There is a pattern in tech that keeps repeating itself. A product or a company gets to scale first, whether by launching first or by spreading faster than everyone else, and once it wins the market, it does not stop there. It slowly starts absorbing everything around it, until a lot of what used to be separate things end up living inside one single platform.
I have been thinking about this pattern for a while, and AI is just the latest example of it. It is not a new story. Same story, new player.
The Original Race: Distribution Over Everything
Go back to the early days of Silicon Valley, and the story is more interesting than just who launched first. Xerox actually built the first proper graphical computer, the Xerox Alto, way back in the 1970s. Apple came next with the Macintosh in 1984, built on some of those same ideas. Microsoft was late to this party. Windows 1.0 launched in 1985, and it was seen as a rough copycat, not a leader. Apple even took Microsoft to court over how similar it looked.
So Windows did not win by being first. It won because of what Microsoft did after being late. Instead of keeping Windows locked to one type of hardware like Apple did with the Mac, Microsoft licensed Windows to every PC maker willing to pay for it, IBM, Compaq, Dell, and dozens of others. That meant Windows ended up running on cheap, mass produced machines everywhere, while Apple stayed limited to its own expensive hardware. Businesses built their software around Windows because it was already everywhere, and once that happened, switching away became too costly for most companies. IBM and Xerox both had strong ideas at that time too, but neither one built that same kind of wide distribution, and both lost ground because of it.
Then there is the Microsoft and Apple story from a bit later, one of my favourite examples. In the 80s and 90s, Apple had good hardware but weak software. Microsoft had no hardware at all, but strong software. So they teamed up. Microsoft software started running inside Apple machines. On paper, these two were rivals. But in reality, both cared more about locking in users through wide reach than fighting each other. Get your product everywhere first, sort out the rest later.
In those days, new features came slowly, maybe once a year or once in six months. Companies needed a lot of skilled engineers, hired fresh from colleges and universities, working for months to build the next version. It was slow compared to today, but the thinking behind it was the same as what we see now, get your product into as many hands as possible, then build everything else around that reach.
Then Facebook Did The Same Thing, Just Faster
Fast forward to 2008. A product called The Facebook exploded the same way PCs did in the 90s, just much faster this time.
I still remember our local newspaper, Eenadu, carrying a half page story about Mark Zuckerberg and this platform. That is how big it felt, even reaching a regional newspaper in India.
What Facebook did was simple. It gave people one single place to share everything about their life. What they like, who they married, what they watched last weekend. It made staying connected effortless, and once people joined, they never really left.
Facebook did not stop at being just a place to connect. It kept adding more and more inside itself. A marketplace to sell things. Subscriptions so people could support their favourite creators. An events page so fans could register for things happening around them. Slowly Facebook stopped being just one app and became a whole world by itself, with everything a user might need packed inside it.
It also went after anything that looked like competition. It bought WhatsApp. It bought Instagram. Both are now such a big part of daily life that people do not even think of alternatives anymore.
Google tried to fight this with Google Plus. It failed. Hangouts and Allo tried to fight WhatsApp. They also failed. Twitter built a decent user base, but nowhere close to what Facebook had.
The lesson is simple. Reach the market first, expand fast, then absorb anything that comes close to competing with you. More users mean more profit, more profit means higher value, and that higher value funds even faster expansion. It just keeps growing on itself.
Now AI Is Running The Same Playbook, Just Faster Still
I want to be clear here, I am not against AI. I think it is one of the most useful things to happen to how we work in a long time. But it is worth pointing out clearly what is happening at the industry level.
Tools like Claude, Codex, and Gemini are giving big tech companies the fastest speed to market they have ever had. What used to take a year to build can now be built in weeks. This is a real and useful jump. But it is also creating pressure. Companies that move slower are watching others move faster, and the fear of missing out is real, even for big companies with a lot of money.
At the same time, companies are automating a lot of work that earlier needed big teams, and getting the same or better results with fewer people. This is genuinely happening, not just hype.
Giving The Skeptics Their Due
This kind of change never comes without people questioning it. Windows had its critics. Facebook had its doubters too. AI has its own set of doubts today, and it is only fair to look at them honestly instead of brushing them aside.
AI does make mistakes. It gets things confidently wrong sometimes. The security around these systems is still being worked on, and running these large models is genuinely expensive. These are not excuses made up by people who dislike AI. These are real problems that engineers are actively trying to fix right now.
At the same time, most people who raise these concerns also quietly know that a large part of repetitive, manual work can now be done reliably by AI. The problems are real, but they are the kind of problems that keep shrinking with time, not the kind that stop the whole shift. Early computers from IBM had flaws too. The first version of Windows was clunky. Facebook had privacy troubles from its very first year. None of that stopped the pattern from playing out. AI’s current problems will not stop it either.
