"As with the last few paradigm shifts, something as simple as a 286 PC with a 4mhz CPU and 1mb of memory or just 56 kb/s connectivity can be sufficient to revolutionize the world."

"There is much more potential to be explored if we think beyond the confines of familiar human-language products and interfaces"

"1. Containing the problem space: thinking in domains"

"There is a lot of domain-specific knowledge baked into the popular foundation models, and this is where most of the meat is in the near term."

"This can be further refined with domain-specific fine tuning. I’ve been referring to it as ADI (Artificial Domain Intelligence) and this is the most exciting and tangible product, experience and business outcome for AI that’s broadly available today."

"Products built with AI need to be clear in the domain-space they’re looking to tackle. This can be broad, i.e comparable depth of knowledge across domains enabling consistent cross-domain experience or narrow with significant depth in a specific domain."

"Until we have bonafide AGI, ADI can be used to create new products and services that were previously prohibitive due to human-costs, scalability or technical constraints."

"2. Construct the UX: breaking the skeuomorphic barrier"

"Almost every attempt to bolt AI on top of existing products and paradigms are very likely to miss the boat."

"We’ve seen products pipe LLM outputs directly into text editors, tables and other existing surfaces to do text generation, summarization, classification etc. While this is useful, but there are no common prior-use cases or behaviors that involve using these surfaces that way."

"This could also suffer from the inefficient approach of incorporating AI at the task rather than problem level."

"Redefining the problem context and rethinking the solutions with the new paradigms enabled by AI leads to some interesting consequences."

"First, the interface might not look like an editor, table or a page."

"Second, it forces a decision in the workflow design of a hand-off point from human to machine."

"Third and possibly most impactful effect, it introduces an additional consideration for if there is a need for human input in the workflow at all."

"When a solution is redesigned to be AI-native, quite often it ends up drastically reducing the complexity of the interfaces with most of the magic happening behind the scenes."

"3. Compose the product stack: simulating proto-AGI"

"In production-grade AI products, this needs to happen behind the scenes and be fully done by machines."

"To enable this, you need some structural scaffolding, workflow handling and data management techniques to ensure the AI pipelines and experiences function reliably at scale."

"One of the things that makes using models in production difficult is their inherent probabilistic nature."

"The crux of most product challenges involved comes down to this:"

"To solve for this, you essentially need to simulate proto-AGI for the use-case and domain you’re building in."

"One approach to getting as close as possible is by scaffolding and engineering around this in the application realm."

"Data structures: LLMs are basically completion machines that figure out what token to output next. It only works on the next token at a time and the next token is determined, at the root of it, by a differentiable function invoked at scale."

"A coherent set of tokens make up words and, eventually, a sentence."

"This means we can now output any data structure that follows a discernible ruleset."

"This turns language model APIs into essentially a synthesis adapter for a vast majority of use cases."

"Offloading complex systems and engineering workflows to the model layers. This abstraction is fundamental to AI being used to power more than chat and language interfaces, and is at the root of its accelerant trait."

"Decomposition & Chaining: Getting the desired result in a single prompt/response is a great way to proof-of-concept."

"If you decompose the problem into several stages and build a pipeline/chain that is step-optimised to produce the output you need - you build a much more resilient and scalable system."

"Machine-interface Models: this an another fun one we use across the board at Qatalog."

"MiMs are designed to be interfacing directly with machines, with no human in the loop."

"These “adaptors” are designed to produce one probabilistic output in a larger chain of outputs that are used to compose the end result."

"Federation & Multiplexing: another approach we concocted at Qatalog is building a recursive multiplexer that knows which set of models, prompts, helper functions and marshallers to use depending on the problem."

"4. Correcting errors: guarding for technical limitations"

"LLMs do not conceptually understand its own outputs."

"LLMs are typically trained on content crowd-sourced through online platforms and/or have error-prone collection protocols. Popular language models are also designed to encourage informativeness over faithfulness."

"Use-cases like search, task assistants, healthcare and other critical services need significant safeguarding around collection protocols, faithfulness and accuracy before production use."

"We need more structural tooling, methodologies and processes to ensure the models are functioning within expected parameters and are not introducing any risk, factual errors or bias in the output."

"Additionally, incorporating programmatic reinforcement features at the application layer to report, identify and guard against negative outputs in the future."

"5. Capture value: building AI businesses"

"Fullstack AI companies, such as Qatalog and inevitably others, are being built from the ground up with a focus on AI-native products as well as AI-powered operations."

"Attempts at driving value with AI seem to mostly create new use-cases and behaviours, while most of the immediate value is in retrospectively applying the technology to existing problems."

"Applying this requires careful evaluation of the existing processes and identifying insertion points for the technology and restructuring the process for the desired outcome."

"To build a sustainable business with AI, there are three possible moats to optimize for: first, unique product infrastructure built with domain insights that enable a better service; structured in a way that can be leveraged by AI."

"Second, access to proprietary data that can be used to train and fine-tune models to an efficacy level that is impossible otherwise."

"Third, access to compute/talent that it can use to build and scale faster than competition."

"by thinking in domains, breaking the skeuomorphic barrier, redefining with AI-native solutions, guarding against technical limitations, and leveraging the technology where it creates most value."