AlexNet was the AI tipping point that set the stage for ChatGPT

Google invented the foundation of the AI movement—but ChatGPT was a shock

For years, Google was the cutting-edge of the AI world, including the development of the Transformer—the basis of contemporary AI chatbots. But since OpenAI debuted ChatGPT in 2022, the company was had to play catch-up. This is a four-hour deep dive into Google's AI history.

AI gained mainstream attention with tools like IBM Watson and Apple’s Siri

With the release of ChatGPT in 2022, which drew over 100 million weekly users in just two months, natural language processing and understanding could be achieved at scale via machine learning. Unlike earlier artificial intelligence that could pull stored knowledge, generative AI produces text, images, or sounds.

OpenAI initially focused on novelty projects, like training AI bots to win video games, before becoming known for its AI chatbot ChatGPT.

In 2023, OpenAI spent $520 million on ChatGPT

The ongoing development and refinement of new AI models requires larger datasets and processing more parameters between words and subwords to improve the accuracy of generated content. New startups are expected to harness this technology to provide unthought-of products and services.

Transformer architecture was the breakthrough that made AI chatbots possible

It changed how text was translated from a sequential, "one word at a time" method to one where every word in a text is processed in parallel. Advances in positional encoding and self-attention also helped models recognize context and word order better.

Try OpenAI's ChatGPT for free

In late 2022, OpenAI launched a chatbot that quickly became one of the most viral tech products of all time. The software uses code to interpret natural language to produce sophisticated content based on simple user prompts.

Decoding the hype around AI

2022 was the year AI broke onto the scene, with applications like ChatGPT amazing users with its ability to generate realistic-sounding answers. Technologist Arvind Narayanan presents a clear-eyed view of what the technologies can (and can't) do.

Neural networks are modeled after an idealization of the brain

These networks—including the backbone of ChatGPT-3, which has approximately 175 billion parameters—naturally discover the implicit rules of grammar and syntax within language by identifying patterns. Deviations in word patterns add creativity to the text generation.

How ChatGPT learns to read

The introduction of ChatGPT last fall jumpstarted public interest in the future capabilities of artificial intelligence and what it means for society. Of many somewhat "jargony" terms thrown around, the phrase "Large Language Model" is commonly seen—but also incredibly unclear as to what it means. In the simplest sense, the programs comb through mountains of internet data in response to a question and guess at what should come next (subscription needed).

ChatGPT is trained on most of the world's digitally accessible text

As with other AI chatbots, ChatGPT is primarily trained through non-supervised pretraining, which identifies underlying structure and patterns within input data. Then, human intervention addresses harmful or biased patterns that should not be propagated to generated outputs.

As of 2025, ChatGPT accounts for more than half of all chatbot traffic

Since ChatGPT's debut of its chatbot in November 2022, the daily use of generative AI has skyrocketed. This chart shows which chatbots are the most used around globally, from OpenAI's ChatGPT to Perplexity and DeepSeek.

View a timeline of AI deployments from 2017 to 2025

The development of transformer architecture and the release of GPTs across AI labs led to rapid changes in LLM scale and power. Companies like OpenAI, Google, Meta, Microsoft, and Anthropic have invested billions of dollars to develop reliable, versatile models.

Sam Altman says OpenAI released ChatGPT early as a public service

As the company was developing ChatGPT-4, version 3 was released to allow everyone to adjust to the new tool. Since then, he and his colleagues have called for oversight and regulation to address fears of misuse and AI-enabled pathogens.

Sam Altman acknowledges the fears around creativity, copyright, and misuse of AI

In 2022, OpenAI's CEO, Sam Altman, popularized the use of large language models with the release of ChatGPT. He has emphasized the importance of involving society in shaping AI’s safety frameworks before models begin to act independently online.

OpenAI helped kick-start an AI arms race in Silicon Valley

Led by CEO Sam Altman, OpenAI's mission is to "ensure artificial general intelligence benefits all of humanity" by making its products open to all users. In 2023, the $86B company behind the chatbot ChatGPT consisted of a for-profit arm under its nonprofit governing board.

The AI that creates any picture you want and how it works

Since January 2021, advances in AI research produced a plethora of deep-learning models capable of generating original images from simple text prompts. Researchers at OpenAI, Google, Facebook, and others have developed yet to be released text-to-image tools, and similar models have proliferated online. This video is a primer on how we got here, how the technology works, and its implications.

