Tiger Global and Fidelity, was last valued at $1.4 billion in 2021. The startup is based in San Francisco and operates in India.

Forto

Has reportedly eliminated 200 jobs, affecting around one-third of its employees. The German logistics startup reduced a significant number of sales staff.

Wicresoft

Will stop its operations in China, affecting around 2,000 employees. The move came after Microsoft decided to end outsourcing after-sales support to Wicresoft amid increasing trade tensions. Wicresoft, Microsoft’s first joint venture in China, was founded in 2022 and operates in the U.S., Europe, and Japan. It has over 10,000 employees.

Five9

Plans to cut 123 jobs, affecting about 4% of its workforce, according to a report by MarketWatch. The software company prioritizes key strategic areas like artificial intelligence for profitable growth.

Google

Has laid off hundreds of employees in its platforms and devices division, which covers Android, Pixel phones, the Chrome browser, and more, according to The Information.

Microsoft

Is contemplating additional layoffs that could happen by May, Business Insider reported, citing anonymous sources. The company is said to be discussing reducing the number of middle managers and non-coders in a bid to increase the ratio of programmers to product managers.

Automattic

The WordPress.com developer is laying off 16% of its workforce across departments. Before the layoffs, the company’s website showed it had 1,744 employees, so more than 270 staff may have been laid off.

Canva

Has let go of 10 to 12 technical writers approximately nine months after telling its employees to use generative AI tools wherever possible. The company, which had around 5,500 staff in 2024, was valued at $26 billion after a secondary stock sale in 2024.

March

Northvolt

Has laid off 2,800 employees, affecting 62% of its total staff. The layoffs come weeks after the embattled Swedish battery maker filed for bankruptcy.

Block

Let go of 931 employees, around 8% of its workforce, as part of a reorganization, according to an internal email seen by TechCrunch. Jack Dorsey, the co-founder and CEO of the fintech company, wrote in the email that the layoffs were not for financial reasons or to replace workers with AI.

Brightcove

Has laid off 198 employees, who make up about two-thirds of its U.S. workforce, per a media report. The layoff comes a month after the company was acquired by Bending Spoons, an Italian app developer, for $233 million. Brightcove had 600 employees worldwide, with 300 in the U.S., as of December 2023.

Acxiom

Has reportedly laid off 130 employees, or 3.5% of its total workforce of 3,700 people. Acxiom is owned by IPG, and the news comes just a day after IPG and Omnicom Group shareholders approved the companies’ potential merger.

Sequoia Capital

Plans to close its office in Washington, D.C., and let go of its policy team there by the end of March, TechCrunch has confirmed. Sequoia opened its Washington office five years ago to deepen its relationship with policymakers. Three full-time employees are expected to be affected, per Forbes.

Siemens

Announced plans to let go of approximately 5,600 jobs globally in its automation and electric-vehicle charging businesses as part of efforts to improve competitiveness.

HelloFresh

Is reportedly laying off 273 employees, closing its distribution center in Grand Prairie, Texas, and consolidating to another site in Irving to manage the volume in the region.

Otorio

Has cut 45 employees, more than half of its workforce, after being acquired by cybersecurity company Armis for $120 million in March.

ActiveFence

Will reportedly reduce 22 employees, representing 7% of its workforce. Most of those affected are based in Israel as the company undergoes a streamlining process. The New York- and Tel Aviv-headquartered cybersecurity firm has raised $100 million at a valuation of about $500 million in 2021.

D-ID

Will cut 22 jobs, affecting nearly a quarter of its total workforce, following the announcement of the AI startup’s strategic partnership with Microsoft.

NASA

Announced it will be shutting down several of its offices in accordance with Elon Musk’s DOGE, including its Office of Technology, Policy, and Strategy and the DEI branch in the Office of Diversity and Equal Opportunity.

Zonar Systems

Has reportedly laid off some staff, according to LinkedIn posts from ex-employees. The company has not confirmed the layoffs, and it is currently unknown how many workers were affected.

Wayfair

Announced plans to let go of 340 employees in its technology division as part of a new restructuring effort.

HPE

Will cut 2,500 employees, or 5% of its total staff, in response to its shares sliding 19% in the first fiscal quarter.

TikTok

Will cut up to 300 workers in Dublin, accounting for roughly 10% of the company’s workforce in Ireland.

LiveRamp

Announced it will lay off 65 employees, affecting 5% of its total workforce.

Ola Electric

Is reportedly set to lay off over 1,000 employees and contractors in a cost-cutting effort. It’s the second round of cuts for the company in just five months.

Rec Room

Reduced its total headcount by 16% as the gaming startup shifts its focus to be “scrappier” and “more efficient.”

ANS Commerce

Was shut down just three years after it was acquired by Flipkart. It is currently unknown how many employees were affected.

February

HP

Will cut up to 2,000 jobs as part of its “Future Now” restructuring plan that hopes to save the company $300 million before the end of its fiscal year.

GrubHub

Announced 500 job cuts after it was sold to Wonder Group for $650 million. The number of cuts affected more than 20% of its previous workforce.

Autodesk

Announced plans to lay off 1,350 employees, affecting 9% of its total workforce, in an attempt to reshape its GTM model. The company is also making reductions in its facilities, though it does not plan to close any offices.

Google

Is planning to cut employees in its People Operations and cloud organizations teams in a new reorganization effort. The company is offering a voluntary exit program to U.S.-based People Operations employees.

Nautilus

Reduced its headcount by 25 employees, accounting for 16% of its total workforce. The company is planning to release a commercial version of its proteome analysis platform in 2026.

eBay

Will reportedly cut a few dozen employees in Israel, potentially affecting 10% of its 250-person workforce in the country.

Starbucks

Cut 1,100 jobs in a reorganizing effort that affected its tech workers. The coffee chain will now outsource some tech work to third-party employees.

Commercetools

Laid off dozens of employees over the last few weeks, including around 10% of staff in one day, after failing to meet its sales growth targets. The “headless commerce” platform raised money at a $1.9 billion valuation just a few years ago.

Dayforce

Will cut roughly 5% of its current workforce in a new efficiency drive to increase profitability and growth.

Expedia

Laid off more employees in a new effort to cut costs, though the total number is unknown. Last year, the travel giant cut about 1,500 roles in its Product & Technology division.

Skybox Security

Has ceased operations and has laid off its employees after selling its business and technology to Israeli cybersecurity company Tufin. The cuts affect roughly 300 people.

HerMD

Is shutting down its operations after shifting from a brick-and-mortar model to a fully virtual women’s healthcare provider. The startup, which raised $18 million in 2023, has not disclosed how many employees are affected, saying recent layoffs were tied to its former in-person business.

Zendesk

Cut 51 jobs in its San Francisco headquarters, according to state filings with the Employment Development Department. The SaaS startup previously reduced its headcount by 8% in 2023.

Vendease

Has cut 120 employees, affecting 44% of its total staff. It’s the Y Combinator-backed Nigerian startup’s second layoff round in just five months.

Logically

Reportedly laid off dozens of employees as part of a new cost-cutting effort that aims to ensure “long-term success” in the startup’s mission to curb misinformation online.

Blue Origin

Will lay off about 10% of its workforce, affecting more than 1,000 employees. According to an email to staff obtained by CNN, the cuts will largely have an impact on positions in engineering and program management.

Redfin

Announced in an SEC filing that it will cut around 450 positions between February and July 2025, with a complete restructuring set to be completed in the fall, following its new partnership with Zillow.

Sophos

Is laying off 6% of its total workforce, the cybersecurity firm confirmed to TechCrunch. The cuts come less than two weeks after Sophos acquired Secureworks for $859 million.

Zepz

Will cut nearly 200 employees as it introduces redundancy measures and closes down its operations in Poland and Kenya.

Unity

Reportedly conducted another round of layoffs. It’s unknown how many employees were affected.

JustWorks

Cut nearly 200 employees, CEO Mike Seckler announced in a note to employees, citing “potential adverse events” like a recession or rising interest rates.

Bird

Cut 120 jobs, affecting roughly one-third of its total workforce, TechCrunch exclusively learned. The move comes just a year after the Dutch startup cut 90 employees following its rebrand.

Sprinklr

Laid off about 500 employees, affecting 15% of its workforce, citing poor business performance. The new cuts follow two earlier layoff rounds for the company that affected roughly 200 employees.

