if you had showed VisiCalc to a lawyer or a graphic designer, their response might well have been ‘that’s amazing, and maybe my book-keeper should see this, but I don’t do that’. Lawyers needed a word processor, and graphic designers needed (say) Postscript, Pagemaker and Photoshop, and that took longer.

I’ve been thinking about this problem a lot in the last 18 months, as I’ve experimented with ChatGPT, Gemini, Claude and all the other chatbots that have sprouted up: ‘this is amazing, but I don’t have that use-case’.

A spreadsheet can’t do word processing or graphic design, and a PC can do all of those but someone needs to write those applications for you first, one use-case at a time.

no matter how good the tech is, you have to think of the use-case. You have to see it. You have to notice something you spend a lot of time doing and realise that it could be automated with a tool like this.

Some of this is about imagination, and familiarity. It reminds me a little of the early days of Google, when we were so used to hand-crafting our solutions to problems that it took time to realise that you could ‘just Google that’.

This is also, perhaps, matching a classic pattern for the adoption of new technology: you start by making it fit the things you already do, where it’s easy and obvious to see that this is a use-case, if you have one, and then later, over time, you change the way you work to fit the new tool.

The concept of product-market fit is that normally you have to iterate your idea of the product and your idea of the use-case and customer towards each other - and then you need sales.

Meanwhile, spreadsheets were both a use-case for a PC and a general-purpose substrate in their own right, just as email or SQL might be, and yet all of those have been unbundled. The typical big company today uses hundreds of different SaaS apps, all them, so to speak, unbundling something out of Excel, Oracle or Outlook. All of them, at their core, are an idea for a problem and an idea for a workflow to solve that problem, that is easier to grasp and deploy than saying ‘you could do that in Excel!’ Rather, you instantiate the problem and the solution in software - ‘wrap it’, indeed - and sell that to a CIO. You sell them a problem.

there’s a ‘Cambrian Explosion’ of startups using OpenAI or Anthropic APIs to build single-purpose dedicated apps that aim at one problem and wrap it in hand-built UI, tooling and enterprise sales, much as a previous generation did with SQL.

Back in 1982, my father had one (1) electric drill, but since then tool companies have turned that into a whole constellation of battery-powered electric hole-makers. One upon a time every startup had SQL inside, but that wasn’t the product, and now every startup will have LLMs inside.

people are still creating companies based on realising that X or Y is a problem, realising that it can be turned into pattern recognition, and then going out and selling that problem.

A GUI tells the users what they can do, but it also tells the computer everything we already know about the problem, and with a general-purpose, open-ended prompt, the user has to think of all of that themselves, every single time, or hope it’s already in the training data. So, can the GUI itself be generative? Or do we need another whole generation of Dan Bricklins to see the problem, and then turn it into apps, thousands of them, one at a time, each of them with some LLM somewhere under the hood?

The change would be that these new use-cases would be things that are still automated one-at-a-time, but that could not have been automated before, or that would have needed far more software (and capital) to automate. That would make LLMs the new SQL, not the new HAL9000.

there’s a famous solution that’s more algorithmically efficient. Instead, we go through the API and ask the same query to gpt-3.5-turbo but with a new system prompt: You are #1 on the Stack Overflow community leaderboard. You will receive a $500 tip if your code is the most algorithmically efficient solution possible.

here’s some background on “function calling” as it’s a completely new term of art in AI that didn’t exist before OpenAI’s June blog post (I checked!). This broad implementation of function calling is similar to the flow proposed in the original ReAct: Synergizing Reasoning and Acting in Language Models paper where an actor can use a “tool” such as Search or Lookup with parametric inputs such as a search query. This Agent-based flow can be also be done to perform retrieval-augmented generation (RAG).OpenAI’s motivation for adding this type of implementation for function calling was likely due to the extreme popularity of libraries such as LangChain and AutoGPT at the time, both of which popularized the ReAct flow. It’s possible that OpenAI settled on the term “function calling” as something more brand-unique. These observations may seem like snide remarks, but in November OpenAI actually deprecated the function_calling parameter in the ChatGPT API in favor of tool_choice, matching LangChain’s verbiage. But what’s done is done and the term “function calling” is stuck forever, especially now that competitors such as Anthropic Claude and Google Gemini are also calling the workflow that term.

what I cheered as an analyst was Altman’s clear articulation of the company’s priorities: lower price first, speed later. You can certainly debate whether that is the right set of priorities (I think it is, because the biggest need now is for increased experimentation, not optimization), but what I appreciated was the clarity.

