Where will LLM coding take us?

Been writing small posts on how LLM based coding is evolving and how it will impact the software industry.The posts are aging so well that by Feb 2026 the markets and public are already convinced that LLM coding will make hand crafted code thing of the post.Thanks to anthropics claude releases.Hand crafted code might become an artistic pursuit even or just a teaching tool.

But where is taking us?

The industry : Would benefit a lot , now that we can ship out software faster than before . This also takes the pivot of success back where it always belonged; that is having superior ideas and executing it well till production.

The code : Perfecting creation of structural code is something that LLM based coding approaches have already demonstrated that they can master well.By now people have also demonstrated that even the intentional part code can also be crafted with LLM based coding approaches.
That leaves us with only the bare bones part the software,which is the code of business rules. The hard part of business which only we know .The best example of such code i could think of was how fortan programmes were written and used .Just the exact business rules sans the I/O or hardware or framework considerations.

(sample fortran code , generated via , well 😉 AI )

Individuals : The spot where our jobs were standing has shifted now.Instead of being the person who wrote code by understanding business from someone.We now have become the person who understand business so well that we can now use LLM coding approaches to fulfill the effect business needs.This was always why IT department existed in the first place (hence the fortran analogy).Is this making all of us business analysts ,not really but it does make us software product owners.A title that needs to be coined now.

Job losses : come on, ask any ceo, there is so so much business always wanted to do and achieve but could not due to to lack of software delivary speed.That constraints is removed now.

However: Transition periods are confusing,chaotic and not time bound.That we need to bear.But a total and complete job losses for us,No way.

What i wrote in Jan 2026 new year:

by the time 2025 is closing and the question on will Coding LLMs/Agents/IDEs replace human programmer? gets aksed ,i am at ” It might actually” moment.

Few quarters before i was at ” may be lets see”.

Thats the amount of progress state of the art has made .
Based on the report you read and the date of it ,some 40 percent of code and 256 billion lines of code has been outputted by models. Forget about the impact but such numbers means it is a large scale training and human verification that has already gone back into the models/to the labs.

With more focused optimizations from the AI LABs in the model architecture and deployments 2026 especially by plugging in some knowledge representation and knowledge priority/ranking might close the year 2026 on “Ah it happened”.

ps: there have been many shots taken at eliminating the programmer over last 4 decedes (really,its that old of a dream ).
These attempts tried visuals, formatted config,drag and drop and even document based approaches. Additionally the design based approaches like annotation and injection also kept on happening.

But the “so called” ability to reason offered by LLMs is breaking past the totality barrier of code generation from specs . My best bet is models becoming runtime aware is what will be the last finishing touch to the masterpiece.

feb 2026 :

Most of the buzz on llm coding is about how fast and easy it was for a non tech person to write an app or software was.At times we hear personal experiences of using llm coding by old techies like me .

But we hardly hear anything from people who sell software itself as the commodity!
So, the likes of android or spring or angular or chrome have a huge builtup of code complexity in them.They also have competing and conflicting features and a huge backlog of features they would have loved to implement yesterday.

The real and more believable metric of impact of llm coding will come from these places.If they are able to ship bigger feature sets faster,it would be the gold standard for productivity measurement around llm coding.

This also raises another question. When llm coding is so powerful why would we need their framework at all? would it not be easy to get your own curated framework based on your needs and use it. The picture is much darker for maintainers of smaller specialized libraries.

vibecoding or llm coding as i call it, is going to have its first evolutionary prey-victim soon. Cambrian explosion are beautiful and brutal at the same time.

Will Code generating LLMs replace programmers soon?

By the time 2025 is closing and the question is whether Coding LLMs/Agents/IDEs replace human programmers? gets asked,i am at ” It might actually” moment.

Few quarters before i was at “maybe let’s see”.

Thats the amount of progress state of the art has made.
Based on the report you read and the date of it ,some 40 percent of code and 256 billion lines of code have been outputted by models. Forget about the impact but such numbers mean it is a large scale training and human verification that has already gone back into the models/to the labs.