Sonos

Reportedly let go of approximately 200 employees, according to The Verge. The company previously cut 100 employees as part of a layoff round in August 2024.

Workday

Laid off 1,750 employees, as originally reported by Bloomberg and confirmed independently by TechCrunch. The cuts affect roughly 8.5% of the enterprise HR platform’s total headcount.

Okta

Laid off 180 employees, the company confirmed to TechCrunch. The cuts come just over one year after the access and identity management giant let go of 400 workers.

Cruise

Is laying off 50% of its workforce, including CEO Marc Whitten and several other top executives, as it prepares to shut down operations. What remains of the autonomous vehicle company will move under General Motors.

Salesforce

Is reportedly eliminating more than 1,000 jobs. The cuts come as the giant is actively recruiting and hiring workers to sell new AI products.

January

Cushion

Has shut down operations, CEO Paul Kesserwani announced on LinkedIn. The fintech startup’s post-money valuation in 2022 was $82.4 million, according to PitchBook.

Placer.ai

Laid off 150 employees based in the U.S., affecting roughly 18% of its total workforce, in an effort to reach profitability.

Amazon

Laid off dozens of workers in its communications department in order to help the company “move faster, increase ownership, strengthen our culture, and bring teams closer to customers.”

Stripe

Is laying off 300 people, according to a leaked memo reported by Business Insider. However, according to the memo, the fintech giant is planning to grow its total headcount by 17%.

Textio

Laid off 15 employees as the augmented writing startup undergoes a restructuring effort.

Pocket FM

Is cutting 75 employees in an effort to “ensure the long-term sustainability and success” of the company. The audio company last cut 200 writers in July 2024 months after partnering with ElevenLabs.

Aurora Solar

Is planning to cut 58 employees in response to an “ongoing macroeconomic challenges and continued uncertainty in the solar industry.”

Wayfair

Will cut up to 730 jobs, affecting 3% of its total workforce, as it plans to exit operations in Germany and focus on physical retailers.

Pandion

Is shutting down its operations, affecting 63 employees. The delivery startup said employees will be paid through January 15 without severance.

Icon

Is laying off 114 employees as part of a team realignment, per a new WARN notice filing, focusing its efforts on a robotic printing system.

Altruist

Eliminated 37 jobs, affecting roughly 10% of its total workforce, even as the company pursues “aggressive” hiring.

Aqua Security

Is cutting dozens of employees across its global markets as part of a strategic reorganization to increase profitability.

SolarEdge Technologies

Plans to lay off 400 employees globally. It’s the company’s fourth layoff round since January 2024 as the solar industry as a whole faces a downturn.

Level

The fintech startup, founded in 2018, abruptly shut down earlier this year. Per an email from CEO Paul Aaron, the closure follows an unsuccessful attempt to find a buyer, though Employer.com has a new offer under consideration to acquire the company post-shutdown.

This list updates regularly.

On April 24, 2025, we corrected the number of layoffs that happened in March.

Every fusion startup that has raised over $100M

Posted on September 1, 2025 by faz_business

Over the last several years, fusion power has gone from the butt of jokes — always a decade away! — to an increasingly tangible and tantalizing technology that has drawn investors off the sidelines.

The technology may be challenging to master and expensive to build today, but fusion promises to harness the nuclear reaction that powers the sun to generate nearly limitless energy here on Earth. If startups are able to complete commercially viable fusion power plants, then they have the potential to upend trillion-dollar markets.

The bullish wave buoying the fusion industry has been driven by three advances: more powerful computer chips, more sophisticated AI, and powerful high-temperature superconducting magnets. Together, they have helped deliver more sophisticated reactor designs, better simulations, and more complex control schemes.

It doesn’t hurt that, at the end of 2022, a U.S. Department of Energy lab announced that it had produced a controlled fusion reaction that produced more power than the lasers had imparted to the fuel pellet. The experiment had crossed what’s known as scientific breakeven, and while it’s still a long ways from commercial breakeven, where the reaction produces more than the entire facility consumes, it was a long-awaited step that proved the underlying science was sound.

Founders have built on that momentum in recent years, pushing the private fusion industry forward at a rapid pace.

Commonwealth Fusion Systems

Commonwealth Fusion Systems (CFS) has raised about a third of all private capital invested in fusion companies to date. Its latest round, which closed in August, added $863 million to its coffers, bringing its total raised near $3 billion.

CFS’s Series B2 came four years after its $1.8 billion Series B, which helped catapult the company into the pole position. Since then, the startup has been hard at work in Massachusetts building Sparc, its first-of-a-kind power plant intended to produce power at what it calls “commercially relevant” levels.

Techcrunch event

San Francisco
|
October 27-29, 2025

Sparc’s reactor is a tokamak design, which resembles a doughnut. The D-shaped cross section is wound with high-temperature superconducting tape, which, when energized, generates a powerful magnetic field that will contain and compress the superheated plasma. Heat generated from the reaction is converted to steam to power a turbine. CFS designed its magnets in collaboration with MIT, where co-founder and CEO Bob Mumgaard worked as a researcher on fusion reactor designs and high-temperature superconductors.

The Massachusetts-based CFS expects to have Sparc operational in late 2026 or early 2027. Later this decade, the company says it will begin construction on Arc, its commercial power plant that will produce 400 megawatts of electricity. The facility will be built near Richmond, Virginia, and Google has agreed to buy half its output.

CFS is backed by a long list of investors, including Breakthrough Energy Ventures, The Engine, Bill Gates, and others.

TAE

Founded in 1998, TAE Technologies (formerly known as Tri Alpha Energy) was spun out of the University of California, Irvine by Norman Rostoker. It uses a field-reversed configuration, but with a twist: after the two plasma shots collide in the middle of the reactor, the company bombards the plasma with particle beams to keep it spinning in a cigar shape. That improves the stability of the plasma, allowing more time for fusion to occur and for more heat to be extracted to spin a turbine.

The company raised $150 million in June from existing investors, including Google, Chevron, and New Enterprise. TAE has raised $1.79 billion in total, according to PitchBook.

Helion

Of all fusion startups, Helion has the most aggressive timeline. The company plans to produce electricity from its reactor in 2028. Its first customer? Microsoft.

Helion, based in Everett, Washington, uses a type of reactor called a field-reversed configuration, where magnets surround a reaction chamber that looks like an hourglass with a bulge at the point where the two sides come together. At each end of the hourglass, they spin the plasma into doughnut shapes that are shot toward each other at more than 1 million mph. When they collide in the middle, additional magnets help induce fusion. When fusion occurs, it boosts the plasma’s own magnetic field, which induces an electrical current inside the reactor’s magnetic coils. That electricity is then harvested directly from the machine.

The company raised $425 million in January 2025, around the same time that it turned on Polaris, a prototype reactor. Helion has raised $1.03 billion, according to PitchBook. Investors include Sam Altman, Reid Hoffman, KKR, BlackRock, Peter Thiel’s Mithril Capital Management, and Capricorn Investment Group.

Pacific Fusion

Pacific Fusion burst out of the gate with a $900 million Series A, a whopping sum even among well-funded fusion startups. The company will use inertial confinement to achieve fusion, but instead of lasers compressing the fuel, it will use coordinated electromagnetic pulses. The trick is in the timing: All 156 impedance-matched Marx generators need to produce 2 terawatts for 100 nanoseconds, and those pulses need to simultaneously converge on the target.

The company is led by CEO Eric Lander, the scientist who led the Human Genome Project, and president Will Regan. Pacific Fusion’s funding might be massive, but the startup hasn’t gotten it all at once. Rather, its investors will pay out in tranches when the company achieves specified milestones, an approach that’s common in biotech.

Shine Technologies

Shine Technologies is taking a cautious — and possibly pragmatic — approach to generating fusion power. Selling electrons from a fusion power plant is years off, so instead, it’s starting by selling neutron testing and medical isotopes. More recently, it has been developing a way to recycle radioactive waste. Shine hasn’t picked an approach for a future fusion reactor, instead saying that it’s developing necessary skills for when that time comes.

The company has raised a total of $778 million, according to PitchBook. Investors include Energy Ventures Group, Koch Disruptive Technologies, Nucleation Capital, and the Wisconsin Alumni Research Foundation.

General Fusion

Now its third decade, General Fusion has raised $462.53 million, according to PitchBook. The Richmond, British Columbia-based company was founded in 2002 by physicist Michel Laberge, who wanted to prove a different approach to fusion known as magnetized target fusion (MTF). Investors include Jeff Bezos, Temasek, BDC Capital, and Chrysalix Venture Capital.