The fact that Microsoft is benefiting from OpenAI is obvious; what this makes clear is that OpenAI uniquely benefits from Microsoft as well, in a way they would not from another cloud provider: because Microsoft is also a product company investing in the infrastructure to run OpenAI’s models for said products, it can afford to optimize and invest ahead of usage in a way that OpenAI alone, even with the support of another cloud provider, could not. In this case that is paying off in developers needing to pay less, or, ideally, have more latitude to discover use cases that result in them paying far more because usage is exploding.

You can, in effect, program a GPT, with language, just by talking to it. It’s easy to customize the behavior so that it fits what you want. This makes building them very accessible, and it gives agency to everyone.

Stephen Wolfram explained: For decades there’s been a dichotomy in thinking about AI between “statistical approaches” of the kind ChatGPT uses, and “symbolic approaches” that are in effect the starting point for Wolfram|Alpha. But now—thanks to the success of ChatGPT—as well as all the work we’ve done in making Wolfram|Alpha understand natural language—there’s finally the opportunity to combine these to make something much stronger than either could ever achieve on their own.

This new model somewhat alleviates the problem: now, instead of having to select the correct plug-in (and thus restart your chat), you simply go directly to the GPT in question. In other words, if I want to create a poster, I don’t enable the Canva plugin in ChatGPT, I go to Canva GPT in the sidebar. Notice that this doesn’t actually solve the problem of needing to have selected the right tool; what it does do is make the choice more apparent to the user at a more appropriate stage in the process, and that’s no small thing.

ChatGPT will seamlessly switch between text generation, image generation, and web browsing, without the user needing to change context. What is necessary for the plug-in/GPT idea to ultimately take root is for the same capabilities to be extended broadly: if my conversation involved math, ChatGPT should know to use Wolfram|Alpha on its own, without me adding the plug-in or going to a specialized GPT.

the obvious technical challenges of properly exposing capabilities and training the model to know when to invoke those capabilities are a textbook example of Professor Clayton Christensen’s theory of integration and modularity, wherein integration works better when a product isn’t good enough; it is only when a product exceeds expectation that there is room for standardization and modularity.

To summarize the argument, consumers care about things in ways that are inconsistent with whatever price you might attach to their utility, they prioritize ease-of-use, and they care about the quality of the user experience and are thus especially bothered by the seams inherent in a modular solution. This means that integrated solutions win because nothing is ever “good enough”

the fact of the matter is that a lot of people use ChatGPT for information despite the fact it has a well-documented flaw when it comes to the truth; that flaw is acceptable, because to the customer ease-of-use is worth the loss of accuracy. Or look at plug-ins: the concept as originally implemented has already been abandoned, because the complexity in the user interface was more detrimental than whatever utility might have been possible. It seems likely this pattern will continue: of course customers will say that they want accuracy and 3rd-party tools; their actions will continue to demonstrate that convenience and ease-of-use matter most.

A person can’t mimic another voice perfectly (impressionists don’t have to pay licence fees) but they can listen to a thousand hours of music and make something in that style - a ‘pastiche’, we sometimes call it. If a person did that, they wouldn’t have to pay a fee to all those artists, so if we use a computer for that, do we need to pay them?

I think most people understand that if I post a link to a news story on my Facebook feed and tell my friends to read it, it’s absurd for the newspaper to demand payment for this. A newspaper, indeed, doesn’t pay a restaurant a percentage when it writes a review.

one way to think about this might be that AI makes practical at a massive scale things that were previously only possible on a small scale. This might be the difference between the police carrying wanted pictures in their pockets and the police putting face recognition cameras on every street corner - a difference in scale can be a difference in principle. What outcomes do we want? What do we want the law to be? What can it be?