With more focused optimizations from the AI LABs in the model architecture and deployments 2026, especially by plugging in some knowledge representation and knowledge priority/rankin,g might close the year 2026 on “Ah it happened”.

ps: There have been many shots taken at eliminating the programmer over the last 4 decades (really, it’s that old of a dream ).
These attempts tried visuals, formatted config, drag and drop and even document based approaches. Additionally, the design based approaches like annotation and injection, also kept on happening.

But the “so called” ability to reason offered by LLMs is breaking past the totality barrier of code generation from specs. My best bet is that models becoming runtime aware is what will be the last finishing touch to the masterpiece.

I created my LLM from ground up, you should too

Edit ,based on the feedback: Building a car and building a Ferrari that can be sold in showroom are 2 diff things . Here are links to learning resources Stanford class , Karpathy and Rashcka

LLMs have moved on since ChatGPT

As an AI Architect with production level experience with AI/Neural Networks and regular reading of various research papers in the field, the arrival of ChatGPT didn’t stun my circle of technologists as much as it did to rest of the sector.

The initial use case in generative AI was focused on using it to literally “generate” insights via prompts. It quickly morphed into a general purpose toolkit for most routine (or shallow) inferences.In a way it was sort of a Java or Windows moment for the technology field. This was the era that felt very comfortable to someone with prior hands on tensors.

The Cambrian explosion of LLMs

By the time 2024 ended, the default choice for GenAI projects wasn’t always OpenAI. There were many LLM companies that shipped many versions of LLMs with different architectural designs and sometimes shipped an internal mix of experts. In the larger field of LLMs, the scientist would still discard such differences as more differences of the same thing. But if one is crafting solutions around LLMs one has to account for differences in modality, reasoning , pure chatbots and so on. Not to forget their parameters and tasks they were specialized in ( and beat some benchmark for ) and evaluating them/output.There is also enough business traction in moving to domain-specific models . All of these demanded a personal experiential look at LLMs.

Hands-on learning of LLMs

As such, there are multiple universities and experts who have amazing courses and materials on LLMs.My search zeroed in on the course by Stanford and the material by Karpathy and Rashka.In fact, one can ask ChatGPT to generate code to create one’s own LLM and it would give you 20-something lines of code to do it . But what insight can it provide?

Learning path and building knowledge pyramid

This boils down to first mapping the knowledge pyramid for LLMs as a field. I got this image created with the help of ChatGPT as a reference, but one should define their own based on where he is starting and how far one has to reach (there has to be an upper end).

Another aspect of my learning approach is to read 2-3 books on the same topic. And then move to read/watch/code more along the knowledge pyramid/path. That ends up being 10-12 books or equivalent of watching/coding for each new technology wave and a few months.

As such, each book has its own objective, so when books are titled as “from scratch”, “hands on”, “head first”,” in production, or “deep dive” etc, it makes sense to read the table of contents or preface. That can give you a good idea of the journey the author will take you (and what remains for you to do on your own).It is also useful to read the book cover to cover, including the references.

For my learning style, I wanted to be guided by someone who could take the learning from a blank slate and build on it, like how the LLM state-of-the-art was built. My approach is also to type along, run and experiment. This is where Sebastian Raschka’s material on LLMs worked best for me.

Building the Actual, not a Large Language Model

Building from scratch approach meant that I had to literally start with understanding transformers, choose text and convert it into embeddings, code attention and then move on to multi-head attention while experiencing the why of it, normalize the layers and code my own GPT model. This version just allowed me to chat around the text I trained it for. But the real intention was to see how everything I did leads up to and affects the output rather than saying look, here is my LLMs”.

I then learnt to add depth by evaluating the generative output and instruction, fine-tuning it (and LoRA). This was done by downloading GPT2 and loading/using it for evaluation.I also took a detour with Ollma since I wanted to use a few mode models for evaluation and see the minute differences.

There were 2-3 variations of my model that I fine-tuned for classification and also generating my riddle version of output (remember 4th standard kids inventing their own riddle/cryptic languages for speaking, that thing).

My most fun moments were experiencing the epochs and watching the output of the print command on the model object(this is real fun, do it). The code-along approach also gave me first-hand intuition around tuning and parameters.