In General Fusion’s reactor, a liquid metal wall surrounds a chamber in which plasma is injected. Pistons surrounding the wall push it inward, compressing the plasma inside and sparking a fusion reaction. The resulting neutrons heat the liquid metal, which can be circulated through a heat exchanger to generate steam to spin a turbine.

General Fusion hit a rough patch in spring 2025. The company ran short of cash as it was building LM26, its latest device that it hoped would hit breakeven in 2026. Just days after hitting a key milestone, it laid off 25% of its staff. CEO Greg Twinney penned an open letter pleading for funding from investors.

In August, they delivered somewhat, injecting $22 million in an pay-to-play round that one investor called “the least amount of capital possible” to keep the General Fusion afloat.

Tokamak Energy

Tokamak Energy takes the usual tokamak design — the doughnut shape — and squeezes it, reducing its aspect ratio to the point where the outer bounds start resembling a sphere. Like many other tokamak-based startups, the company uses high-temperature superconducting magnets (of the rare earth barium copper oxide, or REBCO, variety). Since its design is more compact than a traditional tokamak, it requires less in the way of magnets, which should reduce costs.

The Oxfordshire, U.K.-based startup’s ST40 prototype, which looks like a large, steampunk Fabergé egg, generated an ultra-hot, 100 million degree C plasma in 2022. Its next generation, Demo 4, is currently under construction and is intended to test the company’s magnets in “fusion power plant-relevant scenarios.” Tokamak Energy raised $125 million in November 2024 to continue its reactor design efforts and expand its magnet business.

In total, the company has raised $336 million from investors including Future Planet Capital, In-Q-Tel, Midven, and Capri-Sun founder Hans-Peter Wild, according to PitchBook.

Zap Energy

Zap Energy isn’t using high-temperature superconducting magnets or super-powerful lasers to keep its plasma confined. Rather, it zaps the plasma (get it?) with an electric current, which then generates its own magnetic field. The magnetic field compresses the plasma about 1 millimeter, at which point ignition occurs. The neutrons released by the fusion reaction bombard a liquid metal blanket that surrounds the reactor, heating it up. The liquid metal is then cycled through a heat exchanger, where it produces steam to drive a turbine.

Like Helion, Zap Energy is based in Everett, Washington, and the company has raised $327 million, according to PitchBook. Backers include Bill Gates’ Breakthrough Energy Ventures, DCVC, Lowercarbon, Energy Impact Partners, Chevron Technology Ventures, and Bill Gates as an angel.

Proxima Fusion

Most investors have favored large startups that are pursuing tokamak designs or some flavor of inertial confinement. But stellarators have shown great promise in scientific experiments, including the Wendelstein 7-X reactor in Germany.

Proxima Fusion is bucking the trend, though, having attracted a €130 million Series A that brings its total raised to more than €185 million. Investors include Balderton Capital and Cherry Ventures.

Stellarators are similar to tokamaks in that they confine plasma in a ring-like shape using powerful magnets. But they do it with a twist — literally. Rather than force plasma into a human-designed ring, stellarators twist and bulge to accommodate the plasma’s quirks. The result should be a plasma that remains stable for longer, increasing the chances of fusion reactions.

Marvel Fusion

Marvel Fusion follows the inertial confinement approach, the same basic technique that the National Ignition Facility used to prove that controlled nuclear fusion reactions could produce more power than was needed to kick them off. Marvel fires powerful lasers at a target embedded with silicon nanostructures that cascade under the bombardment, compressing the fuel to the point of ignition. Because the target is made using silicon, it should be relatively simple to manufacture, leaning on the semiconductor manufacturing industry’s decades of experience.

The inertial confinement fusion startup is building a demonstration facility in collaboration with Colorado State University, which it expects to have operational by 2027. Munich-based Marvel has raised a total of $161 million from investors including b2venture, Deutsche Telekom, Earlybird, HV Capital, and Taavet Hinrikus and Albert Wenger as angels.

First Light

First Light dropped its pursuit of fusion power in March 2025, pivoting instead to become a technology supplier to fusion startups and other companies. The startup had previously followed an approach known as inertial confinement, in which fusion fuel pellets are compressed until they ignite.

First Light, which is based in Oxfordshire, U.K., has raised $140 million, according to PitchBook, from investors including Invesco, IP Group, and Tencent.

Xcimer

Though nothing about fusion can be described as simple, Xcimer takes a relatively straightforward approach: follow the basic science that’s behind the National Ignition Facility’s breakthrough net-positive experiment, and redesign the technology that underpins it from the ground up. The Colorado-based startup is aiming for a 10-megajoule laser system, five times more powerful than NIF’s setup that made history. Molten salt walls surround the reaction chamber, absorbing heat and protecting the first solid wall from damage.

Founded in January 2022, Xcimer has already raised $109 million, according to PitchBook, from investors including Hedosophia, Breakthrough Energy Ventures, Emerson Collective, Gigascale Capital, and Lowercarbon Capital.

This story was originally published in September 2024 and will be continually updated.

Everything you need to know about the AI chatbot

Posted on July 1, 2025 by faz_business

ChatGPT, OpenAI’s text-generating AI chatbot, has taken the world by storm since its launch in November 2022. What started as a tool to supercharge productivity through writing essays and code with short text prompts has evolved into a behemoth with 300 million weekly active users.

2024 was a big year for OpenAI, from its partnership with Apple for its generative AI offering, Apple Intelligence, the release of GPT-4o with voice capabilities, and the highly-anticipated launch of its text-to-video model Sora.

OpenAI also faced its share of internal drama, including the notable exits of high-level execs like co-founder and longtime chief scientist Ilya Sutskever and CTO Mira Murati. OpenAI has also been hit with lawsuits from Alden Global Capital-owned newspapers alleging copyright infringement, as well as an injunction from Elon Musk to halt OpenAI’s transition to a for-profit.

In 2025, OpenAI is battling the perception that it’s ceding ground in the AI race to Chinese rivals like DeepSeek. The company has been trying to shore up its relationship with Washington as it simultaneously pursues an ambitious data center project, and as it reportedly lays the groundwork for one of the largest funding rounds in history.

Below, you’ll find a timeline of ChatGPT product updates and releases, starting with the latest, which we’ve been updating throughout the year. If you have any other questions, check out our ChatGPT FAQ here.

To see a list of 2024 updates, go here.

Timeline of the most recent ChatGPT updates

June 2025

OpenAI uses Google’s AI chips to power its products

OpenAI has started using Google’s AI chips to power ChatGPT and other products, as reported by Reuters. The ChatGPT maker is one of the biggest buyers of Nvidia’s GPUs, using the AI chips to train models, and this is the first time that OpenAI is using non-Nvidia chips in an important way.

A new MIT study suggests that ChatGPT might be harming critical thinking skills

Researchers from MIT’s Media Lab monitored the brain activity of writers in 32 regions. They found that ChatGPT users showed minimal brain engagement and consistently fell short in neural, linguistic, and behavioral aspects. To conduct the test, the lab split 54 participants from the Boston area into three groups, each consisting of individuals ages 18 to 39. The participants were asked to write multiple SAT essays using tools such as OpenAI’s ChatGPT, the Google search engine, or without any tools.

ChatGPT was downloaded 30 million times last month

The ChatGPT app for iOS was downloaded 29.6 million times in the last 28 days, while TikTok, Facebook, Instagram, and X were downloaded a total of 32.9 million times during the same period, representing a difference of about 10.6%, according to ZDNET report citing Similarweb’s X post.

The energy needed for an average ChatGPT query can power a lightbulb for a couple of minutes

Sam Altman said that the average ChatGPT query uses about one-fifteenth of a teaspoon of water, equivalent to 0.000083 gallons of water, or the energy required to power a lightbulb for a few minutes, per Business Insider. In addition to that, the chatbot requires 0.34 watt-hours of electricity to operate.

OpenAI has launched o3-pro, an upgraded version of its o3 AI reasoning model

OpenAI has unveiled o3-pro, an enhanced version of its o3, a reasoning model that the chatGPT maker launched earlier this year. O3-pro is available for ChatGPT and Team users and in the API, while Enterprise and Edu users will get access in the third week of June.

