Earlier this year, I had a weird conversation with a Saas Founder. He was not asking what the best AI writing tool is or what is the best AI summarizer. An even bigger question was asked by him: “Who owns the future five years from now if I build my business on one AI ecosystem today?”
I have been pondering on that question as it is the essence of what I feel many people get wrong. When discussing AI, it can all too easily be centred around product comparisons. ChatGPT versus Gemini. Claude versus ChatGPT. Benchmarks. Leaderboards. Viral demos.
However, the true contest is more a contest of the prettiest paragraph than it is about who makes money.
It’s a battle over the control of the infrastructure, developer ecosystem, enterprise trust and distribution layer of artificial intelligence. People are typically looking for OpenAI vs Google vs Anthropic to get an idea of who’s up in the rankings at this time. I believe this the incorrect question.
The question to ask is:
So, who is developing an ecosystem that will drive all business, creation, software companies and ultimately even governments in the decade ahead?
The answer is not as simple as the headlines indicate.
Everyone assumes the smartest model wins. That is probably wrong.
One common pitfall I see several times with founders and down to enterprise IT leaders is that they think that the leader of AI is determined by the sheer power of the model.
I understand why.
If one model is more successful in reasoning / coding, it makes sense to assume that Company wins.
I too had the impression that this was true.
Then I had the experience of working with a medium-sized B2B client that got taken aback by the appealing looking benchmark chart and started to implement internal processes with one AI service. Six months later, they found themselves on an uncomfortable journey trying to migrate due to integration problems, the unpredictable price and lack of enterprise controls.
The model that was the smartest, did not win. It was the ecosystem that was important. This time, History has proved us all wrong. Google wouldn’t be what it is without its search quality. Distribution did.
The mere fact that Excel was more useful to enterprise software than its competitors did not win the hearts of MS. However, simply being more useful than the competition was not enough to make Excel the leader in enterprise software. Enterprise integration mattered.
Amazon was not the sexiest tech company, so cloud infrastructure didn’t go mainstream when Amazon made the leap. With the arrival of execution and developer adoption, all that changed. The trend is expected to be similar for AI.
What each company is actually trying to build
The easiest way to understand this race is to stop thinking about chatbots. Think about ecosystems.
OpenAI wants to become the operating system for AI
OpenAI has made an extraordinary thing. Made generative AI a common practice.
Prior to ChatGPT’s launch, very few, outside of the tech world, spoke about large language models. Once launched usage grew so rapidly it was necessary for the universities, enterprises and governments to react almost in a state of emergency.
The company’s tie-up with Microsoft proved an enormous blessing for the company. The ability to access infrastructure that can scale and is accessible to the corporate market is something that startups like OpenAI do not get to enjoy.
Many enterprise apps are today used and rely on OpenAI APIs in a hidden manner. GPT models seem to have a mature ecosystem and are accessible, which is why developers are creating products on top of them.
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The application of GPT for automating document review in the real world is exemplified by fintech and legal tech companies that turned to GPT automation to decrease the manual effort needed for document review. In a number of deployments that I heard about indirectly via consulting circles, teams were able to drastically cut down on repetitive knowledge work, but still had to manage hallucination.
It’s the last part that’s important. OpenAI is frequently in the fast lane sometimes at an uncomfortable speed. The company is “fast” and as such, innovating. It’s also written in a way to foster trust questions.
In the fall of 2023, when leadership crisis came out in the open, many enterprise leaders asked but whispered,
Is a company still startup chaotic, that we can rely on?
This worry hasn’t gone away.
Why Google might still be the strongest long-term player
The very thing lots of people don’t like to be told is the following:
In public opinion, Google is lagging but it is well on its way to taking over. This seems counter-intuitive, as there is mindshare to be taken and OpenAI owns it. However, there is still more to distribution than hype. The products are used by billions of people and Google controls them.
Search. Android. Chrome. Workspace. YouTube. Cloud infrastructure. Advertising. Data systems. In the history of companies few have more ecosystem leverage than this.
Tackling the challenge of how an AI-powered search engine becomes ubiquitous in the lives of billions of people requires Google to address the following challenges, If Google is going to solve the problem of making an AI-powered search engine ubiquitous in the daily lives of billions of people, it needs to tackle the above challenges simultaneously.
No additional software is needed to be downloaded. No changes in behaviour required. That is powerful.
Generative Ai Services
Google has been announcing publicly that enterprise demand for generative AI services delivered within Google Cloud has increased drastically since Google announced the new product as it’s becoming a business preference to have embedded AI solutions.
This has been my own experience I have worked with teams using marketing tools already so much on Google Workspace that are struggling with the challenge. It’s much simpler to convince them to play around with Gemini integrations in tools that they already use, rather than creating a new ecosystem.
However, there is one issue Google has. Execution inconsistency. The company could’ve been singeled out for being extremely cautious, even as it developed some of the key transformer technology that was essential for the development of modern generative AI.
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I know a lot of practitioners who were like, “Why isn’t Google doing something, but those guys are getting attention. Many practitioners I know were really aggravated when Google was wavering, and other guys were getting attention. But, even with the best research, it is not a given that the market will be won.
Expert Insight
Demis Hassabis, CEO of DeepMind, has repeatedly argued that progress in AI requires balancing rapid innovation with responsible deployment. That philosophy explains why Google often appears slower than competitors.