The most frustrating aspect, which is also a reality check, is that debugging the mistakes is very tough. Not the syntax one, but the ones related to PyTorch and the attention overlap area.

Rashka has been very generous in mentioning additional material for the curious minds. For someone like me who is looking for a wide and deep understanding of the craft, taking detours to DPO and Bahdanau attention added to the joy.

What next

Personally, I went on to spend some money on Google Colab and try it all out at a little large scale/TPUs. But that’s as far as faithful learning goes.

My friend Kamlesh has given me a target to fine-tune my post-trained LLM and beat the incumbents on one of the benchmarks :).Depends on my weekend time and budget, tough. Moreover I and Kamlesh, have a history of big ambitious aims. Last year, we wanted to build a vision model to read documents in MoDi script ( later, IITKgp took up such a project with the institution we were to approach ).

As such, there are not many corporations that will be building LLMs of their own and beat the AI labs.

The amount of data needed to give an LLM commercial meaning is bigger of a problem than server and talent cost. There is, however, a case for creating domain LL models as the optimization cycles in the field accelerate, and things become cheaper.

While I wait for the river to flow in its natural course. The most certain thing in 2026 are :

1. We shall be building a lot of agentic stuff for production.

2. …Build more solutions around LLMs

3. New LLM architectures, their specializations and runtime costs in 2026 will look far different than what we saw in the last 5 years(better, I mean).

4. You and I will be crafting commercial solutions around LLMs and AI. Something that AI labs don’t have an interest in, domain expertise and commercial standing for!

So the learning will continue ..

Chatbots before LLMs

Designing Bot frameworks

In the era before LLMs, building a conversational agent meant ….one had to literally come up with sample phrases so that the Engine could do the Named Entity Recognition. Doing this by ourselves was limiting by definition as each person would have a limited range of expression. In that era, there were also tools that generated sentences if you gave them the base activity as input.(Now that we have experienced LLMs, this sounds funny on multiple count, but it used to work). (The era before production-grade NLP was even weird, i have documented that in an earlier post . )

This is also the era when a formal framework for chatbot interaction did not exist. So we ended up building our own framework. We had to debate the fitness of different NLP libraries (Stanford vs opennlp etc) and debate about their accuracy and code framework capability around interactions/invocation. In one case i was so frustrated with NLP that i designed a framework that would allow users to issue commands instead of chat by typing and we also had autocomplete/type ahead added to it . Like how you type elaborate commands on Unix, but imagine the typing experience of Google search for this. We were able to do this with some good keyword-parser-functional paradigm. And not to forget a brilliant developer with me,Nehal . But the momentum for a formal chat-style bot was huge and frameworks arrived soon.

Designing with chatbot Frameworks

Again,a debate will unfold about the chat agent framework selection. Most of the frameworks had similar capability around the core NLP and invoking services part but they had marked differences in the “flow” aspects, voice vs text capabilities and so on . This madea huge difference when the conversions we were supporting had multiple end states or conditionalities. The implication of this statement is that when we first did our chatbot in 2014/15 , the Alexa one the field of conversation design was not acknowledged (Alexa wasn’t available in India then, we got it from us). It was a few years later, especially when the commercial use cases came along, that the User Experience of the part of the chat interaction became mainstream/part of project work (Interaction Design) . Bot discovery and interaction design are useful and important even in the agentic era.

Getting the chatbots working

Once the engine of the framework we selected and trained would determine the action, I would write and wire handlers to do the processing.It quickly evolved into a chain of command pattern cum workflow or some sort. Giving rise to all sorts of integration issues.Message transformation, Errors, Retries, Auth and so on. Some of the frameworks had built-in capability to chain conversational flows (and pass variables/values around ). Most of them had some take on retries and how long the conversation can be, but it had to be discovered than being documented.

And then there was a user.At time, he would be technically disconnect from the chatbot, so we had to maintain the whole conversation state in the database. Sometimes the follow-up step in the processing needed more inputs from the user to I had to create a local and global state for the whole interaction to be recorded.

In some use cases we had to ask the user to upload receipts, which were processed by a vision model.And guess what, the image upload and processing could take more and varied time for my chatbot to remain active.So we keep some keepalive and sweet nothing “status… updating…” going to the user,to fool the whole system. Moreover, the framework chosen didn’t have native support for this sort of outside call, so everything had to be bundled together.

