OpenAI's o3 launch yesterday made me question my identity as a developer. Seven months ago, I predicted (https://near.tl/developer-forever/forum/announcement/chess-as-a-leading-indicator-of-artificial-superin.anc-8a1a5432-9e97-4e21-81cc-be7d9dd7e8ab.html) that AI would soon add deterministic behavior to its probabilistic foundation. Still, I was shocked to see it happen in the same year. Although OpenAI is tight-lipped about its implementation, they seem to have achieved this through program synthesis — the ability to generate algorithms on the fly just like how developers write code to solve problems. In this post, I explain how similar o3 thinking is to the way we think as developers and explore our relevance in this new AI era. How Users Create Logic Users think with data. Imagine you are a cashier at a grocery store. You learn how to calculate the amount by watching the owner do it for a couple of customers. Based on that, when a customer buys 10 carrots, you would find its price as $2 and multiply $2×10 and tell them that they have to pay $20. This is why users use spreadsheets for repetitive tasks. It provides an intuitive way for users to work with data while writing formulas that can be applied to consecutive rows and see the results immediately: Developers, on the other hand, think with algebra (i.e., as metadata). They declare variables—such as Price and Quantity—multiply them, and assign the result to another variable, Amount. They have the ability to express this logic in an IDE without seeing any data. Only at runtime do they get to apply data and check if their logic is working correctly. This ability to abstract logic is what separates developers from users. This is what makes them build billing software that calculates order amounts millions of times for thousands of customers. How Machine Learning Creates Logic Machine learning works like users. Based on the examples, it finds the pattern using linear regression. It would learn that Amount = Price × Quantity based on that pattern. It’s like a student memorizing multiplication tables without understanding why multiplication works. .png)Machine learning generates logic using training data. Once the model is trained, it applies this learned logic to new inputs like a spreadsheet formula that is applied to consecutive rows. How o3 Creates Logic on the Fly With o3, AI no longer thinks like a user. It thinks like a developer. Just like a developer's brain can think about a problem and come up with a solution using code, o3 generates a program (i.e., metadata) on the fly to solve the problem. Once the program is created, it is executed like developer-written code, producing consistent outputs for the same inputs. The first part is called program synthesis, and the second part is called program execution. .png)Program synthesis is like hearing a problem and coming up with a completely new spreadsheet and a bunch of formulas to solve that problem. It doesn’t need data to come up with formulas anymore. It can just understand the requirements and create a new algorithm that was not invented before. Hands-On vs. Hands-Off Approach The arrival of reasoning models such as o3 changes how developers will write code. Most of us will rely on AI-generated code. However, the question is whether we will review that code and take responsibility for it. This will lead to two distinct approaches to building software: 1. Hands-On Approach Developers will use tools like GitHub Copilot, powered by o3, to generate code. However, they will actively review, refine, and take ownership of the AI-generated output. We are already seeing examples of this with greenfield applications, but o3 is poised to make it the standard across all types of software development. 2. Hands-Off Approach Users will utilize tools like ChatGPT, which leverage o3, to address their business problems. Their initial prompt will articulate the business requirements in plain English. After refining the requirements, users will interact with the chat interface for subsequent operations. Here, program synthesis occurs during the initial prompt, while program execution unfolds within the conversations. This approach can be extended to apps in the GPT Store and elsewhere using API. Summary With the launch of o3, it’s clear that users will be able to generate working code and launch applications without understanding what the code does. Some users will become successful without understanding how their app works. Does this mean we can trust AI like faith in religion? If you extend this analogy to cancer research, if AI comes up with a solution that researchers cannot understand, should we use it to save lives or avoid it because we don’t understand it yet? These ideological questions will shape the future. However, I believe in the hands-on approach. Developers must understand and take responsibility for the logic AI generates if they are to launch it as an application for others. If we forfeit our understanding, the reasoning in AI would bring an end to reasoning in humans. Our ability to understand the code that AI generates will decide if we are the puppeteers or puppets of AI.