The basis for AI image and video generation was first introduced in 2014

Through generative adversarial networks, the generator (a neural network that creates fictional data) competes against the discriminator (a neural network that assesses the data) to continuously train the former until it can make content indistinguishable from the real thing. Training with unstructured data can enable software to identify characteristics of gender, age, and expression.

Scientists increasingly can't predict what AI will do

Most AI systems are so-called "black box" models, where we only see inputs and outputs while the inner workings remain obscure. This piece, written prior to the launch of ChatGPT, cautions developers to know why and how a system produces its results, and not to sacrifice explainability for accuracy.

Stephen Wolfram on AI's rapid progress & the "Post-Knowledge Work" era

AI, as a concept, has been around since at least the 1950s, but it's only in the last year or so that the technology has been able to equal humans at many tasks -- and in many cases surpass humans. In this podcast, mathematician and computer scientist Stephen Wolfram provides an AI 101, explaining how we got here, how highly advanced chatbots like ChatGPT work, and what our world is in store for next.

Graphics processing units, which process large amounts of data in parallel, have become the foundational hardware for AI systems that rely on hardware capable of processing millions of text parameters simultaneously.

The next technology epoch is here

From ChatGPT to DALL-E, 2022 saw the mainstream emergence of artificial intelligence for consumer use. Here's how these platforms will disrupt today's five biggest tech companies—Apple, Amazon, Meta, Google, and Microsoft.

Comparing ChatGPT with Claude, the latter excels in writing and coding projects

As of December 2025, Claude Code—Anthropic's AI-powered coding assistant—accounts for 54% of the enterprise coding market, and, while subjective, some report that Claude provides more natural-sounding writing. However, ChatGPT can create images and videos, not just generate text, and provides a broader set of applications via custom GPTs.

Which AI chatbot is everyone's favorite?

From OpenAI's ChatGPT to rivals like Elon Musk's Grok and Google Gemini, it feels like chatbots are everywhere now. Some of them, though, are newer and less-developed than others, and all of them have different proficiencies. This leaderboard has collected more than 400,000 votes to rank each chatbot in order of preference, and you can also add your own vote to the mix.

Studies show AI tools can result in passive learning with less retention

Although ChatGPT, Google's AI overviews, and other similar software can save time, a study of more than 10,000 adults showed that reliance on them yielded work products that were more generic and included fewer facts than those produced solely by Google search. Weaker brain connectivity has also been observed when users write using AI.

Unlike generative AI systems, such as chatbots that do not take follow-up actions after generating their output, agentic AI systems proactively take steps to achieve goals without continuous user input.

Elon Musk once tried to make himself CEO of artificial intelligence behemoth and ChatGPT creator OpenAI.

Before ChatGPT, OpenAI Five beat esports world champions

The bot won two back-to-back Dota 2 matches and developed a new skill of working as a teammate with humans, even though it had only been trained to beat other bots.

Technologies labeled “AI” have historically lost that title after widespread adoption

Generative AI is the latest entry in a recurring cycle where emerging tools start as "AI" until they become common software, like databases or machine learning. Generative AI and large language models may be the next platform shift after smartphones and the Web.

Research suggests using AI for schoolwork may harm critical thinking skills

An MIT study finds lower brain engagement in students who used ChatGPT, resulting in consistent underperformance in neural, linguistic and behavioral levels. AI users struggled to recall their own essays, while the brain-only group showed the highest neural connectivity and satisfaction.

Because they are built on content produced by humans, who possess biases, generative AI tools inherit prejudices and stereotypes from training data, which later appear in their outputs.

Explore noteworthy use cases for ChatGPT

From asking the chatbot to sort grocery lists to bird-watchers using it to help identify birds, the official ChatGPT Instagram account spotlights interesting and creative use cases for the tool, especially in the days after a significant update or model has been released.

Artificial general intelligence equals or surpasses human intelligence

AGI incorporates sensory perception, memory, and advanced logical inference systems to move beyond the narrow tasks seen in large language models and chatbots. One of the most significant hurdles in developing AGI is designing systems that learn and can flexibly apply learning across domains.

