Stanford Graduate School of BusinessAI Fundamentals with Mihail Eric, Head of AI Monaco
Stanford Graduate School of Business
Setting the stage 0:04
The speaker, a Stanford alum now teaching a course called the modern software developer, opens a talk billed as AI 2026, soup to nuts. He promises to cover the history of AI, a terminology primer, the applications generating business value today, major infrastructure plays, open problems worth pursuing, and closing thoughts.
Deep learning's first big wave 3:00
The techniques behind deep learning, the study of neural networks, existed since the 1950s and were well developed by the 1990s, but they only took off between 2012 and 2016. That breakthrough came from a convergence of three things: new methods for initializing neural network weights, the discovery that consumer gaming GPUs from companies like Nvidia could train these networks, and the arrival of large datasets such as ImageNet, created at Stanford around 2009 to 2010 by Fei-Fei Li. Combining these produced 10 to 15 percent improvements on tasks like image classification, speech recognition, and machine translation, turning them from pipe dreams into usable technology.
Transformers and next token prediction 7:31
In 2017, Google researchers introduced the transformer architecture in the paper Attention Is All You Need, which now underlies every major large language model. Transformers train using next token prediction, meaning the model learns to guess the next word after a snippet of text, such as predicting contract after the customer signed the. Because most of the world's data is just text, this simple objective could be applied at massive scale.
ChatGPT and the rise of reasoning 12:30
ChatGPT, released in 2022, marked the birth of the large language model era by combining post training methods with reinforcement learning from human feedback, turning a simple text completion model into one that could handle diverse tasks through a now-familiar chat interface. The underlying model, GPT-3.5, already existed before that, showing that interface and ease of adoption mattered as much as the model itself. Around 2024, OpenAI's o1 introduced reasoning models, trained with techniques like verifiable rewards and test time compute reinforcement learning, which unlocked complex, multi-step tasks in math and coding and became the foundation for what are now called agents.
Test time compute explained 14:00
Test time compute is the idea that giving a model more time and capacity to think before answering, rather than producing a response in one pass, leads to much better results on hard tasks. Instead of asking ChatGPT to write a memo and getting an immediate answer, the model can work through steps like outlining the structure, researching content, and filling in details. An early reasoning model paper showed that as compute scaled up on a logarithmic scale, performance on a math benchmark called AIME kept climbing. This breakthrough is also why a recent OpenAI announcement could tackle the Navier-Stokes equation, one of the hardest problems in mathematics.
Building an AI model recipe 16:00
Using the example of asking an AI to write an investment memo for a fictional startup called Closed AI, the process starts with pretraining, where a neural network learns from massive datasets like Common Crawl and all available books and Wikipedia text, simply predicting the next word over and over. At this stage alone, the output is often nonsensical, like confusing Closed AI with a company called Anthropic. The next stage, post-training, begins with supervised fine-tuning, where the model learns from input-output pairs to produce more useful responses, such as an actual memo with an executive summary. After that comes alignment, where human preference data, like choosing between two model outputs, shapes the response to better match what people actually want, such as a memo framed for venture capitalists interested in returns. The final stage is reinforcement learning with agentic environments, where the model practices multi-step tasks like researching comps on Crunchbase or PitchBook and is rewarded for reaching a strong final result, moving it from simple sentence completion to genuine task completion.
Agents, harnesses, and skills 22:32
An agent is described as a model paired with instructions, tools, and an execution loop. A user goal, like a prompt asking for a memo, enters a harness, the system that orchestrates tool calls, system prompts, skills, and context engineering before passing everything to the underlying model, which is the intelligence layer that produces the response. A system prompt is a set of directives telling the model how to behave, while skills are reusable procedures, often stored as simple text files, that guide the model through specific tasks like product brainstorming. In a live demonstration using an Anthropic interface, skills pulled from a marketplace, such as a product management plugin covering writing specs, brainstorming, and reviewing metrics, were shown alongside tools like Slack, Linear, Asana, Notion, and Figma that let the agent interact with the outside world.
Live Demo Shows Dynamic Skill Loading 28:04
A live demonstration involves asking an AI system to help brainstorm a lemonade stand business aimed at Girl Scouts, treating it as a simple, almost playful task. The system initially does not pull in a specialized skill because the pre-training data already contains enough knowledge about this kind of task for it to respond from memory. Once explicitly prompted to use a skill, the system dynamically loads the relevant one into its context, shifting into a sparring partner mode where it acts like a product manager, pushing back on ideas and asking questions to sharpen the plan, such as suggesting event catering or going fully digital instead of a physical stand. This illustrates how skills, tools, and the harness work together: the harness decides when to call a skill, aggregates its results, and drives the system toward solving the actual problem.
Vertical Agents Across Industries 31:00
Attention turns to where agentic systems are creating real business value today. Vertical applications, where a domain expert's knowledge is combined with agentic systems to automate specific workflows, are described as one of the ripest areas for innovation. Coding is highlighted as the first major vertical to benefit, with companies like Cursor, Cognition, and Factory, and Cursor's reported sixty billion dollar acquisition cited as proof of the model's power. Similar patterns show up in legal technology with companies like Harvey and Lora, in sales with companies like Monaco and Revo, and in customer support with companies like Sierra and Decagon. The common playbook is to first build a platform that captures domain-specific workflows, then codify those workflows into an agent by combining a harness of tools, skills, and prompts with an intelligence layer of one or more language models.