Computers follow exact rules. They understand logic like "Delhi" ≠ "Chennai" or "Population of Delhi" > 30 million. However, they struggle to understand the concept of similarity (≈). For example, to us, "Delhi" ≈ "Noida" because both are part of the National Capital Region, but computers can't grasp that naturally. To make computers as intelligent as humans, we first need to teach them the concept of similarity (≈). How do we do that? It's all about distance In the Tamil movie Puthumaipithan, Vadivelu plays the role of a politician who explains to journalists why places near Delhi have more political influence than Chennai. His answer? "It’s distance." He goes on to propose moving Chennai closer to Delhi to gain more influence. While this idea may seem absurd at first, it's actually similar to how AI works. AI uses 'distance' to understand how close or similar different entities are. In this post, I'll show how AI applies this concept to determine similarity between different entities like cities. First, we'll look at how Noida is closer to Delhi, then see how Chennai can be considered closer to Delhi depending on the context. For a politician, Noida ≈ Delhi Let's take a politician like Vadivelu as our first example. For a politician, the closer the two cities are, the more similar they are in terms of language, culture, and voting patterns. If you provide a computer with the latitude and longitude of Delhi, Noida, and Chennai, it can calculate the distances between the cities. Based on distance, it can conclude that "Noida" is closer to "Delhi" than "Chennai." Computers excel at calculating exact distances, which helps them determine which cities are similar in this specific context. Computers use machine learning (ML) algorithms to calculate the distance between entities like cities to determine their similarity. We train these algorithms to focus only on the characteristics relevant to a specific use case—such as latitude and longitude for physical distance. For an entrepreneur, Chennai ≈ Delhi Now, consider an entrepreneur looking to expand their business. They want to find a city similar to "Delhi." In this scenario, you would provide different characteristics like "tier" (the size of the city) and "population." This data would show that "Chennai" is more similar to "Delhi" than "Noida" because both Chennai and Delhi are tier-1 cities with similar populations. In this case, the 'distance' doesn't refer to physical proximity but rather to how similar they are in characteristics like population and city size. This ability to find similarities using machine learning helps computers perform pattern matching. This is similar to how humans do pattern recognition and represents a foundational step towards achieving human-like intelligence. Narrow AI The characteristics such as latitude/longitude provided to an ML algorithm depend on the use case—whether it’s for a politician to compete in an election or an entrepreneur to expand their company. In both cases, calculating similarity requires a lot of data, such as knowing the details of all the cities. If you don't have enough data, the algorithm won't work correctly. This is known as the "cold start problem" in machine learning. This was what we tried to solve at our startup, Guesswork (https://techcrunch.com/2014/08/08/guesswork-wants-to-focus-machine-learning-on-customer-information/), back in 2013. By focusing on one specific use case—customer information—we could pre-train our ML model and solve the cold start problem. Hence, these models were called narrow AI. General Purpose AI The current AI era has given rise to a new breed of models called Large Language Models (LLMs). These models solve the cold start problem differently. They are trained on vast amounts of information available on the internet. They understand thousands of characteristics about each word, such as "Delhi" or "Chennai." Instead of using simple two-dimensional charts, LLMs represent words like "Delhi" and "Chennai" in a multidimensional space, where each dimension captures a specific characteristic of the cities. They can then calculate the distance between words in that space to understand similarities—even without specific training data. But there is still a challenge: How do you make sure the model focuses on only certain characteristics? For example, if you’re a politician trying to find a similar city for an election, the model should focus on latitude and longitude to calculate the distance. But if you’re an entrepreneur, it should focus on tier and population. How do you do that? 1. Prompting: With prompting, you instruct the model in plain English to focus on the right characteristics. For example, if a politician asks the model to find a city similar to Delhi for an election, the model might consider that latitude and longitude are important because being close could influence political support. Therefore, it would suggest Noida over Chennai. 2. Fine-Tuning: In fine-tuning, you train the model with examples. This might look similar to ML models from the past, but it is different. Traditional ML models start with no knowledge. But, fine-tuned models already have knowledge from the internet and can focus on characteristics mentioned in the examples. In our case, it knows about the language and culture of the cities, but it will pay more attention to latitude and longitude because we emphasized them in the examples. In short, traditional ML models need a lot of examples, and prompting can be unpredictable without examples. Fine-tuning balances both and gives predictable results with fewer examples. But LLMs can do more than just find similar cities—they can also explain why. For example, they can explain why Chennai might be better for starting a business than Noida, all in plain English. This works a bit like Google Translate. In Google Translate, you provide a fact about Delhi and ask it to translate it into another language. With LLMs, you provide a fact about Delhi and ask it to transform that fact for Chennai. This kind of transformation is called generative AI, which is used in tools like ChatGPT. Summary Computers follow exact rules, so we call them deterministic. But when computers understand ≈, we call them probabilistic. This is because they calculate the distance between two entities and conclude that they are “probably” similar. As long as a computer can measure how "close" two entities are, it can determine if they are similar, understand their connection, and provide meaningful answers. This simple idea of similarity (≈) was a major stepping stone for computers, enabling them to make decisions that are not just logical but also intuitive—much like humans do. AI has taken that concept and turned it into something magical—whether it's answering our questions or helping us make decisions—reshaping the world as we know it.

