Why OpenAI and Perplexity are Launching Browsers
And why it probably won't work
In the early 2010s, startups were popping up everywhere. With AWS for hosting, Web 2.0 frameworks for building, Stripe for billing and the App Store for distribution, it was easier than ever to build and scale web companies.
I was starting a company at the time and I remember one of the most common questions VCs would ask us founders was “Is this really a business, or just a feature?”
Are you getting early traction because you’re truly on to something, or just because a product team in Twitter or Salesforce or Adobe hasn’t got around to building this feature yet?
Getting this question wrong could be detrimental in both directions. Twitter disabled their API in 2018 and left a graveyard of companies like Twitterific, Ecofon and Tweetbot. On the other hand, Instagram was dismissed as just a photo sharing feature for Twitter, but now it’s many times as big.
“Featurization” was also a strategy that companies deployed at each other. Steve Jobs famously told the Dropbox founders that they were just a feature, before he launched iCloud Drive. (They’re now a $7bn company, but nowhere near as big as iCloud, so he was probably half right on that one).
Startups were also trying to do the same in reverse, have their new innovation become the new core application, so as to “featurize” the legacy incumbents. Instagram’s camera filters were their core innovation, which they then build out a social network on the back of. Canva, Figma and Google Docs all gained traction with collaboration as the new core-competency, with the image/design/doc editing as the secondary part.
This dynamic is starting to play out in the AI space too. Here’s some recent announcements:
From The Information on OpenAI:
The AI company is working on collaborative document editing and integrated chat capabilities, marking a strategic expansion beyond its core chatbot functionality. These new tools align with CEO Sam Altman’s vision of transforming ChatGPT into a lifelong personal assistant, including at work.
The planned features would resemble functions offered by Microsoft’s Office 365 and Google’s Workspace, two dominant software suites in business IT that have already incorporated generative AI tools into their platforms.
From WindowsCentral on Perplexity:
Popular AI answer engine Perplexity has announced that it's launching a new AI-powered web browser called "Comet" today.
"Comet transforms entire browsing sessions into single, seamless interactions, collapsing complex workflows into fluid conversations" says Perplexity CEO Aravind Srinivas. "Ask Comet to book a meeting or send an email, based on something you saw. Ask Comet to buy something you forgot. Ask Comet to brief you for your day."
From Reuters on OpenAI (again):
OpenAI is close to releasing an AI-powered web browser that will challenge Alphabet's market-dominating Google Chrome, three people familiar with the matter told Reuters.
The browser is slated to launch in the coming weeks, three of the people said, and aims to use artificial intelligence to fundamentally change how consumers browse the web. It will give OpenAI more direct access to a cornerstone of Google's success: user data.
From Venturebeat on Anthropic:
Anthropic announced Wednesday that it will transform its Claude AI assistant into a platform for creating interactive, shareable applications, marking a significant evolution from conversational chatbots toward functional software tools that users can build and distribute without coding knowledge.
The development represents a fundamental shift in how artificial intelligence interfaces with users, moving beyond static responses toward dynamic, interactive experiences that blur the lines between AI assistance and software development
The makers of foundation models (and Perplexity) very clearly have a view of the future which places their language model at the core of the new paradigm and everything else - web browsing, document editing, code editing - is just a commoditised feature.
Will that vision come true? To take our best guess, I think it’s worth nothing that there are three similar but separate bets being described in the stories above.
Anthropic as Coding Software
I think Anthropic’s is the most straightforward and has the highest chance of success. They have trained a large language model (Claude) and then fine-tuned it into models that are very, very good at writing code (Sonnet and Opus).
There are a range of other companies who believe that their coding application will remain core, with the coding-models as the commodity feature. These include incumbents like Microsoft’s Github Copilot, but also startups like Cursor and Windsurf. These companies believe that their secret sauce is in how they combine a commoditised code editor (IDE) with commoditised models (OpenAI, Anthropic, Gemini etc.)
This is a compelling proposition and one that I think will hold true if the models never get good enough to do 99% of the work unsupervised. Even if they get good enough to do, say, 95%, a human will still benefit from being in the driver seat and having a good application through which they can make edits, interventions, undo mistakes etc.