OpenAI o3-pro is available in the model picker for Pro and Team users starting today, replacing OpenAI o1-pro.

Enterprise and Edu users will get access the week after.

As o3-pro uses the same underlying model as o3, full safety details can be found in the o3 system card.…

— OpenAI (@OpenAI) June 10, 2025

ChatGPT’s conversational voice mode has been upgraded

OpenAI upgraded ChatGPT’s conversational voice mood for all paid users across different markets and platforms. The startup has launched an update to Advanced Voice that enables users to converse with ChatGPT out loud in a more natural and fluid sound. The feature also helps users translate languages more easily, the comapny said.

ChatGPT has added new features like meeting recording and connectors for Google Drive, Box, and more

OpenAI’s ChatGPT now offers new funtions for business users, including integrations with various cloud services, meeting recordings, and MCP connection support for connecting to tools for in-depth research. The feature enables ChatGPT to retrieve information across users’ own services to answer their questions. For instance, an analyst could use the company’s slide deck and documents to develop an investment thesis.

May 2025

OpenAI CFO says hardware will drive ChatGPT’s growth

OpenAI plans to purchase Jony Ive’s devices startup io for $6.4 billion. Sarah Friar, CFO of OpenAI, thinks that the hardware will significantly enhance ChatGPT and broaden OpenAI’s reach to a larger audience in the future.

OpenAI’s ChatGPT unveils its AI coding agent, Codex

OpenAI has introduced its AI coding agent, Codex, powered by codex-1, a version of its o3 AI reasoning model designed for software engineering tasks. OpenAI says codex-1 generates more precise and “cleaner” code than o3. The coding agent may take anywhere from one to 30 minutes to complete tasks such as writing simple features, fixing bugs, answering questions about your codebase, and running tests.

Sam Altman aims to make ChatGPT more personalized by tracking every aspect of a person’s life

Sam Altman, the CEO of OpenAI, said during a recent AI event hosted by VC firm Sequoia that he wants ChatGPT to record and remember every detail of a person’s life when one attendee asked about how ChatGPT can become more personalized.

OpenAI releases its GPT-4.1 and GPT-4.1 mini AI models in ChatGPT

OpenAI said in a post on X that it has launched its GPT-4.1 and GPT4.1 mini AI models in ChagGPT.

By popular request, GPT-4.1 will be available directly in ChatGPT starting today.

GPT-4.1 is a specialized model that excels at coding tasks & instruction following. Because it’s faster, it’s a great alternative to OpenAI o3 & o4-mini for everyday coding needs.

— OpenAI (@OpenAI) May 14, 2025

OpenAI has launched a new feature for ChatGPT deep research to analyze code repositories on GitHub. The ChatGPT deep research feature is in beta and lets developers connect with GitHub to ask questions about codebases and engineering documents. The connector will soon be available for ChatGPT Plus, Pro, and Team users, with support for Enterprise and Education coming shortly, per an OpenAI spokesperson.

OpenAI launches a new data residency program in Asia

After introducing a data residency program in Europe in February, OpenAI has now launched a similar program in Asian countries including India, Japan, Singapore, and South Korea. The new program will be accessible to users of ChatGPT Enterprise, ChatGPT Edu, and API. It will help organizations in Asia meet their local data sovereignty requirements when using OpenAI’s products.

OpenAI to introduce a program to grow AI infrastructure

OpenAI is unveiling a program called OpenAI for Countries, which aims to develop the necessary local infrastructure to serve international AI clients better. The AI startup will work with governments to assist with increasing data center capacity and customizing OpenAI’s products to meet specific language and local needs. OpenAI for Countries is part of efforts to support the company’s expansion of its AI data center Project Stargate to new locations outside the U.S., per Bloomberg.

OpenAI promises to make changes to prevent future ChatGPT sycophancy

OpenAI has announced its plan to make changes to its procedures for updating the AI models that power ChatGPT, following an update that caused the platform to become overly sycophantic for many users.

April 2025

OpenAI clarifies the reason ChatGPT became overly flattering and agreeable

OpenAI has released a post on the recent sycophancy issues with the default AI model powering ChatGPT, GPT-4o, leading the company to revert an update to the model released last week. CEO Sam Altman acknowledged the issue on Sunday and confirmed two days later that the GPT-4o update was being rolled back. OpenAI is working on “additional fixes” to the model’s personality. Over the weekend, users on social media criticized the new model for making ChatGPT too validating and agreeable. It became a popular meme fast.

OpenAI is working to fix a “bug” that let minors engage in inappropriate conversations

An issue within OpenAI’s ChatGPT enabled the chatbot to create graphic erotic content for accounts registered by users under the age of 18, as demonstrated by TechCrunch’s testing, a fact later confirmed by OpenAI. “Protecting younger users is a top priority, and our Model Spec, which guides model behavior, clearly restricts sensitive content like erotica to narrow contexts such as scientific, historical, or news reporting,” a spokesperson told TechCrunch via email. “In this case, a bug allowed responses outside those guidelines, and we are actively deploying a fix to limit these generations.”

ChatGPT helps users by giving recommendations, showing images, and reviewing products for online shopping

OpenAI has added a few features to its ChatGPT search, its web search tool in ChatGPT, to give users an improved online shopping experience. The company says people can ask super-specific questions using natural language and receive customized results. The chatbot provides recommendations, images, and reviews of products in various categories such as fashion, beauty, home goods, and electronics.

OpenAI wants its AI model to access cloud models for assistance

OpenAI leaders have been talking about allowing the open model to link up with OpenAI’s cloud-hosted models to improve its ability to respond to intricate questions, two sources familiar with the situation told TechCrunch.

OpenAI aims to make its new “open” AI model the best on the market

OpenAI is preparing to launch an AI system that will be openly accessible, allowing users to download it for free without any API restrictions. Aidan Clark, OpenAI’s VP of research, is spearheading the development of the open model, which is in the very early stages, sources familiar with the situation told TechCrunch.

OpenAI’s GPT-4.1 may be less aligned than earlier models

OpenAI released a new AI model called GPT-4.1 in mid-April. However, multiple independent tests indicate that the model is less reliable than previous OpenAI releases. The company skipped that step — sending safety cards for GPT-4.1 — claiming in a statement to TechCrunch that “GPT-4.1 is not a frontier model, so there won’t be a separate system card released for it.”

OpenAI’s o3 AI model scored lower than expected on a benchmark

Questions have been raised regarding OpenAI’s transparency and procedures for testing models after a difference in benchmark outcomes was detected by first- and third-party benchmark results for the o3 AI model. OpenAI introduced o3 in December, stating that the model could solve approximately 25% of questions on FrontierMath, a difficult math problem set. Epoch AI, the research institute behind FrontierMath, discovered that o3 achieved a score of approximately 10%, which was significantly lower than OpenAI’s top-reported score.

OpenAI unveils Flex processing for cheaper, slower AI tasks

OpenAI has launched a new API feature called Flex processing that allows users to use AI models at a lower cost but with slower response times and occasional resource unavailability. Flex processing is available in beta on the o3 and o4-mini reasoning models for non-production tasks like model evaluations, data enrichment, and asynchronous workloads.

OpenAI’s latest AI models now have a safeguard against biorisks

OpenAI has rolled out a new system to monitor its AI reasoning models, o3 and o4 mini, for biological and chemical threats. The system is designed to prevent models from giving advice that could potentially lead to harmful attacks, as stated in OpenAI’s safety report.

OpenAI launches its latest reasoning models, o3 and o4-mini

OpenAI has released two new reasoning models, o3 and o4 mini, just two days after launching GPT-4.1. The company claims o3 is the most advanced reasoning model it has developed, while o4-mini is said to provide a balance of price, speed, and performance. The new models stand out from previous reasoning models because they can use ChatGPT features like web browsing, coding, and image processing and generation. But they hallucinate more than several of OpenAI’s previous models.

OpenAI has added a new section to ChatGPT to offer easier access to AI-generated images for all user tiers

Open AI introduced a new section called “library” to make it easier for users to create images on mobile and web platforms, per the company’s X post.

OpenAI could “adjust” its safeguards if rivals release “high-risk” AI

OpenAI said on Tuesday that it might revise its safety standards if “another frontier AI developer releases a high-risk system without comparable safeguards.” The move shows how commercial AI developers face more pressure to rapidly implement models due to the increased competition.