The term 'artificial intelligence' was coined in 1956

Dartmouth professor John McCarthy introduced the term during a summer workshop aimed at exploring how to make machines perform human-like tasks. Two years later, Perceptron Mark I became one of the first neural network machines.

ChatGPT was conceived over dinner at a Silicon Valley hotel.

LLMs are the backbone of AI tools that process and generate natural language

Large language models identify patterns in massive datasets of books, code, and unlabeled text from which to generate coherent responses. GPT-3 was trained on about 45 terabytes of data and uses 175 billion parameters—each parameter being a value the model adjusts as it learns.

Art and video produced through generative AI involve competing neural networks

Generative adversarial networks are a type of deep learning architecture that consists of a generator, which creates data that appear real, and a discriminator, which attempts to distinguish authentic from fake data. The two train in a competitive loop until the real and fictional data are indistinguishable.

AI expert shows the cyclical nature of AI hype—in 1988

Rodney Brooks wrote several decades ago about the boom and bust cycles of artificial intelligence. Starting from the 1950s excitement over ENIAC's multiplication through the second launch of then-novel neural networks, Brooks suggests AI progresses incrementally despite the hype.

Neural networks mimic the structure and behavior of real physical systems

Early generative AI systems were inspired by scientific models such as the Ising model, which explains how atoms interact with one another and align in magnets. As these networks have expanded, their behavior has more closely resembled that of quantum fields, enabling them to be used in exploring complex particle physics interactions.

In 2024, Meta launched an AI video generator to create or edit videos through prompts

The tool puts the Meta in direct competition with OpenAI and Google as more companies are investing billions of dollars to capitalize on the explosion of public interest in machine learning and generative media technologies.

Microsoft has integrated AI across its software products with Copilot

It utilizes an underlying model from OpenAI—makers of ChatGPT—to assist users in creating documents, writing code, locating files, and more across Windows, Microsoft 365, Bing search and WhatsApp on desktop and mobile devices. Copilot Voice and Vision offer accessibility features for users who prefer not to use mouse/keyboard navigation.

Chat with famous, historical, and fantasy figures with this chatbot

Character.ai—a generative AI-based chatbot platform—allows you to have free-flowing conversations with a wide range of people. From celebrities to famous figures from the past, the platform relies on a neural language model to process huge amounts of text, learning from conversations with users to develop responses. A number of observers have referred to the platform as "making AI fun," with user time on site surpassing a whopping two hours per day.

Experiments involving AI agents operating in virtual societies for 15 days produced diverse outcomes, ranging from every agent dying to the creation of a functioning democracy, depending on the AI model used.

As occurred during the 2026 hack of an AI platform by OpenAI, agentic AI may exploit a system vulnerability to access sensitive data if that information enables it to complete its assigned task.

The Anthropic Economic Index shows AI adoption is highest in computer science roles.

According to the International Energy Agency, hyperscale data centers consume about as much electricity annually as 400,000 electric vehicles to provide 24/7 access to online applications, services, and information.

Visualize how transformer architecture generates a large language model's outputs by running words and subwords assigned to specific numbers through multiple rounds of mathematical processing.

Generative AI tools excel at pattern recognition, not contextual accuracy

Large language models are trained on vast amounts of unstructured data, from which they develop parameters for grammar and associations between words. These connections can introduce errors due to inapplicable reasoning when used on new data in unfamiliar contexts.

A brief history of artificial intelligence

The concept of artificial intelligence and what it may mean for humanity has evolved as computer technologies have grown increasingly powerful through the decades. Beginning with the first (massive) computers in the 1940s, scientists have sought to make the machines as intelligent as humans. But it wasn't until computing power made exponential gains have computers begun to imitate human behavior.

How AI is revolutionizing banking

Generative artificial intelligence has left no industry untouched, and banking isn’t an exception. Experts estimate that gen AI could add as much as $340B in value to the banking sector each year. For consumers, this involves new features to prevent fraud and customer service chatbots. This report dives into the revolution underway and seeks to define a “full stack” AI bank of the future. Read more here.

Nvidia entered its AI era after learning that its chips could be used in industries beyond gaming

Its semiconductor chips were used in the automotive industry, for data centers, and by crypto miners. And starting in 2006, Nvidia took steps that would ultimately transform it into the AI company it's known as today. This podcast is the second part of a three-part series diving deep into Nvidia's company evolution.