In another use case, we had served him content based on a help document.This had to be done with a combination of a search index in case the document repo was too large.In case of a structured FAQ one of the engine ,NIA had built in the capability of mapping queries to document keywords (density).

As a side note , many engine had some capability to detect obscenity that sufficed.PII interestingly panned out in black and white in many cases (due to the domain and use case mix at that time).

Voice Video and Human agent handovers

The voice based chatbots had a different set of additional issues.The engines from AWS and Google had built in ability to prompt for the question again if the pronunciation wasn’t clear. At times, this ended up in multiple retries/pass at the same handler so it had to be taken care of (since not all services were idempotent ).At time, the user would totally rephrase the ask, which would throw our design off guard .

Another fun aspect is that as Alexa evolved into a voice device with screen, we suddenly had to take care of the visual aspect of the interaction. Multilingual support was out of the box, so life was cool .

Video bots were a different thing to handle. Out stuff didn’t make it to production but the idea was to emulate a human face with expressions (confidentiality etc etc).It was pretty impressive for that time .

In one of the case, we had to handover the interaction to a human agent based on the predefined scenario.It was a straightforward integration to another system with some adjustments to timeout. But when the requirement evolved into passing the whole conversation to a human agent ,we realized that we didn’t have a handle to chat interaction that is provided by the framework! So I logged them as passed it on. That eventually led us to design another product around chat interaction analysis/insights and designs and it went on to (then ) compete with chatbase product.

Some notes

When we select a new technology or framework, it’s best to adapt to its way of doing things. However, when the field is new and evolving, the capability mismatch can be huge. In many case,s when it came to call/orchestrate service calls, my experience with traditional banking development helped me handle the issues with ambiguity, state and performance better than the chatbot native generation of freshers who looked towards the state of the art for solution.It also mattered because most of the recommended remedies around these problems were to use some sort of ESB or wire RPA somehow.I had found them out of sync with spirt of chat interaction (Now that we have LLMs to reason, plan and orchestrate them, i feel validated with some sort of emotional closure) .

Later, when ChatGPT happened and we moved on to RAG and Agentic tools,it felt like I was remaking the Spiderman movie Franchise for the third time. The story from there on in next post.

Embracing the Weird Stuff :becoming AI Architect

Weird Stuff that added flavor

As any good technologist has new “weird stuff” knocking on the door while he is focusing on some other technology. This is also the theme of how my resume got built.

Python was the first programming language I started my career with. At that time I was working on code generators,compiler switches and other “weird stuff” while my friends were working on struts and j2ee. So with much effort I moved to those technologies.Working in banking domain in mid 2000s we would create jobs to facilitate what was then called as business intelligence.These were early ancestors of data cleaning, aggregation and summarization that was done via code and service via ui. It didn’t feel amazing but it was work so we did it.

Enter ChatBots

Cut to 2009s I was obsessed with JavaScript ,Spring and whole SMAC buzz and then another “weird stuff” came my way . There we were building a chatting bot for relationship managers to support the internet banking users .I used the IBM SameTime stack while my colleagues used MS stack. Out bot could do basic chat and then allow screen sharing and video calls.

Post 2010 while I was chasing Angular ,microservices and my Ethical hacker certification, another “weird stuff” called Hadoop came along. So there I was working on MapReduce, Storm and Spark. We had do some Data Science work using Apache’s MLLib. My background with BI allowed me to work on it but converting beautiful data structures to some numbers and flags didn’t seem like calling to the programmer in me 🙂 ,so I was very happy when Data Scientist joined our team. By this time it was clear that DS was the hottest job of the decade but I had moved on to Node and Dockers of the world and then another “weird stuff” called R came along. And again i was supposed to build a chat application .I was so angry to have my “When Harry meets Sally” moment that a wrote a blog post against chatbots 🙂 (here).