When you create a leave application form using Google Forms, you notice how effortless it is to add fields, one by one. However, with an AI-enabled form builder like Formfacade (https://formfacade.com), the form is generated automatically as soon as you enter the title “leave application form.” This marks a fundamental shift in how products are being built. Instead of simplifying form creation on a blank canvas, products are now pre-creating forms, giving users a tailored starting point. Prompting will replace templates in B2B products In the past, products like Google Forms addressed form creation by offering a variety of templates. Due to the wide range of user needs, many of these templates were crowdsourced from users, leading to inconsistent quality and difficulty in finding the right one. In the future, products will use a handful of base templates as a “seed” for AI to generate customized templates for each user. At Formfacade, we use Promptrepo (https://promptrepo.com/finetune/) to create AI model from our pre-existing form templates. This model is used to pre-create form automatically when the user enters a form title. This shift fundamentally changes the onboarding experience. The focus will no longer be on teaching users to add fields to a blank canvas. Instead, products will make it easy to edit pre-created forms (e.g., "Add final polish" in ChatGPT 4o with canvas). In both scenarios—form title during creation and form fields during editing—the form itself acts as the prompt, rather than relying on user-entered prompt as seen in many AI-enabled apps today. This change applies not only to form builders but also to other platforms, from Canva to WordPress. Eventually, these products will be redesigned—or replaced—by new solutions that offer pre-filled canvas ready for editing, rather than blank slates requiring manual setup. Agents will replace integration in consumer products While prompting helps prefill information within a single app, AI agents will work across multiple apps to prefill information. Consider planning a trip to attend a conference. Typically, you would need to interact with separate apps for booking the conference, airline tickets, and hotel accommodations. In the past, integrations were built between each of these applications to avoid reentering the same information. In an AI-enabled future, agents will work across these platforms based on a single input, dynamically building the necessary integration between user-facing apps on the fly. AI agents will handle multiple applications behind the scenes, allowing you to interact through one interface. This seamless, cross-platform experience could unfold over multiple days, with a single app serving as the central point of your journey rather than requiring you to juggle multiple apps. The conference booking app, for instance, could become the origin of your trip, coordinating all necessary bookings. Additionally, this app could collect affiliate fees from other applications involved in the process. Summary Ten years ago, I explained how software was augmented with hardware in the SaaS era, leading to new wave of products like Salesforce. And, I predicted (https://www.youtube.com/watch?v=GZmK-3sZ9hc) that the next wave would augment intelligence with software using AI. Specifically, AI would automate tasks—such as prefilled leads in CRM systems—that required human intelligence. Today, with the rise of generative AI, we are moving in that direction, although we may have been influenced too much by the success of ChatGPT. Instead of focusing on prefilling information, we are adding chat interfaces to all products, even when they may be unnecessary. I believe that once we refocus on solving core use cases rather than simply emulating ChatGPT's success, we will see this shift: Prompting will redefine B2B SaaS onboarding by replacing blank canvas with tailored form or content based on minimal user input like form title. Meanwhile, AI agents will integrate multiple apps on the fly, enabling users to accomplish tasks across products from a single interface. In essence, prompting will redefine B2B products, while agents will redefine consumer products.

