Reading Time: 21 minutes -

Editor’s Note [September 16th 2024]: This article was originally published in March 2023 and has undergone multiple updates since. On March 3rd 2023, we published our original article discussing the AI inflection point introduced by OpenAI’s ChatGPT. Only days later, analysts at Morgan Stanley also reported that AI was at an inflection point with a “$6 trillion dollar addressable market in AI”, which we covered on May 19th, 2023.

Since our very last update in July 2023—covering the release of Google’s Bard AI with connectivity to the internet, GPT-4 with web browsing capabilities, ChatGPT plugins and Anthropic’s Claude AI—major breakthroughs have continued to accelerate, including the role of open source in AI. Fast-forward to today, more than 12 months later, and today’s update is more significant the the others and potentially more so than even the original article.

Four days ago, we got news of a new model called OpenAI o1 and o1-preview, codenamed Strawberry. The launch of OpenAI’s “o1” series could represent yet another AI inflection point—one of several we’ve seen in recent years—and perhaps the most profound shift in AI yet. This follows rumours of OpenAI achieving AGI internally with a project called Q-star (Q*), which we cover in more detail here. Today’s updates reflect these changes and will explore whether this signifies another significant AI inflection point, an AI tipping point in terms of new adoption, or even the largest inflection point we’ve seen for AI so far.

We only usually appreciate the significance of tipping points in hindsight

It’s true that when significant technological breakthroughs happen liketipping points and inflection points, we only usually appreciate their significance in hindsight.

An example that comes to mind is the initial release of the iPhone which transformed what a phone actually was, and gave rise to the modern ‘smartphone’. I can’t help but wonder whether we are seeing a similar thing right now with ChatGPT being an inflection point for AI. More than this, it’s not too far-fetched to believe we may be at a tipping point with regard to our relationship with technology, one to rival and even surpass that of the Internet.

Some context and history regarding breakthroughs and tipping points 

The advent of the Internet in 1983

Firstly, It is important to remember the time when the Internet (as we know it today) was created. Although this wouldn’t have been known at the time, 1983 was the first major inflection point leading to where we are today. At the time, it was just a primitive simple network used to transfer text locally between a few computers. TCP/IP was created with the intention to create a communications protocol that allowed different types of computers to communicate with each other.

Never in your wildest dreams would you have predicted that this would eventually evolve into what we have today with 4KHD movies being played from the palm of your hand, and eCommerce becoming an essential part of everyday life. It has transformed the way we communicate, access information, conduct business, and has fundamentally changed the way we interact with the world. But back in 1983,

In 1995, Bill Gates was interviewed on the Letterman show where he was quizzed on the usefulness and functionality of the Internet. The concept was mocked and dismissed by many people that felt it was pointless, and even a waste of time. Even mentioning the Internet in the interview resulted in laughs from most of the audience. It’s a shame as most people tend to be somewhat myopic when it comes to technology and unable to look at the big picture.

A nod to the IBM PC in 1981

Of course, the introduction of the first Personal Computer, the IBM PC in 1981 (Model 5150), marked the first major shift in the way people interacted with technology. For the first time in history, people could afford to use computing power from the comfort of their own homes. However, with a starting price of $1,565 USD (roughly $4,370 USD in today’s money after inflation has been factored in), the PC was still a luxury for many.

Today, of course, we can purchase high-powered PCs for a small fraction of the equivalent cost back in 1981. Without the introduction of the PC back in 1981 and reduced costs due to advancements in semiconductor manufacturing, the Internet as it is today wouldn’t have been possible.

Apple’s iPhone in 2007

Mobile phones have become a significant part of our lives over the last few decades, but Apple’s iPhone can be considered another inflection point in terms of how we consume information and communicate using technology. Released in 2007, the iPhone changed the game for mobile phones and the technology industry as a whole. More than just a mobile phone, the iPhone combined several technologies into a single device. It was a mini-computer that could fit in your pocket, allowing access to media and information anywhere.

The impact of the iPhone on the mobile phone industry can’t be underestimated as it led to the rapid adoption of smartphones around the world. With the rise of messaging apps like iMessage, WhatsApp, Facebook Messenger, and social media platforms, we can now stay in touch with friends and family across the world instantly, at any time, and from anywhere. Without the iPhone it’s hard to imagine all of this happening.

Accidental discoveries

Many breakthroughs and innovations result from something that seems somewhat insignificant or unintended at the time. This is why curiosity-driven science is so important.

In 1820, Hans Christian Oersted made the surprising discovery that an electric current creates a magnetic field. This wasn’t intended and was entirely by accident. He certainly didn’t set out to do this – quite the opposite. He made the discovery while trying to prove to a class that electricity and magnetism were not related.

Present day, we are still making similar discoveries by accident when pursuing a seemingly unrelated experiment. Many people have been critical of projects such as the CERN’s LHC, feeling as if money and investment would be better spent in other areas. This is due to not understanding that as a result of this science, we are seeing the benefits in unexpected areas such as improvements to medical treatments.

What is an inflection point? 

An inflection point is a term used to describe a point in time or situation in which a significant change occurs, indicating a turning point, transition, or a new phase of growth or decline. This is often shown as a point on a graph where the curvature changes direction, indicating a significant shift in the underlying trend.

A turning point is another phrase used to describe this kind of change.

