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The Future of Software Development

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The Future of Software Development

The Future of Software Development is a world where AI models become so smart, so fast, and so cheap that we simply tell the computer what we want, and AI agents build, test, and ship it for us. The work of software engineers moves from writing every line of code to building, checking, and improving the systems around these AI models.

In this blog, we will learn about the future of software development. We will also see how fast AI models are improving, how we moved from the code editor to the terminal and where we will move next, why operating systems and protocols must change for AI agents, how apps will create their screens on the fly for each user, which skills will be in demand in the next 5 to 10 years, and what we must do today to stay relevant.

We will cover the following:

  • Why I am writing this blog
  • The world we must imagine
  • How far we have come in less than a year
  • From the code editor to the terminal to just talking
  • An operating system for AI agents
  • Screens created on the fly for every user
  • New protocols for AI agents
  • The skills that will be in demand
  • Why experience matters more than ever
  • What we can do today

I am Amit Shekhar, Founder @ Outcome School, I have taught and mentored many developers, and their efforts landed them high-paying tech jobs, helped many tech companies in solving their unique problems, and created many open-source libraries being used by top companies. I am passionate about sharing knowledge through open-source, blogs, and videos.

I teach AI and Machine Learning at Outcome School.

Let's get started.

Why I am writing this blog

I keep thinking about one question again and again: what will happen to software development in the next 5 to 10 years?

Recently, I decided to share these thoughts so that everyone can take advantage of how I am thinking about the future. So, here I am.

You can agree or disagree, it all depends on you. But I try to think from a logical perspective, and I will explain the logic behind every point so that you can decide for yourself.

The world we must imagine

ChatGPT, Claude, and Gemini are all built on top of AI models. When we talk about an LLM (Large Language Model), we are talking about this kind of model that works with language.

Now, let's see how fast these models are improving.

In the current scenario, every six months we are getting a model that feels around 10x better. So, within a year, we are getting a model that feels around 100x better than the one we had a year back.

There is no single global scorecard to measure this. But whether we measure it by feel or by numbers, we can agree on the fact that models are getting much more intelligent every year.

So, when I think about the future, I am not thinking of a model that is just 10x or 100x better. I am thinking of the world after 5 to 10 years, where the model is 100,000x better, or even more than that. Nobody knows the exact number.

We must imagine a world where the model is:

  • Very, very intelligent - far smarter than what we have today.
  • Very, very fast - it gives answers almost instantly.
  • Very, very cheap - using it costs almost nothing compared to today.

Keep these three points in mind. Every prediction in this blog comes from these three points.

How far we have come in less than a year

Let's go back in time a little.

At the end of 2025, most people were saying that coding will never be automated by AI.

At that time, AI inside the IDE was mostly doing autocomplete. Means, while we were typing code, it was suggesting the next few words, just like our phone keyboard suggests the next word while we type a message.

And people were saying, "Autocomplete is not a big deal. It is not helping us a lot."

Now, compare that with where we are today in 2026, just around 8 to 9 months later.

We have moved from the IDE to the terminal. The terminal is a simple text window where we type instructions for the computer. Today, developers open the terminal, talk to an AI coding agent in plain English, and the agent writes the code, runs it, finds the mistakes, and fixes them.

So, in less than a year, AI went from "suggesting the next word" to "writing the whole feature". This is the speed at which things are changing.

From the code editor to the terminal to just talking

Now, the next big question is: what comes after the terminal?

I feel that the terminal will also go away.

Let's say we want a new feature in our app. In the future, there will be an interface where we simply say, "I want this feature, ship it to the users." That's it. And we will be able to say this using our voice as well.

After that, everything will be automated. The system will be intelligent enough to do the following on its own:

  • Build the feature.
  • Find where the bug is.
  • Fix that bug.
  • Run the loop again and again, checking and improving the work.
  • Put the final version into production.

This loop of build, check, fix, and repeat is the key.

Note: In the current scenario, this is not fully happening. Some companies are able to build this complete loop, and some are not. It is a process, and it takes time. But I strongly believe that it will be fully automated in the future.

So, the journey looks like below:

IDE (autocomplete)  ->  Terminal (AI coding agent)  ->  Interface (just tell what we want)

Here, we can see that at every step, we write less code by hand and communicate more of our intention.

If we want to go deep into AI Agent, Agent Architecture, and Loop Engineering, we cover all of them end to end in our AI and Machine Learning Program at Outcome School.

An operating system for AI agents

Now, here is the important point. Whatever operating systems we have today are built for humans.

That is why they have a UI (User Interface). The UI is everything we see and touch on the screen: buttons, icons, menus, windows, and etc. It exists so that a human can look at the screen and interact with the computer.

But, here is the catch.

AI agents are already software. An AI agent does not have eyes to look at buttons. It does not need icons or windows. For an AI agent, there is no need of UI at all.

So, the operating system must change for the AI agent. AI agents will be the ones operating it.

And this is not something we can solve by just adding a thin layer on top of the current operating system. I feel that the foundation itself needs to be changed.