In the 2018 World Chess Championship, Magnus Carlsen and Fabiano Caruana's game ended in a draw despite Caruana's material advantage. Stockfish, the powerful chess engine, revealed a stunning missed opportunity: a forced checkmate in 35 moves. Stockfish's analysis suggested unconventional moves, like trapping the Knight on the edge of the board—moves no human would consider. This showcased Stockfish's ability to see far beyond human intuition, highlighting the power of AI in uncovering deep, hidden strategies in chess, even outwitting the world's best players. Stockfish is a narrow AI with a rating estimated to be over 3500. This is significantly higher than Magnus Carlsen’s rating of 2882. Within its specific domain, Stockfish demonstrates superintelligent behavior. This trend is likely to happen in other fields, such as programming and customer support, where AI could surpass the world's best experts. If we extrapolate this trend, we can predict three outcomes: 1\. AI will enhance\, not replace human expertise To improve their game, players use Stockfish to analyze their chess moves. This has contributed to the growth of top chess players, since its launch in 2008: | Decade | Grandmasters | International Masters | FIDE Masters | Candidate Masters | | ------ | ------------ | --------------------- | ------------ | ----------------- | | 1970s | 82 | 200 | 500 | 500 | | 1980s | 300 | 400 | 1000 | 1000 | | 1990s | 600 | 800 | 2000 | 2000 | | 2000s | 1000 | 1500 | 3500 | 3000 | | 2010s | 1500 | 2000 | 5000 | 4000 | | 2020s | 1722 | 2400 | 6000 | 5000 | Similarly, top performers in different fields will increase, not decrease with AI. AI will teach us to be superhumans. On platforms like Chess.com, you can play for free but pay to improve your game using Stockfish. Similarly, future apps might be free, but you might pay for AI-driven learning. Edtech might not be a separate vertical; it could be the premium version in all verticals. 2\. AI automation might be considered cheating Using Stockfish in competition is considered cheating. In the 2022 Sinquefield Cup, World Chess Champion Magnus Carlsen resigned, suspecting his opponent, Hans Niemann, of using Stockfish. This caused a stir in the chess world, raising concerns about the impact of technology. The same might happen with AI assistants. Using AI to learn might be the norm, but delegating your work to AI could be frowned upon like cheating. AI would be a teacher, not a proxy who writes exam for you. If you are rebranding your product as AI, you might want to hold your horses. Investors might like it, but customers may not. Note: For people who think customers care only about the output, this didn't happen in chess. We don't watch Stockfish vs. Magnus Carlsen games. Magnus Carlsen is the star we celebrate, not Stockfish. 3\. Super intelligence could be deterministic In the endgame, Stockfish acts like a deterministic algorithm, using precomputed tablebases to make perfect moves with absolute certainty. This capability outstrips even Magnus Carlsen, as Stockfish can foresee and execute a flawless 35-move checkmate sequence that no human could match. This combination of a probabilistic middle game and a deterministic endgame makes it unbeatable. AI will hit a plateau once LLMs have read all available text and reach the IQ of top experts in various fields. However, just as Stockfish has surpassed Carlsen's rating of 2882 through the use of deterministic algorithms, artificial superintelligence could achieve unprecedented levels of capability by combining the adaptability of probabilistic LLMs with the precision of deterministic systems. Given that deterministic systems have a 50-year head start over probabilistic LLMs, this hybrid approach could excel at both nuanced tasks and well-defined problems, ultimately surpassing human capabilities across many domains. Conclusion Ten years ago, I predicted the impact of AI (https://www.youtube.com/watch?v=GZmK-3sZ9hc) on CRM and other business sectors. Most of these predictions have come true, except for the widespread combination of rule engines with machine learning. If chess is a leading indicator, I still believe that superintelligence will combine deterministic algorithms and probabilistic LLMs, excelling at both structured and nuanced tasks. Its adoption in chess not only showcases technological prowess but also provides a blueprint for thriving with superintelligence instead of being scared about it.

Both first-time and second-time founders often don’t know what they’re doing. However, first-time founders believe they must appear knowledgeable, so they pretend to have all the answers in front of their employees. This act contributes to their feeling of being impostors—the more they pretend, the more they feel like frauds. Second-time founders understand that all founders are in the same situation of not knowing everything. This realization leads them not to feel obliged to act as if they have all the answers. They are more open about their uncertainties with their employees and investors, which is why they come across as more authentic. The advantage of being in accelerators like YC is that first-time founders can realize they don’t need to know everything. They see that their heroes—alums, investors, mentors—don’t have all the answers either. They offer suggestions, not definitive answers, which instead of being a disappointment, shows first-time founders that everyone is in the same boat. It’s better to be authentic and seek answers from their own experiences. This is a major benefit of being in a community such as YC or in a city like San Francisco.