OpenAI is currently in the early stages of developing its own social media platform to compete with Elon Musk’s X and Mark Zuckerberg’s Instagram and Threads, according to The Verge. It is unclear whether OpenAI intends to launch the social network as a standalone application or incorporate it into ChatGPT.

OpenAI will remove its largest AI model, GPT-4.5, from the API, in July

OpenAI will discontinue its largest AI model, GPT-4.5, from its API even though it was just launched in late February. GPT-4.5 will be available in a research preview for paying customers. Developers can use GPT-4.5 through OpenAI’s API until July 14; then, they will need to switch to GPT-4.1, which was released on April 14.

OpenAI unveils GPT-4.1 AI models that focus on coding capabilities

OpenAI has launched three members of the GPT-4.1 model — GPT-4.1, GPT-4.1 mini, and GPT-4.1 nano — with a specific focus on coding capabilities. It’s accessible via the OpenAI API but not ChatGPT. In the competition to develop advanced programming models, GPT-4.1 will rival AI models such as Google’s Gemini 2.5 Pro, Anthropic’s Claude 3.7 Sonnet, and DeepSeek’s upgraded V3.

OpenAI will discontinue ChatGPT’s GPT-4 at the end of April

OpenAI plans to sunset GPT-4, an AI model introduced more than two years ago, and replace it with GPT-4o, the current default model, per changelog. It will take effect on April 30. GPT-4 will remain available via OpenAI’s API.

OpenAI could release GPT-4.1 soon

OpenAI may launch several new AI models, including GPT-4.1, soon, The Verge reported, citing anonymous sources. GPT-4.1 would be an update of OpenAI’s GPT-4o, which was released last year. On the list of upcoming models are GPT-4.1 and smaller versions like GPT-4.1 mini and nano, per the report.

OpenAI has updated ChatGPT to use information from your previous conversations

OpenAI started updating ChatGPT to enable the chatbot to remember previous conversations with a user and customize its responses based on that context. This feature is rolling out to ChatGPT Pro and Plus users first, excluding those in the U.K., EU, Iceland, Liechtenstein, Norway, and Switzerland.

OpenAI is working on watermarks for images made with ChatGPT

It looks like OpenAI is working on a watermarking feature for images generated using GPT-4o. AI researcher Tibor Blaho spotted a new “ImageGen” watermark feature in the new beta of ChatGPT’s Android app. Blaho also found mentions of other tools: “Structured Thoughts,” “Reasoning Recap,” “CoT Search Tool,” and “l1239dk1.”

OpenAI offers ChatGPT Plus for free to U.S., Canadian college students

OpenAI is offering its $20-per-month ChatGPT Plus subscription tier for free to all college students in the U.S. and Canada through the end of May. The offer will let millions of students use OpenAI’s premium service, which offers access to the company’s GPT-4o model, image generation, voice interaction, and research tools that are not available in the free version.

ChatGPT users have generated over 700M images so far

More than 130 million users have created over 700 million images since ChatGPT got the upgraded image generator on March 25, according to COO of OpenAI Brad Lightcap. The image generator was made available to all ChatGPT users on March 31, and went viral for being able to create Ghibli-style photos.

OpenAI’s o3 model could cost more to run than initial estimate

The Arc Prize Foundation, which develops the AI benchmark tool ARC-AGI, has updated the estimated computing costs for OpenAI’s o3 “reasoning” model managed by ARC-AGI. The organization originally estimated that the best-performing configuration of o3 it tested, o3 high, would cost approximately $3,000 to address a single problem. The Foundation now thinks the cost could be much higher, possibly around $30,000 per task.

OpenAI CEO says capacity issues will cause product delays

In a series of posts on X, OpenAI CEO Sam Altman said the company’s new image-generation tool’s popularity may cause product releases to be delayed. “We are getting things under control, but you should expect new releases from OpenAI to be delayed, stuff to break, and for service to sometimes be slow as we deal with capacity challenges,” he wrote.

March 2025

OpenAI plans to release a new ‘open’ AI language model

OpeanAI intends to release its “first” open language model since GPT-2 “in the coming months.” The company plans to host developer events to gather feedback and eventually showcase prototypes of the model. The first developer event is to be held in San Francisco, with sessions to follow in Europe and Asia.

OpenAI removes ChatGPT’s restrictions on image generation

OpenAI made a notable change to its content moderation policies after the success of its new image generator in ChatGPT, which went viral for being able to create Studio Ghibli-style images. The company has updated its policies to allow ChatGPT to generate images of public figures, hateful symbols, and racial features when requested. OpenAI had previously declined such prompts due to the potential controversy or harm they may cause. However, the company has now “evolved” its approach, as stated in a blog post published by Joanne Jang, the lead for OpenAI’s model behavior.

OpenAI adopts Anthropic’s standard for linking AI models with data

OpenAI wants to incorporate Anthropic’s Model Context Protocol (MCP) into all of its products, including the ChatGPT desktop app. MCP, an open-source standard, helps AI models generate more accurate and suitable responses to specific queries, and lets developers create bidirectional links between data sources and AI applications like chatbots. The protocol is currently available in the Agents SDK, and support for the ChatGPT desktop app and Responses API will be coming soon, OpenAI CEO Sam Altman said.

OpenAI’s viral Studio Ghibli-style images could raise AI copyright concerns

The latest update of the image generator on OpenAI’s ChatGPT has triggered a flood of AI-generated memes in the style of Studio Ghibli, the Japanese animation studio behind blockbuster films like “My Neighbor Totoro” and “Spirited Away.” The burgeoning mass of Ghibli-esque images have sparked concerns about whether OpenAI has violated copyright laws, especially since the company is already facing legal action for using source material without authorization.

OpenAI expects revenue to triple to $12.7 billion this year

OpenAI expects its revenue to triple to $12.7 billion in 2025, fueled by the performance of its paid AI software, Bloomberg reported, citing an anonymous source. While the startup doesn’t expect to reach positive cash flow until 2029, it expects revenue to increase significantly in 2026 to surpass $29.4 billion, the report said.

ChatGPT has upgraded its image-generation feature

OpenAI on Tuesday rolled out a major upgrade to ChatGPT’s image-generation capabilities: ChatGPT can now use the GPT-4o model to generate and edit images and photos directly. The feature went live earlier this week in ChatGPT and Sora, OpenAI’s AI video-generation tool, for subscribers of the company’s Pro plan, priced at $200 a month, and will be available soon to ChatGPT Plus subscribers and developers using the company’s API service. The company’s CEO Sam Altman said on Wednesday, however, that the release of the image generation feature to free users would be delayed due to higher demand than the company expected.

OpenAI announces leadership updates

Brad Lightcap, OpenAI’s chief operating officer, will lead the company’s global expansion and manage corporate partnerships as CEO Sam Altman shifts his focus to research and products, according to a blog post from OpenAI. Lightcap, who previously worked with Altman at Y Combinator, joined the Microsoft-backed startup in 2018. OpenAI also said Mark Chen would step into the expanded role of chief research officer, and Julia Villagra will take on the role of chief people officer.

OpenAI’s AI voice assistant now has advanced feature

OpenAI has updated its AI voice assistant with improved chatting capabilities, according to a video posted on Monday (March 24) to the company’s official media channels. The update enables real-time conversations, and the AI assistant is said to be more personable and interrupts users less often. Users on ChatGPT’s free tier can now access the new version of Advanced Voice Mode, while paying users will receive answers that are “more direct, engaging, concise, specific, and creative,” a spokesperson from OpenAI told TechCrunch.

OpenAI, Meta in talks with Reliance in India

OpenAI and Meta have separately engaged in discussions with Indian conglomerate Reliance Industries regarding potential collaborations to enhance their AI services in the country, per a report by The Information. One key topic being discussed is Reliance Jio distributing OpenAI’s ChatGPT. Reliance has proposed selling OpenAI’s models to businesses in India through an application programming interface (API) so they can incorporate AI into their operations. Meta also plans to bolster its presence in India by constructing a large 3GW data center in Jamnagar, Gujarat. OpenAI, Meta, and Reliance have not yet officially announced these plans.

OpenAI faces privacy complaint in Europe for chatbot’s defamatory hallucinations

Noyb, a privacy rights advocacy group, is supporting an individual in Norway who was shocked to discover that ChatGPT was providing false information about him, stating that he had been found guilty of killing two of his children and trying to harm the third. “The GDPR is clear. Personal data has to be accurate,” said Joakim Söderberg, data protection lawyer at Noyb, in a statement. “If it’s not, users have the right to have it changed to reflect the truth. Showing ChatGPT users a tiny disclaimer that the chatbot can make mistakes clearly isn’t enough. You can’t just spread false information and in the end add a small disclaimer saying that everything you said may just not be true.”