ChatBots strike again

It was by this time that the role of AI-Architect was coined ,brining me peace .So I moved on to a projects working full time on AI and Automation .Our projects had working going on neural network of all kinds and all of were hands on. But industry had some other plan .A “weird stuff” called a chatbot came knocking on the door .So there I was building chatbots on multiple frameworks.We had Alexa,Google’s Dialogflow,Amazon and IBMs stacks and open source framework called RASA. Not to forget mentioning our Inhouse AI Studio/Chatbot solution called NIA that my friend GuruPrasad was developing .

This was probably the Jurassic age of bots .

Have LLM use cases platued

This is the spread of use cases of ChatGPT usage. What is not evident from the chart is that people didn’t really ask/need an LLM to do it.
Most of the users would be totally cool to have one app for images, another for summaries, another for coding assistance and just another for music.

( The chart was created by asking ChatGPT )

So long the features and cost are reasonably good, most of users won’t even care what’s powering them.

The everything-everywhere-all-at-once nature of LLMs signifies that their builders have been successful in discovering use cases around the powers that GPT gave them.
This is also the game where feature parity tends to plateau very soon. Ask anyone who has developed a successful consumer-focused mobile utility app or look at your own mobile app installation history over the last decade.
While there is always some buzz around the benchmark, the latest model from some company has beaten but 5 years into LLMs, the real question we need to ask is does the end consumer notice or even care ? Especially the average retail internet consumer? (where the market capitalization lies 🙂 )

Which is to also imply that for end internet users, the craze or utility of GenAI might have peaked as novelty and is taken for granted at par with you mobile phones.
When was the last time did you really felt the difference between version 10 and 11 of your mobile phone?

Ah LLMs ….

PS: 60% of paying chatgpt users are enterprise

Podcast : AI threats and Future outlook

In this podcast in Marathi language hosted by Omkar Dabhadkar i spoke about hashtag#AI . As such Marathi language is spoken by 80 to 100 million people but when it comes to Artificial Intelligence we did not have not-dumbed down content .
In this video i spoke of impact of AI on society in general and IT jobs in particular .The discussion also touched up Indian efforts in fields of AI , nature of Indian IT industry, Indian startups , the need of different skilling that AI will enforce upon freshers and the remedies colleges can take . We ended up also covering Gen-Z and my optimism about them .
https://www.youtube.com/watch?v=A-3CWRoQQnY

Dixit’s dilemma of Generative AI

How do you verify output of thing more intelligent that you ?
That’s Dixit’s dilemma of Generative AI. Its not famous yet 🙂 .

But as we employ Generative AI more this dilemma will become famous . Take the case of code generated by GenAI , you need the developer using it , to be more skilled than the complexity of the code being generated to find out correctness of the output .
A corollary of Dixit’s dilemma is that our Qualitative gains from GenAI are capped by the Quality of the verifier !

Did someone talk of GenAI making your job redundant ? It can in the quantitative domain but not in qualitative domain (it the mundane can be but the classy can’t be ).

EDIT : Some more clarifications

The holy grail of code generation .

The holy grail that we all are chasing from tools like copilot is that they can generate the whole code from requirement documents .This is not a stated need but that is what people are hoping for . read on .

If you see the popular demos on code generation by GenAI tools there are few scenarios that occur often :

1. Given a table or json generate complete webservice

2. Find the performance or security bugs in code

3. Find structural issues with code as per the language specification

4. Flag issues with dependencies from deep within codebase

5. Document my code

6. Write test cases , elementary ones .

Now if you see the capabilities of IDEs , code generators ,profilers ,linters such things existed since decades for most of the programming languages .But they were in siloed in nature .So we can give credits to LLMs tools for bringing it all together .This can benefit existing codebase a lot .

The situation becomes interesting for new code base . The demos where one can make the tool write a script for managing a server or sorting algorithm are in essence demonstration of smart lookup (for relevant code fragment ).This can benefit seasoned developers by saving few keystrokes .What next ?

What do we all expect next is , can the tool take long format requirements and generate functional code .That is to say can it read my Jira requirements and understand my user persona , interfaces , domain jargon ,different flows so on and so forth . It is certainly passible to give lots of prompt context to AI tools and create such a demo .

Can we do it at scale for an organization ?

Can we do it in a manner where prompting the requirement context doesn’t becomes as complex a job as that of programmer ? AND

How can we validate that what ever is generated is functionally correct without needing a new battalion of validators ?