Whether patience becomes a strength or weakness remains an open question.
Anthropic is quietly becoming the company enterprises trust
Let’s now get back to the company that many readers don’t take into account. Anthropic. When fast and ambitious is OpenAI’s verb, and massive and strategic is Google’s, it is deliberate for Anthropic.
It’s a difference that is more than people think.
Claude has earned a reputation amongst developers and enterprise teams for its reliability, extended context size and safer output.
I am constantly getting the same statement from tech founders:
“Claude feels calmer.”
That’s all well and good until you start implementing these systems in full use. Many professionals actually prefer Claude over longer document analysis, nuanced writing or workflow that requires safety.
Amazon has also backlined Anthropic and Google has been strong on the investment front. That bi-faceted role entails the company having a unique level of flexibility.
Amazon has also added Claude to Bedrock to enable enterprise customers to access AI models more safely via AWS services. This is significant because business is concerned with governance – it’s almost as important as capability.
“The company that wins AI may not be the one with the smartest model. It may be the one enterprises trust enough to build around.”
Trust is boring. Trust is also expensive to earn. And incredibly profitable once secured.
A real-world case study that changed my thinking
One retail analytics firm I’ve been closely watching over a period of enterprise AI transition, was initially based on the OpenAI APIs. The reasoning seemed a no-brainer. Best-known model. Strong developer community. Fast innovation.
But problems emerged. Costs fluctuated. There were concerns about data governance from within the compliance teams. The need to depend on vendors for a long time came up at the board level. The company later decided to do more than just rely on one provider, opting for a multi-model approach.
OpenAI was responsible for the automation that was used by customers. Internal documentation process supported by anthropic. Google tools have been connected with productivity tasks via Workspace.
The surprising outcome?
Performance got better as teams realized that it wasn’t a race for wins and they were able to restrain from thinking of AI as a getting them all game. So, that was an eye-opening experience for me, and I applied that to my thinking around this market. Perhaps there will be no winner! Perhaps the dawn of an AI oligopoly is just starting.
The strange advantage nobody talks about enough
The part that most headlines miss is the following. Access to computing power could be the key to the future of AI, rather than intelligence. It is very expensive to train frontier Models.
We are talking big numbers in the billions. As compute is strategic, the money kept getting spent on infrastructure, with companies such as Microsoft, Google, Amazon and Meta ramping up their investments.
As AI has shown to be a competitive necessity and not an experimental expenditure, organisations are adopting generative AI more and more, according to McKinsey research into generative AI adoption. It’s a turn that changes all that.
It has a significant disadvantage for smaller, AI labs that do not have infrastructure partners. Part of the reason for Anthropic’s collaboration with Amazon is this. And why it is so important that Microsoft has a relationship with OpenAI. AI isn’t all about software anymore.
It’s an infrastructure that requires a lot of capital. That forms a barrier which most competitors are not able to cross.
So who actually shapes the future?
My straight-forward opinion after following this market for a while. The conversation is driven by OpenAI. Google shapes distribution. Anthropic shapes trust. There is something special about each company. OpenAI is currently in a strong position if you’re concerned about what consumers are going to do or developers are going to excited about.
For those who want to reach as much as ecosystem as possible, and integrate the product in everyday use, Google might still have the potential to reach the highest ceiling. When enterprise safety and reliable adoption matter – it’s becoming impossible to not pay attention to Anthropic. Nevertheless, I believe it is an understatement to say how fast things change in the technology sector.
When social media was in the phase where it was believed to always be a Facebook thing?
Or would cloud computing have been solely the domain of Amazon? Markets change. Fast.
What should businesses actually do this week?
Set aside predictions, for now. If your company is considering using AI on a serious level, then do this! Test each and every ecosystem using one real workflow. Not a demo. No showy indicator. Choose one business process that you repeat that costs money.
Customer support. Research. Internal documentation. Lead qualification. Contract review. Test the small difference between OpenAI, Google and Anthropic for 14 days.
Measure output quality. Track cost. Watch reliability. Take into account compliance requirements. That one exercise will be more useful to you than six months of reading opinion pieces on the internet. Including this one.
OpenAI vs Google vs Anthropic the uncomfortable reality
When people ask, “OpenAI vs Google vs Anthropic,” they typically want to know the answer. It’s unlikely that there is one. AI could turn out to be more much like cloud computing than social media. Various leaders in levels.
OpenAI could rule interfaces and the innovation of developers. Google can have distribution and embedded AI experiences. The enterprise layer that is trusted by anthropic may provide sensitive work.
The sad truth is that no one can say with any sort of confidence. Or perhaps that’s the best indication that we’re still early. 5 years from now we may all smile and wonder how sure we were of one winner.
Or we might discover that the future is for business entities able to collaborate rather than compete so as to eliminate one another.
Which of the possibilities seems more realistic to you?
Expert Insight
Andrew Ng, AI entrepreneur and founder of DeepLearning.AI, has consistently argued that practical AI adoption matters more than chasing hype cycles. His position aligns closely with what businesses are learning now, execution beats excitement.
Source: DeepLearning. AI and public interviews
Author Bio
Talha Qureshi is an enterprise technology analyst and blogger with hands on experience across cybersecurity, cloud infrastructure, B2B SaaS, Tech News and enterprise AI. He writes about the gap between how enterprise technology is marketed and how it actually performs in real organizational environments.