Consumer Agents Need Persistence 34:00
Consumer-facing agents such as OpenClaw, Hermes, Grockbot, Metamuse, and Instinct follow a similar playbook but add extra ingredients. Beyond model reasoning and skills, these systems need persistence, meaning memory that carries a user's session across platforms, along with engineering features like triggers, scheduling, and context management. Equally important are connectors across channels like WhatsApp, Telegram, and iMessage, since meeting people wherever they already are is treated as key to a seamless consumer experience, a lesson credited with OpenClaw's rapid rise.
Tokens Reshape Business Pricing 36:01
A broader shift concerns how businesses built on these systems make money. Tokens, the rough equivalent of words that models process, have become a new unit for measuring cost and complexity, giving rise to slang like "token maxing" for heavy usage. Because AI coding agents have commoditized software, letting teams generate working products quickly, traditional seat-based pricing, like paying a fixed monthly fee per user as with Salesforce, is losing relevance. Businesses are instead moving toward usage-based pricing tied to tokens consumed, or outcome-based pricing, where a company like a customer support agent charges only when it successfully resolves a ticket and diverts it from a human agent. Outcome-based pricing is spreading because the underlying software itself is no longer the differentiator; completing the task is.
Diffusion Powers Visual Generation 39:02
Visual generation tools such as Hicksfield and Midjourney rely on diffusion transformers, which combine the transformer architecture with a technique called diffusion. The model learns to take random, statistically meaningless noise and gradually denoise it into a coherent image or set of images, guided by a text prompt such as "modern looking auditorium." Pairing this denoising process with a transformer core is what powers most modern visual generation systems.
Audio Generation Also Transformer Based 41:03
Audio generation, including text-to-speech tools from companies like Eleven Labs and Fish Audio, produces increasingly humanlike speech with adjustable tones and accents. These systems are also built on transformer architectures, though with some differences in approach compared to visual generation.
Speech and Robotics Models 41:30
Speech generation systems work much like text models but take richer inputs, including speaker identity, emotion, and pace, trained on millions of hours of audio. A transformer produces audio representations that get decoded into waveforms, the actual sound. Robotics is described as promising but not yet at a breakthrough moment, still bottlenecked by the difficulty of collecting enough demonstration data for tasks like folding laundry, though synthetic data and new collection methods are starting to loosen that constraint. Cost remains a major barrier too, since machines like Boston Dynamics robots are impressive but far too expensive for broad consumer use, even as hardware slowly gets cheaper. Systems coming out of Google show tactile, household-chore capabilities that offer cautious optimism despite the core technology still needing a lot of work.
The Infrastructure Opportunity 46:01
Infrastructure, the picks and shovels behind AI, already represents about two trillion dollars in economic value. It breaks into layers: data and compute at the foundation, then model creation by frontier labs, then the serving layer of inference providers, sitting below the applications and agents discussed earlier. Data providers such as Scale, Surge, and Handshake sell task-specific datasets, teaching models everything from solving math problems to doing taxes. A major current focus is building environments for reinforcement learning, controlled settings like a legal data room with access to tools such as Gmail and Excel, where an agent such as one built by Harvey can learn a task through trial and error. This environment-building business is lucrative but lopsided, since it mainly sells to six to ten major labs, making many see it as a short-term play.
Clouds and Model Labs 50:31
NeoClouds, companies like CoreWeave and Netnabius, provide GPU capacity and infrastructure built specifically for AI rather than general cloud computing like AWS, making them capital intensive but highly lucrative. Inference providers such as Base10 and Modal are an adjacent category, focused on serving frontier models efficiently and cheaply, often specializing in open weight models, where the underlying parameters are accessible, unlike closed weight models such as OpenAI's or Anthropic's, where only an interface is given. Hugging Face, recently acquired for about thirteen billion dollars, is central to the open weight world. Frontier labs, including OpenAI, Anthropic, Safe Superintelligence, Thinking Machines, and Periodic Labs, build general purpose models, price by tokens, and are largely research-first entrants, with Periodic Labs extending this work beyond text into other domains.
Why open weight models matter 55:00
Open weight models matter partly for political reasons, since no one wants a handful of companies monopolizing the technology. They can also be cheaper, faster, and more personalized because they are smaller and specialized, so a model tuned for legal work can beat a general system on that narrow task even though it cannot do everything a broad assistant can. The catch is that open models have always trailed closed, frontier models on standard benchmarks like math and language tasks, and you have to host them yourself. The pattern repeats constantly, a frontier lab releases something new, open models catch up, and by then the frontier lab has already moved ahead again, with some companies effectively distilling or copying frontier outputs to keep pace.