The Race Between AI Labs And The Consulting World
Every big AI company, OpenAI, Anthropic, Google DeepMind, xAI, wants to be seen as the leader in this space, in terms of money, users, and influence. At the same time, the big IT consulting firms, TCS, Wipro, Accenture, Persistent Systems etc want to be the ones who bring AI into every company and earn from doing that work.
Neither side wants to lose out, so instead of fighting each other, they are teaming up. AI companies are signing deals with big enterprise and financial firms to get their tools deployed quickly, and the consulting firms get to stay relevant as the ones who actually set it up. It is the same Microsoft and Apple story again, just wearing a 2026 outfit.
Look at how AI companies build their products today. Claude, for example, now has plugins that do the same job as products already sold by other companies with their own decent user base. The difference is scale. A big AI company that already has millions of users can add these features inside its own app, and use its scale and AI tools to build them faster than smaller standalone companies can. Not every one of these will succeed, but the direction is clear, big platforms are aiming to become more complete, all in one products.
But Let Us Push Back On The “SaaSpocalypse” Idea also
There is an argument going around that AI coding tools mean one person, or a small team of ten people, can now build a real competitor to Salesforce or Microsoft. I do not think that holds up, and here is why.
Building something like Salesforce is not just about writing code. It needs huge compute power, dedicated security teams, proper UX research, compliance across many countries, and support teams that only make sense with hundreds, sometimes over a thousand people. AI tools do not remove that need. They only make the coding part faster.
Where AI actually helps a lot is at a smaller scale. A solo person or a tiny team can now build a mobile app, a browser extension, or a simple website in a fraction of the time it took before. Design work is mostly handled by AI now, and coding is much faster too. Security, integrations, and scaling still need real skill, but even that gap is shrinking every month. I have already seen reports of people running fully automated pipelines to take an idea from start to launch with almost no manual work.
So both things are true together. AI tools do help a new player build something fast and even find some early users. But speed of building is only one part of competing at scale. Big tech has money, compute, distribution, and an existing user base, advantages that take years to build and that a new entrant cannot recreate just by building fast. So when a small team launches something useful using AI, big tech is usually well placed to build something similar using the same tools, or to bring the idea in through a partnership or acquisition, the same way Facebook did with WhatsApp and Instagram. The new entrant gets a genuine head start, but big tech has the resources to catch up quickly if the idea proves itself. That is really the point I was making with the Salesforce example. Small players can absolutely build things faster than ever now, but scale and resources still end up deciding who leads the market.
The Bigger Pattern, Everything Slowly Becoming One
This is why the overall direction tends to stay the same, whether a market is being built by a fresh startup, a small AI powered team, or a giant like Facebook or Microsoft. Big tech generally has the resources to compete for the long run and, in many cases, to bring in or absorb whoever comes up with something new. It happened with search, with social media, with messaging apps, and it is now beginning to happen with the small tools AI has made possible. The players change, but the pattern tends to repeat.
The same pattern is playing out well beyond social media and AI, quietly, for years now.
Local clothing shops, kirana stores, hardware shops, absorbed into Amazon and Flipkart. Coaching centres, tuition classes, colleges, absorbed into Udemy, Coursera, LinkedIn Learning, and now AI tools. Friends meeting in person, community gatherings, absorbed into Google Meet and Zoom calls.
None of these changes got the kind of attention AI is getting today, even though the same thing is happening, offline life slowly moving into a few big digital platforms. AI is not something new in that sense. It is just the latest and loudest chapter in something that has been happening for a long time.
Put all these examples together, Windows, Facebook, WhatsApp, Instagram, kirana stores, coaching centres, and now AI, and the direction is clear. Whoever gets to scale first ends up winning. And winning never means stopping. It means absorbing whatever comes next, until entire parts of daily life live inside one place.
AI is simply the fastest version of this pattern the industry has ever seen. The same handful of companies racing to launch first are also the ones building the infrastructure, the compute, the models, that everything else will eventually depend on. What used to take a whole decade to consolidate is now happening in just a few years.
The small tools and apps people use individually today for work, learning, and daily decisions are the next layer this pattern is moving into. Just like kirana stores folded into Amazon and coaching centres folded into Udemy, these small individual tools will likely fold into a handful of big AI platforms. Not because those platforms are always better, but because that is exactly what has happened at every stage of this industry so far, and there is no clear reason this stage will end any differently.
That is the real pattern here. It started with local shops and coaching centres. It moved through operating systems and social media. It is now reaching the small tools sitting on everyone’s phone and laptop, and this time, it is moving faster than any wave before it. Every era has had its own version of this story, and every era has ended the same way, with everything eventually becoming one platform.
🧠 Human driven, AI assisted. The perspectives shared in this newsletter are my own, refined and structured with the help of Large Language Models.