OpenAI upgrades its transcription and voice-generating AI models

OpenAI has added new transcription and voice-generating AI models to its APIs: a text-to-speech model, “gpt-4o-mini-tts,” that delivers more nuanced and realistic sounding speech, as well as two speech-to-text models called “gpt-4o-transcribe” and “gpt-4o-mini-transcribe”. The company claims they are improved versions of what was already there and that they hallucinate less.

OpenAI has launched o1-pro, a more powerful version of its o1

OpenAI has introduced o1-pro in its developer API. OpenAI says its o1-pro uses more computing than its o1 “reasoning” AI model to deliver “consistently better responses.” It’s only accessible to select developers who have spent at least $5 on OpenAI API services. OpenAI charges $150 for every million tokens (about 750,000 words) input into the model and $600 for every million tokens the model produces. It costs twice as much as OpenAI’s GPT-4.5 for input and 10 times the price of regular o1.

OpenAI research lead Noam Brown thinks AI “reasoning” models could’ve arrived decades ago

Noam Brown, who heads AI reasoning research at OpenAI, thinks that certain types of AI models for “reasoning” could have been developed 20 years ago if researchers had understood the correct approach and algorithms.

OpenAI says it has trained an AI that’s “really good” at creative writing

OpenAI CEO Sam Altman said, in a post on X, that the company has trained a “new model” that’s “really good” at creative writing. He posted a lengthy sample from the model given the prompt “Please write a metafictional literary short story about AI and grief.” OpenAI has not extensively explored the use of AI for writing fiction. The company has mostly concentrated on challenges in rigid, predictable areas such as math and programming. And it turns out that it might not be that great at creative writing at all.

we trained a new model that is good at creative writing (not sure yet how/when it will get released). this is the first time i have been really struck by something written by AI; it got the vibe of metafiction so right.

PROMPT:

Please write a metafictional literary short story…

— Sam Altman (@sama) March 11, 2025

OpenAI launches new tools to help businesses build AI agents

OpenAI rolled out new tools designed to help developers and businesses build AI agents — automated systems that can independently accomplish tasks — using the company’s own AI models and frameworks. The tools are part of OpenAI’s new Responses API, which enables enterprises to develop customized AI agents that can perform web searches, scan through company files, and navigate websites, similar to OpenAI’s Operator product. The Responses API effectively replaces OpenAI’s Assistants API, which the company plans to discontinue in the first half of 2026.

OpenAI reportedly plans to charge up to $20,000 a month for specialized AI ‘agents’

OpenAI intends to release several “agent” products tailored for different applications, including sorting and ranking sales leads and software engineering, according to a report from The Information. One, a “high-income knowledge worker” agent, will reportedly be priced at $2,000 a month. Another, a software developer agent, is said to cost $10,000 a month. The most expensive rumored agents, which are said to be aimed at supporting “PhD-level research,” are expected to cost $20,000 per month. The jaw-dropping figure is indicative of how much cash OpenAI needs right now: The company lost roughly $5 billion last year after paying for costs related to running its services and other expenses. It’s unclear when these agentic tools might launch or which customers will be eligible to buy them.

ChatGPT can directly edit your code

The latest version of the macOS ChatGPT app allows users to edit code directly in supported developer tools, including Xcode, VS Code, and JetBrains. ChatGPT Plus, Pro, and Team subscribers can use the feature now, and the company plans to roll it out to more users like Enterprise, Edu, and free users.

ChatGPT’s weekly active users doubled in less than 6 months, thanks to new releases

According to a new report from VC firm Andreessen Horowitz (a16z), OpenAI’s AI chatbot, ChatGPT, experienced solid growth in the second half of 2024. It took ChatGPT nine months to increase its weekly active users from 100 million in November 2023 to 200 million in August 2024, but it only took less than six months to double that number once more, according to the report. ChatGPT’s weekly active users increased to 300 million by December 2024 and 400 million by February 2025. ChatGPT has experienced significant growth recently due to the launch of new models and features, such as GPT-4o, with multimodal capabilities. ChatGPT usage spiked from April to May 2024, shortly after that model’s launch.

February 2025

OpenAI cancels its o3 AI model in favor of a ‘unified’ next-gen release

OpenAI has effectively canceled the release of o3 in favor of what CEO Sam Altman is calling a “simplified” product offering. In a post on X, Altman said that, in the coming months, OpenAI will release a model called GPT-5 that “integrates a lot of [OpenAI’s] technology,” including o3, in ChatGPT and its API. As a result of that roadmap decision, OpenAI no longer plans to release o3 as a standalone model.

ChatGPT may not be as power-hungry as once assumed

A commonly cited stat is that ChatGPT requires around 3 watt-hours of power to answer a single question. Using OpenAI’s latest default model for ChatGPT, GPT-4o, as a reference, nonprofit AI research institute Epoch AI found the average ChatGPT query consumes around 0.3 watt-hours. However, the analysis doesn’t consider the additional energy costs incurred by ChatGPT with features like image generation or input processing.

OpenAI now reveals more of its o3-mini model’s thought process

In response to pressure from rivals like DeepSeek, OpenAI is changing the way its o3-mini model communicates its step-by-step “thought” process. ChatGPT users will see an updated “chain of thought” that shows more of the model’s “reasoning” steps and how it arrived at answers to questions.

You can now use ChatGPT web search without logging in

OpenAI is now allowing anyone to use ChatGPT web search without having to log in. While OpenAI had previously allowed users to ask ChatGPT questions without signing in, responses were restricted to the chatbot’s last training update. This only applies through ChatGPT.com, however. To use ChatGPT in any form through the native mobile app, you will still need to be logged in.

OpenAI unveils a new ChatGPT agent for ‘deep research’

OpenAI announced a new AI “agent” called deep research that’s designed to help people conduct in-depth, complex research using ChatGPT. OpenAI says the “agent” is intended for instances where you don’t just want a quick answer or summary, but instead need to assiduously consider information from multiple websites and other sources.

January 2025

OpenAI used a subreddit to test AI persuasion

OpenAI used the subreddit r/ChangeMyView to measure the persuasive abilities of its AI reasoning models. OpenAI says it collects user posts from the subreddit and asks its AI models to write replies, in a closed environment, that would change the Reddit user’s mind on a subject. The company then shows the responses to testers, who assess how persuasive the argument is, and finally OpenAI compares the AI models’ responses to human replies for that same post.

OpenAI launches o3-mini, its latest ‘reasoning’ model

OpenAI launched a new AI “reasoning” model, o3-mini, the newest in the company’s o family of models. OpenAI first previewed the model in December alongside a more capable system called o3. OpenAI is pitching its new model as both “powerful” and “affordable.”

ChatGPT’s mobile users are 85% male, report says

A new report from app analytics firm Appfigures found that over half of ChatGPT’s mobile users are under age 25, with users between ages 50 and 64 making up the second largest age demographic. The gender gap among ChatGPT users is even more significant. Appfigures estimates that across age groups, men make up 84.5% of all users.

OpenAI launches ChatGPT plan for US government agencies

OpenAI launched ChatGPT Gov designed to provide U.S. government agencies an additional way to access the tech. ChatGPT Gov includes many of the capabilities found in OpenAI’s corporate-focused tier, ChatGPT Enterprise. OpenAI says that ChatGPT Gov enables agencies to more easily manage their own security, privacy, and compliance, and could expedite internal authorization of OpenAI’s tools for the handling of non-public sensitive data.

More teens report using ChatGPT for schoolwork, despite the tech’s faults

Younger Gen Zers are embracing ChatGPT, for schoolwork, according to a new survey by the Pew Research Center. In a follow-up to its 2023 poll on ChatGPT usage among young people, Pew asked ~1,400 U.S.-based teens ages 13 to 17 whether they’ve used ChatGPT for homework or other school-related assignments. Twenty-six percent said that they had, double the number two years ago. Just over half of teens responding to the poll said they think it’s acceptable to use ChatGPT for researching new subjects. But considering the ways ChatGPT can fall short, the results are possibly cause for alarm.