These are the practical question for which we don’t have answers .

This is not to say its not passible , domain languages for automatic code generation has been in place for long . Coupled with GenAI they can do wonders .Its just the current breed of GenAI are not made for this sole purpose .The day that happens , all the GenAI demos will happen to CTOs instead of CEOs 🙂 .Read that again 🙂

Pair Programming with a Large Language Models

Pair Programming with a Large Language Model .

Guess this wording settles the debate against the developers . The narrative has been set for use of LLM for productivity boosts with this phrasing !But are developers burdened with expectations or is there some hope of real usefulness to developers with the help of AI?lets check .

Using llm for coding task is expected to give huge productivity boost . Mckinsey already released a report on measuring developer productivity .Organizations like ChatGPT/OpenAI/Microsoft and the likes of meta/CodeLLma are coming up with code LLMs and numbers on productivity boost .There are new coding LLMs or LLMs with such capability coming up every week ,so this list is non exhaustive .

Paid to code or paid to recollect ?

However the question remains , can LLMs in its current form boost developer productivity across the board . The answer is no .Its only the developers who have a clear picture in mind of what they want ,can save some time with typing work with tools like copilot .

However the cognitive work for the developer now shifts towards reviewing the code generated by AI .Which is heavier mental work then writing code via learned skills .

This also raises a question of memory recall of such AI generated code at scale ! .Developers after all are not paid just to “code it” . They are valued for knowing their code , maintaining it and debugging it . Will the developers have a clear mental model of such AI generated code ? and what is the limit of such active recollections ? For now we we don’t know answers to these questions .We can take some guess from our experience with social media .This experience tells as we honestly don’t have first class recall of the virtual updates from our network when compared to actual moments spent in physical world. Even if someone argues that social media is not approached with the serious ness coding is ; the limits of our fatigued brains are known now. So I would conclude that the so called boost to developer productivity is overhyped .

The Future of coding with AI

There is however a good case of vendor curated assistants .Imagine an coding assistant from spring/java or vuejs or likes. These can help us with smarter code highlight ,offer deeper code review and provide standardized lookup code samples for developers to use. They can also have a real connection to the language runtime and offer are better suggestions on optimizations and debugging .

This is probably the less shiny ,less geeky middle path to developer productivity with the help of AI. Exactly in the spirit of Pair Programming with a Large Language Model .

(image via genecraft )

ChatGPT LLMs , AI for consumers and Future

Remember “talking tomcat” ? This was one of the unsung hero’s that made android OS popular .A cartoonish cat that can echo what you spoke in cat voice .This was a fine demonstration that with prevailing hardware android could replay voice with modification . Of course the lay uses would not word it this way but they got the point anyhow .This is exactly how technology goes mainstream . What followed tomcat is set of apps that could then add dog face to your picture then to your video and so on .By then the novelty had faded , the capability was taken for granted .People expected mobiles to this much at least and then moved on .

Users perceive ChatGPT as bright teenager

ChatGPT in its current form is talking tomcat of AI .This is first time common lay person is getting a demo on how much more can be done with AI . For all he knows ,its nice chatbot that can do brainy stuff . How brainy you ask ?

This is important question to ask .If we were to equate the “general” feel that ChatGPT gives to most common interactions people have then what would it feel like ? Remember spelling bee contest that are held in USA ? For most of the lay users chatgpt feels like a smart teenager that is G.K bee (in that sense). This is how consumer mass market sees innovation . Simplified and equated to mundane things in his/her life .

Two directions LLM needs to evolve

LLMs for consumer use cases

The above discourse make it clear that large language models(LLM) need to evolve and also be understood in 2 distinct set of parameters . One is the consumer angle . Taking a leaf from how android was seen or how voice assistants were seen ,LLMs for lay consumer simply means that computers can now answer diverse and more complex answers(#1).This also implies that how much ever the the press focuses on ChatGPT , the future of LLM is in the usage driven consumer space . These are specialized models that do one or few things in one are with unambiguous and immediate utility . Imagine an app that can take your picture or live videos and suggest fashion makeovers to you (ideal copyrighted hereby 🙂 ). Or take for example BoomberGPT that aims to cater to offer targeted consumer utility for end users . Similar such gpt models can be built around legal advisory ,medical first line help ,cultural adjustment needed during travel .A general LLM that can filter money laundering names can makes life easy for regulators .

