Remember when you emailed your manager for sick leave, and they approved it with a kind "take care"? Then one day, instead of a warm response, you got a link to a leave request form from HR. Suddenly, what used to be a human interaction turned into a cold, bureaucratic process. Why do companies do this? Why turn simple conversations into forms? Users need chain of thought more than AI While AI companies are building "chain of thought" into their language models, we are the ones who need it the most. Whether it's filling out a leave request form (painful) or a Y Combinator application (thoughtful), the questions in the form make us think. If we sent them through email, we wouldn't consider all the necessary details. We might say, "I'm feeling unwell, taking the next two days off," and that feels natural to us. But for the HR department, there's much more they need to know—like how many leave days you have left, whether it affects payroll, and if your manager has approved it. Companies turn conversations into structured forms because it streamlines information gathering. Instead of HR needing to follow up for missing details, a form can capture all this information upfront, reducing delays and confusion. As we automate these processes with AI, this balance must be maintained. AI should not only convert our conversation into a leave request; it should also prompt us for missing information, similar to required fields and validations in a form. Chain of thought in business logic This need for structured data doesn't stop at how we provide information; it also influences how we process it. For instance, if someone has exhausted their leave balance, applying simple, deterministic rules to structured data yields better results than relying on AI models that use probabilistic logic. Deterministic logic rules are more reliable than AI that relies on probabilistic reasoning. This is especially important in regulated industries like insurance, where the step-by-step logic leading to a decision is not only preferable but mandatory. Extending chain of thought to UI But how do you communicate this to the user? Should AI respond like a screen reader, dictating these details step by step? Or should it show a pre-filled form, visually presenting the information all at once? The answer is clear — user interface is far more efficient than a chat response because it offers clarity and speed of comprehension. Think about ordering a cab on Uber. Instead of receiving chat updates about the cars around you, a UI that shows the cars on a map is far more efficient and clear. This is where generative AI often falls short—chatting endlessly when a well-designed interface would be more effective. Semantic AI At Promptrepo (https://promptrepo.com/semantic/), we call this approach 'Semantic AI' — it's about understanding the meaning behind our words to extract structured data from conversations and presenting it in a modality that's easier for you. Semantic AI will identify key details like dates and reasons when you send an email about taking leave. It will then automatically populate the necessary fields in a leave request form for you to verify and submit. This way, AI assists in maintaining our natural communication style while ensuring all required information is captured. Once the generative AI hype settles, we believe the true power of AI will lie in extracting structured data rather than generating a "word salad." As AI continues to evolve, we'll see a shift in its architecture. While generative AI relies on probabilistic logic and chat-based interaction, Semantic AI will focus on structured data and deterministic logic. Structured data isn't just a business need for HR to generate reports; it also helps us understand and feel informed about the consequences of our requests (such as unpaid leave, price change), making interactions with AI meaningful and effective.

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.

Code Generation AI is all the rage these days. But is generating code for programming languages like JavaScript and Python the right path to take? I think not. I think we should be generating code for declarative languages like Excel or SQL. What's the difference, you ask? In declarative languages, you express what your intention is. For example, in Excel, you can use SUM() to add all the line items and calculate the order amount. If the quantity of a line item changes, it will automatically recalculate the line item amount and then invoke SUM() to recalculate the order amount. But in imperative languages like JavaScript or Python, you instruct the computer on how to calculate the order amount. You would implement a function to add the line items as the order amount. Anytime the quantity of a line item changes, it is your job to call the function and recalculate the order amount. Why is this important? If you are asking AI to generate code for your requirements, you are essentially expressing your intent. So, expressing it in a declarative language seems natural. This will help the person who gave the requirement to understand the code generated by the AI. On the other hand, generating code for a programming language seems like the worst form of leaky abstraction (https://www.joelonsoftware.com/2002/11/11/the-law-of-leaky-abstractions/). "Leaky abstraction" describes a scenario where attempts to simplify a system end up requiring users to understand its underlying complexities to troubleshoot it. Code generation can automate the creation process. But, the resulting code can be a puzzle even to a skilled developer who is debugging it. The person who gave the requirement will most likely not understand any of it. So, why do AI companies generate code like this? I guess it comes down to the availability of training data for AI. There are a lot of open-source projects in JavaScript or Python, so it is easy to train AI with it. But open-source projects in Excel are almost non-existent. So, the unavailability of training data might be the primary reason behind the direction these code generation AI companies are taking. At Neartail, we are taking a middle road. We have created a declarative language using JavaScript syntax so that we could train the AI as well as make it understandable to business owners. Will other AI companies realize the perils of leaky abstraction and change its course? Only time will tell.