OpenAI says it may store deleted Operator data for up to 90 days

OpenAI says that it might store chats and associated screenshots from customers who use Operator, the company’s AI “agent” tool, for up to 90 days — even after a user manually deletes them. While OpenAI has a similar deleted data retention policy for ChatGPT, the retention period for ChatGPT is only 30 days, which is 60 days shorter than Operator’s.

OpenAI launches Operator, an AI agent that performs tasks autonomously

OpenAI is launching a research preview of Operator, a general-purpose AI agent that can take control of a web browser and independently perform certain actions. Operator promises to automate tasks such as booking travel accommodations, making restaurant reservations, and shopping online.

OpenAI may preview its agent tool for users on the $200-per-month Pro plan

Operator, OpenAI’s agent tool, could be released sooner rather than later. Changes to ChatGPT’s code base suggest that Operator will be available as an early research preview to users on the $200 Pro subscription plan. The changes aren’t yet publicly visible, but a user on X who goes by Choi spotted these updates in ChatGPT’s client-side code. TechCrunch separately identified the same references to Operator on OpenAI’s website.

OpenAI tests phone number-only ChatGPT signups

OpenAI has begun testing a feature that lets new ChatGPT users sign up with only a phone number — no email required. The feature is currently in beta in the U.S. and India. However, users who create an account using their number can’t upgrade to one of OpenAI’s paid plans without verifying their account via an email. Multi-factor authentication also isn’t supported without a valid email.

ChatGPT now lets you schedule reminders and recurring tasks

ChatGPT’s new beta feature, called tasks, allows users to set simple reminders. For example, you can ask ChatGPT to remind you when your passport expires in six months, and the AI assistant will follow up with a push notification on whatever platform you have tasks enabled. The feature will start rolling out to ChatGPT Plus, Team, and Pro users around the globe this week.

New ChatGPT feature lets users assign it traits like ‘chatty’ and ‘Gen Z’

OpenAI is introducing a new way for users to customize their interactions with ChatGPT. Some users found they can specify a preferred name or nickname and “traits” they’d like the chatbot to have. OpenAI suggests traits like “Chatty,” “Encouraging,” and “Gen Z.” However, some users reported that the new options have disappeared, so it’s possible they went live prematurely.

FAQs:

What is ChatGPT? How does it work?

ChatGPT is a general-purpose chatbot that uses artificial intelligence to generate text after a user enters a prompt, developed by tech startup OpenAI. The chatbot uses GPT-4, a large language model that uses deep learning to produce human-like text.

When did ChatGPT get released?

November 30, 2022 is when ChatGPT was released for public use.

What is the latest version of ChatGPT?

Both the free version of ChatGPT and the paid ChatGPT Plus are regularly updated with new GPT models. The most recent model is GPT-4o.

Can I use ChatGPT for free?

There is a free version of ChatGPT that only requires a sign-in in addition to the paid version, ChatGPT Plus.

Who uses ChatGPT?

Anyone can use ChatGPT! More and more tech companies and search engines are utilizing the chatbot to automate text or quickly answer user questions/concerns.

What companies use ChatGPT?

Multiple enterprises utilize ChatGPT, although others may limit the use of the AI-powered tool.

Most recently, Microsoft announced at its 2023 Build conference that it is integrating its ChatGPT-based Bing experience into Windows 11. A Brooklyn-based 3D display startup Looking Glass utilizes ChatGPT to produce holograms you can communicate with by using ChatGPT. And nonprofit organization Solana officially integrated the chatbot into its network with a ChatGPT plug-in geared toward end users to help onboard into the web3 space.

What does GPT mean in ChatGPT?

GPT stands for Generative Pre-Trained Transformer.

What is the difference between ChatGPT and a chatbot?

A chatbot can be any software/system that holds dialogue with you/a person but doesn’t necessarily have to be AI-powered. For example, there are chatbots that are rules-based in the sense that they’ll give canned responses to questions.

ChatGPT is AI-powered and utilizes LLM technology to generate text after a prompt.

Can ChatGPT write essays?

Yes.

Can ChatGPT commit libel?

Due to the nature of how these models work, they don’t know or care whether something is true, only that it looks true. That’s a problem when you’re using it to do your homework, sure, but when it accuses you of a crime you didn’t commit, that may well at this point be libel.

We will see how handling troubling statements produced by ChatGPT will play out over the next few months as tech and legal experts attempt to tackle the fastest moving target in the industry.

Does ChatGPT have an app?

Yes, there is a free ChatGPT mobile app for iOS and Android users.

What is the ChatGPT character limit?

It’s not documented anywhere that ChatGPT has a character limit. However, users have noted that there are some character limitations after around 500 words.

Does ChatGPT have an API?

Yes, it was released March 1, 2023.

What are some sample everyday uses for ChatGPT?

Everyday examples include programming, scripts, email replies, listicles, blog ideas, summarization, etc.

What are some advanced uses for ChatGPT?

Advanced use examples include debugging code, programming languages, scientific concepts, complex problem solving, etc.

How good is ChatGPT at writing code?

It depends on the nature of the program. While ChatGPT can write workable Python code, it can’t necessarily program an entire app’s worth of code. That’s because ChatGPT lacks context awareness — in other words, the generated code isn’t always appropriate for the specific context in which it’s being used.

Can you save a ChatGPT chat?

Yes. OpenAI allows users to save chats in the ChatGPT interface, stored in the sidebar of the screen. There are no built-in sharing features yet.

Are there alternatives to ChatGPT?

Yes. There are multiple AI-powered chatbot competitors such as Together, Google’s Gemini and Anthropic’s Claude, and developers are creating open source alternatives.

How does ChatGPT handle data privacy?

OpenAI has said that individuals in “certain jurisdictions” (such as the EU) can object to the processing of their personal information by its AI models by filling out this form. This includes the ability to make requests for deletion of AI-generated references about you. Although OpenAI notes it may not grant every request since it must balance privacy requests against freedom of expression “in accordance with applicable laws”.

The web form for making a deletion of data about you request is entitled “OpenAI Personal Data Removal Request”.

In its privacy policy, the ChatGPT maker makes a passing acknowledgement of the objection requirements attached to relying on “legitimate interest” (LI), pointing users towards more information about requesting an opt out — when it writes: “See here for instructions on how you can opt out of our use of your information to train our models.”

What controversies have surrounded ChatGPT?

Recently, Discord announced that it had integrated OpenAI’s technology into its bot named Clyde where two users tricked Clyde into providing them with instructions for making the illegal drug methamphetamine (meth) and the incendiary mixture napalm.

An Australian mayor has publicly announced he may sue OpenAI for defamation due to ChatGPT’s false claims that he had served time in prison for bribery. This would be the first defamation lawsuit against the text-generating service.

CNET found itself in the midst of controversy after Futurism reported the publication was publishing articles under a mysterious byline completely generated by AI. The private equity company that owns CNET, Red Ventures, was accused of using ChatGPT for SEO farming, even if the information was incorrect.

Several major school systems and colleges, including New York City Public Schools, have banned ChatGPT from their networks and devices. They claim that the AI impedes the learning process by promoting plagiarism and misinformation, a claim that not every educator agrees with.

There have also been cases of ChatGPT accusing individuals of false crimes.

Where can I find examples of ChatGPT prompts?

Several marketplaces host and provide ChatGPT prompts, either for free or for a nominal fee. One is PromptBase. Another is ChatX. More launch every day.

Can ChatGPT be detected?

Poorly. Several tools claim to detect ChatGPT-generated text, but in our tests, they’re inconsistent at best.

Are ChatGPT chats public?

No. But OpenAI recently disclosed a bug, since fixed, that exposed the titles of some users’ conversations to other people on the service.

What lawsuits are there surrounding ChatGPT?

None specifically targeting ChatGPT. But OpenAI is involved in at least one lawsuit that has implications for AI systems trained on publicly available data, which would touch on ChatGPT.

Are there issues regarding plagiarism with ChatGPT?

Yes. Text-generating AI models like ChatGPT have a tendency to regurgitate content from their training data.

This story is continually updated with new information.

The TechCrunch AI glossary | TechCrunch

Posted on March 2, 2025 by faz_business

Artificial intelligence is a deep and convoluted world. The scientists who work in this field often rely on jargon and lingo to explain what they’re working on. As a result, we frequently have to use those technical terms in our coverage of the artificial intelligence industry. That’s why we thought it would be helpful to put together a glossary with definitions of some of the most important words and phrases that we use in our articles.