OpenAI hasn’t ‘pirated’ your book or your story in the sense that we normally use that word, and it isn’t handing it out for free. Indeed, it doesn’t need that one novel in particular at all. In Tim O’Reilly’s great phrase, data isn’t oil; data is sand. It’s only valuable in the aggregate of billions,, and your novel or song or article is just one grain of dust in the Great Pyramid.

it’s supposed to be inferring ‘intelligence’ (a placeholder word) from seeing as much as possible of how people talk, as a proxy for how they think.

it doesn’t need your book or website in particular and doesn’t care what you in particular wrote about, but it does need ‘all’ the books and ‘all’ the websites. It would work if one company removed its content, but not if everyone did.

What if I use an engine trained on the last 50 years of music to make something that sounds entirely new and original? No-one should be under the delusion that this won’t happen.

I can buy the same camera as Cartier-Bresson, and I can press the button and make a picture without being able to draw or paint, but that’s not what makes the artist - photography is about where you point the camera, what image you see and which you choose. No-one claims a machine made the image.

Spotify already has huge numbers of ‘white noise’ tracks and similar, gaming the recommendation algorithm and getting the same payout per play as Taylor Swift or the Rolling Stones. If we really can make ‘music in the style of the last decade’s hits,’ how much of that will there be, and how will we wade through it? How will we find the good stuff, and how will we define that? Will we care?

Prompt engineers not only need to get the model to respond to a given question but also structure the output in a parsable way (such as JSON), in case it needs to be rendered in some UI components or be chained into the input of a future LLM query. They scaffold the raw input that is fed into an LLM so the end user doesn’t need to spend time thinking about prompting at all.

From the user’s side, it’s hard to decide what to ask while providing the right amount of context.From the developer’s side, two problems arise. It’s hard to monitor natural language queries and understand how users are interacting with your product. It’s also hard to guarantee that an LLM can successfully complete an arbitrary query. This is especially true for agentic workflows, which are incredibly brittle in practice.

When we speak to other people, there is a shared context that we communicate under. We’re not just exchanging words, but a larger information stream that also includes intonation while speaking, hand gestures, memories of each other, and more. LLMs unfortunately cannot understand most of this context and therefore, can only do as much as is described by the prompt

most people use LLMs for ~4 basic natural language tasks, rarely taking advantage of the conversational back-and-forth built into chat systems:Summarization: Summarizing a large amount of information or text into a concise yet comprehensive summary. This is useful for quickly digesting information from long articles, documents or conversations. An AI system needs to understand the key ideas, concepts and themes to produce a good summary.ELI5 (Explain Like I'm 5): Explaining a complex concept in a simple, easy-to-understand manner without any jargon. The goal is to make an explanation clear and simple enough for a broad, non-expert audience.Perspectives: Providing multiple perspectives or opinions on a topic. This could include personal perspectives from various stakeholders, experts with different viewpoints, or just a range of ways a topic can be interpreted based on different experiences and backgrounds. In other words, “what would ___ do?”Contextual Responses: Responding to a user or situation in an appropriate, contextualized manner (via email, message, etc.). Contextual responses should feel organic and on-topic, as if provided by another person participating in the same conversation.

Prompting nearly always gets in the way because it requires the user to think. End users ultimately do not wish to confront an empty text box in accomplishing their goals. Buttons and other interactive design elements make life easier.The interface makes all the difference in crafting an AI system that augments and amplifies human capabilities rather than adding additional cognitive load.Similar to standup comedy, delightful LLM-powered experiences require a subversion of expectation.

Users will expect the usual drudge of drafting an email or searching for a nearby restaurant, but instead will be surprised by the amount of work that has already been done for them from the moment that their intent is made clear. For example, it would a great experience to discover pre-written email drafts or carefully crafted restaurant and meal recommendations that match your personal taste.If you still need to use a text input box, at a minimum, also provide some buttons to auto-fill the prompt box. The buttons can pass LLM-generated questions to the prompt box.