Anthropic are betting that their coding models will continue to improve so much that the user won’t need to make manual edits. It will be able to write and maintain code to such a high quality that the IDE can disappear into the background and the primary interface can be the chat messenger (or voice conversation).
This feels promising to me because the task of writing code has two properties that make it an ideal candidate to be performed by a model. The first is that code is versatile. There are several ways to write any given function that can work. Software is written in languages, after all, which makes them good fodder for pattern-generating language models.
The second property is that code writing can be verifiably correct. It either works or it doesn’t - which makes it more likely that models can be trained to improve at great speed and will likely get better with scaled usage. The more Anthropic does this, the better they become.
OpenAI as an Office Suite
Just as Anthropic have trained their language model to be good at coding, OpenAI have trained theirs to be good at conversational assistance (ChatGPT). Their bet here is that ChatGPT delivers such a strong value add in document generation that users won’t mind that its document editing applications aren’t very good.
The value proposition is reasonably compelling; the most difficult part of writing a Word Doc or a Powerpoint deck is often getting started. Starting at the blank page.
Microsoft have traditionally tried to help users overcome this cold-start problem with templates. It’s clear that describing what you want to ChatGPT is a superior experience for starting a word document, and not hard to imagine how the same will become true for presentations and spreadsheets in time.
It’s not clear, however, why Microsoft copilot or Google Gemini, integrated into their office suites, won’t deliver the better overall experience here.
I have very often created a doc in ChatGPT, then copy & pasted it into Google Docs, both because ChatGPT’s document editor is just not up to scratch (just try add or delete a row from a table), or because edits-through-conversation isn’t as quick or reliable as just making the edits myself.
I think we’re much further from the 99%-done-by-models here than we are with coding, and, if I had to bet on anyone getting a model there first, I would bet on Google over OpenAI. (OpenAI could have partnered with Microsoft on this, but it’s hard to see why Microsoft would do that now, if they’re making an Office competitor)
Given that, it’s hard to imagine OpenAI making office suite software that is good enough at the must-haves to make it an overall more compelling offering than Google Workplace + Gemini or Microsoft Office + Copilot.
I think that both Microsoft and Google will have strong positions here and, although ChatGPTs offering will undoubtedly get lots of usage, I don't see it as being fundamentally disruptive.
Perplexity & OpenAI Make Browsers
The third case, which I have the least hope for, is the plan to “featurize” the Browsers.
I can see what they’re going for. Both want to ultimately train models that are good at generic task completion. “Agentic” models that can book you a flight or a restaurant or to switch your phone provider. The implicit bet here is that Perplexity (who don’t even have their own foundation models) and OpenAI will take over from the user and do the browsing.
This seems unlikely to succeed for a number of reasons. The first is that it will be slow and error prone (which is my guess as to why Perplexity are waitlisting the rollout of their browser). The second is that few businesses are going to want a third party agent to access their website, so they’ll probably play whackamole trying to block them.
They are going to be squeezed from two sides. On the browser side, Google is well placed to integrate Gemini into Chrome and Microsoft to place Copilot into Edge. (Safari is the big and obvious gap here).
On the website side, it’s hard to imagine the generic browser agent would beat a native experience. Perplexity’s launch video gives two examples:
Asking it to summarise a thread on Reddit
Asking it to make a multi-step map on Google Maps
You’d imagine that both of these services will integrate their own language model capabilities, which should be decidedly more capable at those specific tasks than a generic one in the browser.
I don’t think either OpenAI or Perplexity are blind to this, so I read these moves as more of a data-gathering exercise than an ultimate product vision. If they own and control a browser, then they have visibility into where their agentic models succeed and where they fail when trying to complete tasks, see where users intervene and where they don’t.
This is also a strategic play from Perplexity who can build up some defensible moat by owning some of their own agentic logic, to hopefully commodities the language models they depend on.
It might not be the ideal user experience in the long run, but it could be a valuable way to scale the training of their agentic models in the mean time.