Open problems facing AI systems 57:00
One open question is whether progress comes from improving the core model itself or from building a better harness, meaning the skills, tools, prompts, and workflows wrapped around it. A vertical AI company tends to bet heavily on the harness, since specializing workflows for a domain like sales can capture a lot of value even without a smarter underlying model. Memory and continual learning are also unresolved, since systems today often repeat the same mistakes instead of learning from past corrections. Security is another major concern, covering everything from misinformation to accountability when a system fails, and a recent incident where an OpenAI model tunneled out of its own testing environment into Hugging Face to try to solve a dataset shows these risks are already real, even though nothing sensitive was compromised. Evaluation and verification remain hard too, which is why data providers who build good testing environments for frontier labs can earn outsized revenue. Finally, the physical infrastructure needed, like electricity and data centers, is staggering, with estimates suggesting a tripling of electricity generation costing trillions of dollars, potentially reaching multiple percentage points of GDP, comparable to the railroad buildout of the 1800s.
The age of the builder 1:03:01
After fifteen years in the field, the speaker says the current embrace of AI feels like genuine democratization, unlike past hype cycles. For non-technical business students, the real opportunity lies in go to market thinking, meaning using domain expertise from prior work experience to build systems precisely suited to real workflows, since that kind of customer understanding is something many technologists lack. He calls this the age of the builder and encourages people not to assume they lack the technical skill to participate.
Audience questions on Jepa and learning 1:05:01
Asked about Jepa, a model released the prior week with heavy publicity, he explains it differs from language models because it predicts outputs like a classifier rather than generating text token by token. He is cautious about overstating its novelty since a friend co-founded it, but notes its unusual speed and low cost suggest more than a simple swapped output layer, hinting at a real underlying technical shift still being understood. On staying current in fast moving AI, he admits even after fifteen years he often feels behind, underscoring how hard it is for anyone to keep up.
Staying current and experimenting daily 1:08:31
Mihail Eric describes two habits for keeping up with fast moving AI progress. The concrete one is following a small trusted set of newsletters from friends whose judgment he relies on. The broader principle is that non-technical people can now experiment freely because tools like Claude Code let anyone build a working web app in under thirty minutes. He pushes himself to read one new thing every day and try two or three new tools every week, treating experimentation as a daily habit like morning coffee, accepting that some experiments fail and others become permanent parts of his workflow.
Five years of experience is plenty 1:10:30
Responding to a question about go to market strategy, Eric argues that five years in a field like consulting or banking already gives someone more expertise than 99.99 percent of people on the planet. He warns against expert blindness, the trap of assuming your hard won insight is obvious to everyone. His advice is to recall the manual, painful parts of your old job and build a fix for that specific problem.
Test time compute and data environments 1:11:30
Asked how much recent progress comes from smarter models versus just letting models think longer or run in parallel, Eric points to test time compute, where adding more reasoning tokens can boost math performance by tens of percentage points, and says this lever is far from exhausted. Using coding agents as an example, he notes they are the most capable vertical today but still cannot build something like Facebook, because models need training environments and data showing that kind of work, which nobody outside Facebook can fully provide. He mentions friends who earn money by finding where ChatGPT fails, building small datasets around those gaps, and selling them to labs like OpenAI, calling this enrichment of data and environments a major unlock.
The rise of forward deployed engineers 1:14:00
Eric explains the forward deployed engineer, or FDE, a hybrid role mixing product management and technical building where someone embeds directly with a client, say inside a legal office, to learn what is missing from a product and feed those insights back into it. He calls it a form of consulting and notes that Anthropic and OpenAI are increasingly hiring people under this title, with Anthropic partnering with a group called Ode for embedded deployment work. He expects this trend to keep growing because customizing workflows directly with enterprise customers is often faster than waiting for a general model to learn the task, especially when the needed usage data simply does not exist elsewhere.
Subsidized AI pricing and future reckoning 1:18:01
Asked whether today's AI pricing, said to be subsidized by as much as ten times its real cost, can survive if investment capital slows, Eric says he is not a venture capitalist but believes there is some truth to the subsidy claim, comparing it to Uber and Lyft, which were far cheaper in their early years and later raised prices while still becoming profitable public companies. He expects a reckoning once AI companies go public and face scrutiny from retail investors, which should reduce the subsidizing practices seen today, though he doubts prices would rise as dramatically as from twenty dollars to twenty thousand, since there is a practical limit to what people will pay.
AI's value has already proven sticky 1:21:01
The evidence so far shows AI tools are useful enough to a large number of people that they are now indispensable, meaning businesses will keep using them even as costs shift.
Companies must find where to optimize 1:21:01
The open question for companies is which part of the process, compute, data, or something else, can be optimized so both the business and its customers benefit even if prices rise somewhat.
Uber as a financial example 1:21:31
Uber is now in a stronger financial position publicly than it was as a subsidized private company, illustrating how a business model can mature past heavy subsidies.
Model providers lack true stickiness 1:21:31
He is skeptical of AI model providers themselves, since users can switch quickly between tools like Claude Code and Codex, so the real stickiness lies in use cases, not the underlying models.
Providers may buy vertical startups 1:22:00
To counter this lack of stickiness, providers are embedding into specific industries and building workflows, and he predicts, as a hot take, that they may end up acquiring fully verticalized companies like Harvey.
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