From Algorithms to Autonomy - Bhanuprakash
Bhanuprakash
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From Algorithms to Autonomy<br>How AI is rewriting the Architecture of Software and Mobility<br>Bhanuprakash<br>Jul 19, 2026
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Artificial intelligence is not simply a new tool being added to the developer’s toolkit. It is rewriting the foundational contract between humans and machines.<br>Tl;dr: The evolution of problem-solving using computers. The tectonic shift happening around us that is resetting the tech industry. The breakthrough of AI: how it all started, the limitations of AI, and what’s coming up next.<br>Thanks for reading! Subscribe for free to receive new posts and support my work.
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I came up with this story line while I was trying to connect all the dots on the recent developments in the field of AI: where it all started and where it is now and what’s next? I’d like to thank Mr. Andrej Karpathy, Ms. Fei-Fei Li, and Mr. Benedict Evans for their perspectives and deep insights, which gave me a lens to see AI in a different way.<br>Artificial intelligence is not simply a new tool being added to the developer’s toolkit. It is rewriting the foundational contract between humans and machines. Recently I was listening to Mr. Karpathys talk “SW in the era of AI” where he spoke about Software 1.0, 2.0, and 3.0. It was quite insightful. In the first era, humans solved their problems using computers by programming the logic themselves. In the second era, the computer learned the logic on its own from data and interfaces fed by humans. And the current era is defined by a natural language interface, where humans converse with the machine, and the machine understands the intent, handles ambiguity, and produces solutions with fluency.<br>Software 1.0, 2.0, 3.0: Abstraction levels<br>Software 1.0 is the era where most engineers were trained to define explicit rules, write deterministic logic in various languages like assembly, C++, Python etc. telling machines exactly what to do. The developer has handcrafted the logic to take care of all possible “if-then-else.” For the problems we planned to solve, this is powerful, predictable, and is inherently limited by what a human can specify.<br>Software 2.0 era shifted the locus of logic from human-written instructions to learned weights & biases. As the complexity of problems to be solved increased, hand-crafted logic no longer worked effectively. For example, to achieve highly accurate traffic signs, traffic light, or pedestrian predictions for ADAS and Autonomous driving, mere 80-90% accuracies are not enough. Accuracy must be > 99%. In this era, developers merely curated the data and designed loss functions for the network to find the logic on its own and solve problems with better accuracy. One side effect is that the system became more probabilistic and not deterministic. “Mind = blown” moment. This is what I call Cambrian explosion of AI. This is when “AlexNet” from Alex, Ilya & Hinton has cracked “ImageNet challenge” using labeled images, hand crafted loss functions and using NVidia GPUs. Many new network deep learning architectures exploded after this defining moment. The infamous “AI winters” has never come after that.<br>Software 3.0 era is where we stand today. The interface is natural language. The source of logic is emergent comprehension i.e., it is not pre specified, not trained on a fixed tasks , but generalized across the domains. The developer’s role here changes to architecting the intent and giving precise context. The system here is more generative, not merely classifying or predicting, but synthesizing. While generative way which we call LLM (Large language Models) brings in meta learning and reasoning by learning the patterns across domains. But one draw back of this approach is hallucinations.
─────────────────────────────────────<br>“LLMs are kind of like these fallible people spirits that we have to learn to work with.” - Andrej Karpathy<br>─────────────────────────────────────<br>LLMs as General-Purpose Technology(GPT)<br>As Benedict Evans puts it, Large language models are a general-purpose technology (GPT) i.e.., a category that includes electricity, the internet, and GPS. It is characterized by broad applicability, scalability, and the capacity to spawn entirely new industries at the application layer.
If we zoom out and see technological evolution like an archeologist, we see that every platform shifts like an S-curve. Every S-curve displaces the old one and a new foundational layer emerges. The companies that recognized it early and built on top of it capture the value of the next decade for example Microsoft won the PC era., Google won the web era, Apple, Samsung, Google won smartphone era and now in GenAI era we see stratospheric valuations for the power houses like Meta, Anthropic, NVidia, Google, OpenAI etc.…and is popularly known as MANGO 😊 .<br>As LLMs commoditize and become infrastructure, the differentiated value will move to the application and experience layers. The products...