OpenAI is aiming to be THE general purpose engine for all such use case via its plugin architecture .Can it succeed in giving curated user experience is matter of debate with ChatGPT 4 .With future versions of ChatGPT things might change .But it can also be case of diminishing returns where the model size and compute cost cant justify future refinement . As far as end users of AI are concerned they are interested in the utility than specifics of the software internals .

LLMs for AI community

Information ownership and privacy leakage are tow important issues any LLM has to handle .We have learned many lessons from years of legal cases and government request of page take downs to search engines . Once the hype subsides the LLMs fed on public information will soon get into all of this mess .

And don’t even think what will happen if ChatGPT gives an answer that is blasphemous in some culture . This is my main reasoning as to why GPTs in chat mode wont harm Google’s search business .There is need for sanitizing ,curating and localizing the outcomes and none knows it better than google .Just that they need to offer same LLM toppings on their pizza too .

But as community we need to keep pushing the boundary on parameters .Efforts will also be made to plugin knowledge representation (universal or specific ) with LLMs for more deterministic answers .Size/cost optimization and Realtime model updates at this scale and geodistributed LLMs are few directions in which efforts can go

Premature Universal Knowledge claims by LLMs PRs

Its not the AI scientist but the PR machines that are claiming that we are very close to general intelligence .So far computing is concerned LLMs have given a feel that they are generally intelligent .We must remember that Googles LamDA was the first LLM that was said to be sentient (funny how google lost the PR battle ). So on the basis of “feels like human” the LLMs have started giving a feel that it is human level or intelligent or both . Moreover given a focused effort a “self” neural module can be built into LLMs .Say an LLM that can sense that its cloud billing is crossing the daily threshold and its starts feeling tired now .

This is funny example but its tells you the inherent problem with sentience of machines .Without change and limitations that living being experience , machines can achieve plant level equality to being alive .For animal level behavior they need to have ability to grasp animal concepts staring from reptilian to mammal and then human Brain .And also the concept of emotions that affect their whole existence (as opposed to giving a feel of an emotion).

So far the end user experience is concerned LLMs in current form do “feels like human !”

The second challenge is do LLMs have universal knowledge .Any one working on web search or elementary ML knows the answer is NO. Current LLMs are limited by the thin slice of information it was fed .So in reality this is more of a media claim than anything an AI scientist believes .

Societal Impact of post LLM era of AI

How has TV or mobile or internet affected humans/Students/Kids ?

The cognitive-behavioral impact that above waves of revolutions had on humans will be further multiplied by the capability expansion brought by AI (Apart from LLMs , image search was one such capability expansion but it was under hyped). So this issue and the debate and the remedies that follow are known to us .

However the issues of cultural, individual and situation sensitivity is something that the centralized models are not geared up to handled . Nor are the efforts behind them are aiming to .So good number of “situations” where “feels like human” AI did not really “work like human” will come up in coming decade .

New AI frontier

LLMs have not expanded the frontier of AI as field .However they are first class coming of age story for the community .As next level evolution AI can now evolve into two directions .

Personal Models

Current efforts in AI designs come from corporate style centralized AI desings .If there is any effort where a personal model can exist it will be more revolutionary than LLMs . A sort of “AI thing ” that stays with individual and monitors and learns and advices him/her .Imagine your fitness tracker which can suck data from your online activity and also listen to your speech and brain MRI .The corporate business case for this is lacking but the challenges pursuit of such “AI thing” can have on the AI community is huge .

Architecture for Sentient AI in 2026?

Leaving aside the debate of whether we really need it ,once the LLMs are seen as normal a concerted effort of AI labs can work on developing new neural architecture ,that learns from evolution on sentience in living organisms .Whether we will succeed or not ,AI community deserves to pursue its own “voodoo doll” moment like all branches of science .In fact if present AI labs gets enough money they might work on it sooner than 2026 .It is one of those effort worth failing .