Note: DHH, the creator of Ruby on Rails, has been sharing a lot on X (Twitter) about an operating system setup that he has built. We can check that as well to see how people are already rethinking the operating system.

What will this new operating system look like?

I feel that the operating system for the agents will be intent-based.

Intent simply means "what we want to get done".

Let's understand this with an example.

Today, if we want to send a file to a friend, we open the folder, find the file, open the chat app, click on attach, select the file, and click on send. We tell the computer how to do it, step by step, by clicking.

In an intent-based operating system, the agent just says the intent: "Send this file to my friend." The operating system understands the task and gets it done.

In simple words, today we tell the computer how to do something. In the future, the agent will tell the operating system what to do.

The agent will talk to the operating system using APIs and MCP:

So, there is no need of the user interface at all on the operating system side.

I feel completely, 100% sure about this. This needs to change, and it will change.

This was all about the operating system for AI agents. Now, let's see how this changes the apps that we use every day.

Screens created on the fly for every user

Today, every app we use has taken a lot of time from designers and developers to design, build, and put into production. Even with AI coding agents in our hands, building a feature and shipping it still takes a lot of time.

And one more thing to notice: the apps we use today are completely the same for all users. The same app looks exactly the same on my phone and on your phone.

Now, let's think about the future.

In the future:

  • The time taken for inference will become 1000 times, 10,000 times, or even 100,000 times shorter than today. We do not know the exact number, but it will be in that range.
  • The cost of inference will become very, very cheap compared to today.

So, given that we can work at least 1000x faster than today with AI agents, the UI will be created automatically, on the fly.

Means, when we have the intent-based operating system for AI agents, the AI agents will build the screen for us at the exact moment we need it, and that too personalized for each particular user.

Let's say two people open the same shopping app. One person loves big pictures and simple buttons. Another person wants a detailed list with prices and ratings. In the future, the AI agent will create a different screen for each of them, instantly.

This is only possible because of our three points from earlier:

  • The model will become very, very intelligent.
  • The model intelligence will become cheaper.
  • The model will become very, very fast.

That is the reason why the UI can be made on the fly in the future, which is not the case at the moment.

A quick note for you

No matter which tech domain you work in, get familiar with these topics:

  • LLM
  • RAG
  • MCP
  • Agent
  • Fine-tuning
  • Quantization

We put it all together in one video:

AI Engineering Explained: LLM, RAG, MCP, Agent, Fine-Tuning, and Quantization

No need to stop reading - bookmark it and watch later when you get time. Future you will thank you.

Now, let's get back to the topic.

New protocols for AI agents

This was all about the screens. Now, let's go one level deeper, below the operating system.

A protocol is a set of rules that two computers follow to talk to each other. Just like two people must speak the same language to understand each other, two computers must follow the same protocol.

For example, HTTP is the protocol that our browser uses to talk to websites. We have seen different versions of it over the years, like HTTP/1.1, HTTP/2, and HTTP/3.

These protocols were made for the way apps talk to servers today and in the past. A server is a powerful computer that stores data and sends it to our apps whenever they ask for it.

But AI agents will talk to each other a lot, and very fast. So, I believe there will be a completely new protocol for faster communication between AI agents, not what we have today.

It will take some time. But when the operating system changes, all the basic layers that sit under it will also change. The hidden rules that decide how software talks to other software must change for the AI agents.

Now, it's time to learn about what this means for us as software engineers.

The skills that will be in demand

So, overall, we have to think of a world where the model is very, very intelligent, very, very fast, and far, far cheaper than what we have today.

In that world, the way we build things around the model will become completely different from what we have today.

So, what will we build? The answer is: everything around the model.

Let's see the roles that I feel will be very important in the next 5 to 10 years, and are already in demand today.

Harness Engineering and Agentic Engineering

Harness Engineering is everything we build around the model to use its power effectively.

Harness Engineering = Harness + Engineering

The word "harness" comes from the equipment we put on a horse. A horse is very powerful, but without a harness, we cannot use that power to pull a cart. The harness turns raw power into useful work.

Similarly, the model is very powerful. But to make it do useful work, we must build things around it:

  • We write tools for the model. A tool is a small piece of code that lets the model do something in the real world, like searching files or running tests.
  • We write code that gives the model access to those tools effectively.
  • We decide how the model plans, which steps it takes, and when it stops.

Everything around the model comes under harness engineering. And if we understand how the model works internally, we can do a great job of harnessing its power.

Agentic Engineering is very close to this. It is about building AI agents that take actions and complete tasks on their own. We can also build domain-specific agents, for example, an agent built only for banking or only for healthcare.

Eval Engineering

At the end of the day, we want to ship something that is of production level, with a lesser number of bugs.

So, here comes the Eval Engineering into the picture.

Eval is short for evaluation. Eval Engineering is the work of checking what the AI has done before we put it into production.

Think of it like a teacher checking the answer sheet before announcing the result. If the AI writes a feature, eval engineering answers questions like: Is it correct? Is it safe? Does it break anything else?

When AI agents build most of the software, checking their work becomes one of the most important jobs. I think eval engineering will play a big role in the next 5 to 10 years.