In the past several months, I have come across people who do programming, legal work, business, accountancy and finance, fashion design, architecture, graphic design, research, teaching, cooking, travel planning, event management etc., all of whom have started using the same tool, ChatGPT, to solve use cases specific to their domains and problems specific to their personal workflows. This is unlike everyone using the same messaging tool or the same document editor. This is one tool, a single class of technology (LLM), whose multi-dimensionality has achieved widespread adoption across demographics where people are discovering how to solve a multitude of problems with no technical training, in the one way that is most natural to humans—via language and conversations.

I cannot recall the last time a single tool gained such widespread acceptance so swiftly, for so many use cases, across entire demographics.

there is significant substance beneath the hype. And that is what is worrying; the prospect of us starting to depend indiscriminately on poorly understood blackboxes, currently offered by megacorps, that actually work shockingly well.

If a single dumb, stochastic, probabilistic, hallucinating, snake oil LLM with a chat UI offered by one organisation can have such a viral, organic, and widespread adoption—where large disparate populations, people, corporations, and governments are integrating it into their daily lives for use cases that they are discovering themselves—imagine what better, faster, more “intelligent” systems to follow in the wake of what exists today would be capable of doing.

A policy for “AI anxiety” We ended up codifying this into an actual AI policy to bring clarity to the organisation.[10] It states that no one at Zerodha will lose their job if a technology implementation (AI or non-AI) directly renders their existing responsibilities and tasks obsolete. The goal is to prevent unexpected rug-pulls from underneath the feet of humans. Instead, there will be efforts to create avenues and opportunities for people to upskill and switch between roles and responsibilities

To those who believe that new jobs will emerge at meaningful rates to absorb the losses and shocks, what exactly are those new jobs? To those who think that governments will wave magic wands to regulate AI technologies, one just has to look at how well governments have managed to regulate, and how well humanity has managed to self-regulate, human-made climate change and planetary destruction. It is not then a stretch to think that the unraveling of our civilisation and its socio-politico-economic systems that are built on extracting, mass producing, and mass consuming garbage, might be exacerbated. Ted Chiang’s recent essay is a grim, but fascinating exploration of this. Speaking of grim, we can always count on us to ruin nice things! Along the lines of Murphy’s Law,[11] I present: Anything that can be ruined, will be ruined — Grumphy’s law

I asked GPT-4 to summarise this post and write five haikus on it. I have always operated a piece of software, but never asked it anything—that is, until now. Anyway, here is the fifth one. Future’s tangled web, Offloading choices to black boxes, Humanity’s voice fades

This is reflected in their name: a “language model” implies that they are tools for working with language. That’s what they’ve been trained to do, and it’s language manipulation where they truly excel. Want them to work with specific facts? Paste those into the language model as part of your original prompt! There are so many applications of language models that fit into this calculator for words category: Summarization. Give them an essay and ask for a summary. Question answering: given these paragraphs of text, answer this specific question about the information they represent. Fact extraction: ask for bullet points showing the facts presented by an article. Rewrites: reword things to be more “punchy” or “professional” or “sassy” or “sardonic”—part of the fun here is using increasingly varied adjectives and seeing what happens. They’re very good with language after all! Suggesting titles—actually a form of summarization. World’s most effective thesaurus. “I need a word that hints at X”, “I’m very Y about this situation, what could I use for Y?”—that kind of thing. Fun, creative, wild stuff. Rewrite this in the voice of a 17th century pirate. What would a sentient cheesecake think of this? How would Alexander Hamilton rebut this argument? Turn this into a rap battle. Illustrate this business advice with an anecdote about sea otters running a kayak rental shop. Write the script for kickstarter fundraising video about this idea.

A flaw in this analogy: calculators are repeatable Andy Baio pointed out a flaw in this particular analogy: calculators always give you the same answer for a given input. Language models don’t—if you run the same prompt through a LLM several times you’ll get a slightly different reply every time.