We will regularly update this glossary to add new entries as researchers continually uncover novel methods to push the frontier of artificial intelligence while identifying emerging safety risks.

An AI agent refers to a tool that makes use of AI technologies to perform a series of tasks on your behalf — beyond what a more basic AI chatbot could do — such as filing expenses, booking tickets or a table at a restaurant, or even writing and maintaining code. However, as we’ve explained before, there are lots of moving pieces in this emergent space, so different people can mean different things when they refer to an AI agent. Infrastructure is also still being built out to deliver on envisaged capabilities. But the basic concept implies an autonomous system that may draw on multiple AI systems to carry out multi-step tasks.

Given a simple question, a human brain can answer without even thinking too much about it — things like “which animal is taller between a giraffe and a cat?” But in many cases, you often need a pen and paper to come up with the right answer because there are intermediary steps. For instance, if a farmer has chickens and cows, and together they have 40 heads and 120 legs, you might need to write down a simple equation to come up with the answer (20 chickens and 20 cows).

In an AI context, chain-of-thought reasoning for large language models means breaking down a problem into smaller, intermediate steps to improve the quality of the end result. It usually takes longer to get an answer, but the answer is more likely to be right, especially in a logic or coding context. So-called reasoning models are developed from traditional large language models and optimized for chain-of-thought thinking thanks to reinforcement learning.

(See: Large language model)

Deep learning AIs are able to identify important characteristics in data themselves, rather than requiring human engineers to define these features. The structure also supports algorithms that can learn from errors and, through a process of repetition and adjustment, improve their own outputs. However, deep learning systems require a lot of data points to yield good results (millions or more). It also typically takes longer to train deep learning vs. simpler machine learning algorithms — so development costs tend to be higher.

(See: Neural network)

This means further training of an AI model that’s intended to optimize performance for a more specific task or area than was previously a focal point of its training — typically by feeding in new, specialized (i.e. task-oriented) data.

Many AI startups are taking large language models as a starting point to build a commercial product but vying to amp up utility for a target sector or task by supplementing earlier training cycles with fine-tuning based on their own domain-specific knowledge and expertise.

(See: Large language model (LLM))

AI assistants and LLMs can have different names. For instance, GPT is OpenAI’s large language model and ChatGPT is the AI assistant product.

Those are created from encoding the patterns they find in billions of books, articles, and transcripts. When you prompt an LLM, the model generates the most likely pattern that fits the prompt. It then evaluates the most probable next word after the last one based on what was said before. Repeat, repeat, and repeat.

(See: Neural network)

Neural network refers to the multi-layered algorithmic structure that underpins deep learning — and, more broadly, the whole boom in generative AI tools following the emergence of large language models.

Although the idea to take inspiration from the densely interconnected pathways of the human brain as a design structure for data processing algorithms dates all the way back to the 1940s, it was the much more recent rise of graphical processing hardware (GPUs) — via the video game industry — that really unlocked the power of theory. These chips proved well suited to training algorithms with many more layers than was possible in earlier epochs — enabling neural network-based AI systems to achieve far better performance across many domains, whether for voice recognition, autonomous navigation, or drug discovery.

(See: Large language model (LLM))

Weights are core to AI training as they determine how much importance (or weight) is given to different features (or input variables) in the data used for training the system — thereby shaping the AI model’s output.

Put another way, weights are numerical parameters that define what’s most salient in a data set for the given training task. They achieve their function by applying multiplication to inputs. Model training typically begins with weights that are randomly assigned, but as the process unfolds, the weights adjust as the model seeks to arrive at an output that more closely matches the target.

For example, an AI model for predicting house prices that’s trained on historical real estate data for a target location could include weights for features such as the number of bedrooms and bathrooms, whether a property is detached, semi-detached, if it has or doesn’t have parking, a garage, and so on.

Ultimately, the weights the model attaches to each of these inputs is a reflection of how much they influence the value of a property, based on the given data set.

DeepSeek: Everything you need to know about the AI chatbot app

Posted on January 28, 2025 by faz_business

DeepSeek has gone viral.

Chinese AI lab DeepSeek broke into the mainstream consciousness this week after its chatbot app rose to the top of the Apple App Store charts. DeepSeek’s AI models, which were trained using compute-efficient techniques, have led Wall Street analysts — and technologists — to question whether the U.S. can maintain its lead in the AI race and whether the demand for AI chips will sustain.

But where did DeepSeek come from, and how did it rise to international fame so quickly?

DeepSeek’s trader origins

DeepSeek is backed by High-Flyer Capital Management, a Chinese quantitative hedge fund that uses AI to inform its trading decisions.

AI enthusiast Liang Wenfeng co-founded High-Flyer in 2015. Wenfeng, who reportedly began dabbling in trading while a student at Zhejiang University, launched High-Flyer Capital Management as a hedge fund in 2019 focused on developing and deploying AI algorithms.

In 2023, High-Flyer started DeepSeek as a lab dedicated to researching AI tools separate from its financial business. With High-Flyer as one of its investors, the lab spun off into its own company, also called DeepSeek.

From day one, DeepSeek built its own datacenter clusters for model training. But like other AI companies in China, DeepSeek has been affected by U.S. export bans on hardware. To train one of its more recent models, the company was forced to use Nvidia H800 chips, a less-powerful version of a chip, the H100, available to U.S. companies.

DeepSeek’s technical team is said to skew young. The company reportedly aggressively recruits doctorate AI researchers from top Chinese universities. DeepSeek also hires people without any computer science background to help its tech better understand a wide range of subjects, per The New York Times.

DeepSeek’s strong models

DeepSeek unveiled its first set of models — DeepSeek Coder, DeepSeek LLM, and DeepSeek Chat — in November 2023. But it wasn’t until last spring, when the startup released its next-gen DeepSeek-V2 family of models, that the AI industry started to take notice.

DeepSeek-V2, a general-purpose text- and image-analyzing system, performed well in various AI benchmarks — and was far cheaper to run than comparable models at the time. It forced DeepSeek’s domestic competition, including ByteDance and Alibaba, to cut the usage prices for some of their models, and make others completely free.

DeepSeek-V3, launched in December 2024, only added to DeepSeek’s notoriety.

According to DeepSeek’s internal benchmark testing, DeepSeek V3 outperforms both downloadable, openly available models like Meta’s Llama and “closed” models that can only be accessed through an API, like OpenAI’s GPT-4o.

Equally impressive is DeepSeek’s R1 “reasoning” model. Released in January, DeepSeek claims R1 performs as well as OpenAI’s o1 model on key benchmarks.

Being a reasoning model, R1 effectively fact-checks itself, which helps it to avoid some of the pitfalls that normally trip up models. Reasoning models take a little longer — usually seconds to minutes longer — to arrive at solutions compared to a typical non-reasoning model. The upside is that they tend to be more reliable in domains such as physics, science, and math.

There is a downside to R1, DeepSeek V3, and DeepSeek’s other models, however. Being Chinese-developed AI, they’re subject to benchmarking by China’s internet regulator to ensure that its responses “embody core socialist values.” In DeepSeek’s chatbot app, for example, R1 won’t answer questions about Tiananmen Square or Taiwan’s autonomy.

A disruptive approach

If DeepSeek has a business model, it’s not clear what that model is, exactly. The company prices its products and services well below market value — and gives others away for free.

The way DeepSeek tells it, efficiency breakthroughs have enabled it to maintain extreme cost competitiveness. Some experts dispute the figures the company has supplied, however.

Whatever the case may be, developers have taken to DeepSeek’s models, which aren’t open source as the phrase is commonly understood but are available under permissive licenses that allow for commercial use. According to Clem Delangue, the CEO of Hugging Face, one of the platforms hosting DeepSeek’s models, developers on Hugging Face have created over 500 “derivative” models of R1 that have racked up 2.5 million downloads combined.

DeepSeek’s success against larger and more established rivals has been described as “upending AI” and ushering in “a new era of AI brinkmanship.” The company’s success was at least in part responsible for causing Nvidia’s stock price to drop by 18% on Monday, and for eliciting a public response from OpenAI CEO Sam Altman.

As for what DeepSeek’s future might hold, it’s not clear. Improved models are a given. But the U.S. government appears to be growing wary of what it perceives as harmful foreign influence.

TechCrunch has an AI-focused newsletter! Sign up here to get it in your inbox every Wednesday.