We have a detailed blog on how to evaluate AI agents that explains this in depth.

LLM Inference Engineering

We already learned that inference is the work the model does to produce an answer.

LLM Inference Engineering is the work of making the model produce answers faster and cheaper.

Two words matter here:

  • Cost: how much money we pay to get an answer from the model.
  • Latency: how long we wait to get the answer.

I think both cost and latency will become around 1000 times lower than what they are today. But this will not happen on its own. Someone has to do this work.

So, there is a lot of great work to be done in this domain for the next 5 to 10 years, and beyond that as well. I think LLM inference is a big thing that will play a role over this entire decade.

Memory in AI Agents

Another big challenge is memory in AI agents.

Every company has its own knowledge base. A knowledge base is all the information a company has collected: its documents, its rules, its past decisions, its code, and etc.

When the employees of a company use an AI model, that knowledge must be included, so that the answers fit that company. And these knowledge bases are private. No company is going to expose them to other companies.

Let's say a new employee joins a company. On the first day, the employee is smart, but does not know how things work in that company. After a few months, the employee remembers everything. The AI agent needs this same kind of memory.

So, there is a lot of great work to be done in this area as well: how to give AI agents the right memory, safely and correctly.

FDE (Forward Deployed Engineer)

AI labs like OpenAI are building a lot of products, and they want to sell those products to other companies too.

So, for that, we have the FDE, which stands for Forward Deployed Engineer.

An FDE is an engineer who works directly with customer companies to help them understand and use an AI product to solve their real problems.

In simple words, an FDE is a technical expert who goes to the customer, understands their problem, and makes the AI product work for them.

These are the roles that I feel will be in demand, and I would say that they are already in demand today.

To master Harness Engineering, Evaluation of LLMs and Agents, LLM Inference Engineering, and Memory in Agents, we build an AI Coding Agent from scratch in our AI and Machine Learning Program at Outcome School.

Why experience matters more than ever

When we have the knowledge, the right job comes to us.

I have never seen a person who has real expertise in a topic, and that topic is in demand, and still that person is not getting a job in that domain. I have never seen that.

If we have expertise in something that is in demand, we will definitely get the job we deserve, and that too with the salary we deserve.

Many of us today are backend developers or frontend developers.

Now, the question is: "I am a backend developer or a frontend developer today. Can I still move into these roles?"

The answer is yes. It is not like that our current experience becomes useless when we learn these new skills. In fact, companies want experienced developers, and there is a huge demand for experienced developers who have these skill sets.

This is logical. Let's take the FDE role as an example.

Do you think a fresher, someone who has just started their career, can go to a big company and explain the AI product that OpenAI sells? Very difficult.

Do you think a non-technical person can explain that AI product? Very difficult.

But a person who has experience in software engineering plus AI engineering, and understands all these things, can definitely go and talk about it. That is a very good opportunity for that person to become an FDE.

So, our current experience is not wasted. Our experience plus these new skills is a very powerful combination.

What we can do today

So, now, what can we do today?

The only thing that is in our control is upskilling ourselves, which means learning new skills that will be in demand. And that too, by thinking logically.

Before putting effort into learning any topic, we must ask ourselves:

  • Does it make sense to put effort into learning this topic?
  • Is this topic the future or not?
  • Can I get the most out of it or not?

First, think logically. Then, upskill yourself.

So, overall, we need to learn how to build systems with speed and with accuracy, so that we can ship something into production in record time. For that, we need:

  • Harness Engineering and Agentic Engineering
  • Eval Engineering
  • LLM Inference Engineering
  • Memory in AI Agents

And whatever we do around the model, we must be very, very good at it.

Go for the FDE role, go for the AI engineering role, whatever suits you. But before that, do this exercise: think about what you must be doing today to stay relevant for the next 5 to 10 years.

I keep myself updated by reading, writing, and doing a lot of experiments around what is happening in this AI world. And frankly saying, I am super, super excited about what will happen in the next 5 to 10 years.

This is how I feel the future of software development will evolve. These are the things that will define the next 5 to 10 years of software engineering.

This is a very, very good time to be alive, because we are seeing this transition live, and it is happening today.

Frequently Asked Questions

Will AI replace software engineers?

No. The work of software engineers moves from writing every line of code to building, checking, and improving the systems around AI models. The AI agents build, test, and ship, while engineers build everything around the model, such as the tools, the evals, and the memory. Whatever we do around the model, we must be very, very good at it.

Can a backend or frontend developer move into AI engineering roles?

Yes. Our current experience does not become useless when we learn these new skills. Companies want experienced developers, and there is a huge demand for experienced developers who have skill sets like harness engineering, eval engineering, and LLM inference engineering. Our experience plus these new skills is a very powerful combination.

How fast are AI models improving today?

In the current scenario, every six months we are getting a model that feels around 10x better, so within a year, the model feels around 100x better than the one we had a year back. There is no single global scorecard to measure this, but models are clearly getting much more intelligent every year.

Prepare yourself for AI Engineering Interview: AI Engineering Interview Questions

That's it for now.

Thanks

Amit Shekhar
Founder @ Outcome School

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