Disruptive technologies

Disruptive technologies are innovations that completely change the way we do things. They shake up established industries and shatter the status quo by introducing new ways of doing business or providing products and services. Think of them as game-changers or catalysts for innovation that can transform the world around us.

In technology, inflection points are often associated with a major breakthrough or the emergence of disruptive technologies that fundamentally alter the competitive landscape and create new opportunities or threats.

We’ve already mentioned the Internet, PC, Apple iPhone and electricity but other examples of existing or emergent disruptive technologies include:

1. Gun powder
2. The jet engine and flight
3. Nuclear fission
4. X-Rays
5. Microchips and microprocessors
6. Virtual Reality
7. Augmented Reality
8. 5G
9. E-Commerce
10. Blockchain
11. Cryptocurrency
12. Gene editing
13. 3D printing
14. AI and Machine Learning
15. Nanotechnology
16. Quantum computing

In almost all cases, they are cheaper, faster, and more efficient than the technologies they replace, and they often appeal to a new or under-served market.

We’re here to talk mainly about AI but it’s important to consider this within the context of other disruptive technologies, those that came before or are still yet to come.

The difference between an inflection and a tipping point? 

Inflection points and tipping points are both critical moments of change, but they differ in their nature and implications.

Whereas an inflection point represents a turning point, a tipping point represents a point of no return, where a behaviour abruptly shifts to a new state. Quite often this can be a small change or event that can cause a larger, more significant change to occur. Using technology as an example, a tipping point could be a critical mass of users or adoption that leads to exponential growth or widespread adoption of a technology. Examples of this are the mass adoption of social media (Facebook) or e-commerce (Amazon).

Is ChatGPT an AI inflection point or something else?

Well, some people could dismiss the recent improvements seen in AI as a gimmick, hype or buzz but I certainly don’t think so.

When you’re so close to something that has just been released, ChatGPT for example, it’s hard to imagine the far-reaching implications of the technology. It’s also hard to predict the advancements that could be made in other areas as a result of experimenting with the technology. For example, the changes in store for AI and Natural Language Processing (NLP) could surprise us and this could lead to unexpected innovations and improvements across multiple industries.

So is ChatGPT an inflection point for AI? Yes, absolutely. If you think back to the graph above and imagine it showing the use and acceptance of AI, ChatGPT is a turning point for AI like no other. However, it’s also a disruptive technology and it’s fairly safe to say that we’ve seen a tipping point in the adoption of AI.

The following shows an interesting chart from Gartner, a Hype Cycle mapping expectations of AI to time, through to 2022. What’s most interesting here is that this is all pre-ChatGPT and I expect the picture would look entirely different today.

ChatGPT is the fastest-growing application – ever! The numbers involving ChatGPT’s usage are staggering and grow constantly — keep in mind that ChatGPT is still in its testing/beta stages too. As you can see in the below image, ChatGPT reached 100 million monthly users in just 2 months. Another thing to keep in mind is that this is before Bing fully integrated and released its own ChatGPT integration! More on this later.

Even Bill Gates has been on record saying that AI is the “most important” innovation at the moment, continuing to say:

“This will change our world”

A search on Google Trends shows another interesting perspective. I wanted to try and gauge the level of general interest in AI compared to another disruptive technology. I chose VR as this one is also close to my heart. The chart below shows the relative interest in both AI and VR from 2015 onwards. We can see both are fairly level until November 2022 when the interest in AI changed direction and grew significantly. Obviously, this was due to ChatGPT, which has also been plotted to show the correlation.

This looks very much like an inflection point. Even Morgan Stanley seems to think so. Four days after publishing our post, the following video popped up on YouTube:

The potential of AI

Not even a day can pass without headlines appearing such as “ChatGPT AI passes test designed to show theory of mind in children”. This shows how sophisticated AI, particularly ChatGPT, has become. It’s easy to get caught up in the ‘wow factor’ in regards to ChatGPT, but I don’t think many people truly understand how far-reaching and life-changing AI is becoming. Here are just a few potential scenarios that modern sophisticated AI could improve.

• Medical analysis and advice that is personalised based on patient history potentially giving a higher success rate of medicines and operations.
• Video game NPCs (non-player characters) don’t just read pre-determined dialogue, they operate within the confines of a set of parameters specific to that character.
• Enhance the classroom experience by acting as an assistant, supplementing the tutor’s lessons by providing nuanced information based on specialised topics.
• Programming problems can be quickly fixed with the use of ChatGPT with users being able to ask specific programming questions and get accurate answers 24/7.
• Being able to potentially automate admin tasks in more efficient ways so that companies can become more productive.

It is important to remember, this technology will continue to improve and develop. OpenAI has already hinted at the possibility of ChatGPT 4 being multimodal meaning it won’t be limited to just text. The groundwork has been laid and has been for some time, resulting in the functionality we see with transformer-based architecture and NLP (natural language processing) improvements.

In order to gain a better understanding of how integral ChatGPT might become, let’s look to the past at another innovative and ‘game-changing’ service, Google Translate. Google Translate has transformed the way we interact with others all over the world since its release in 2006.

Although it’s not as much of a game-changer as ChatGPT, it illustrates that these types of things can drastically transform our lives. It’s interesting to hear responses from people when asking them about AI and ChatGPT currently. It seems as if only techies and those with an interest in technology are excited about it. In an effort to understand more about what others think about the future and whether we’re at a tipping point, I decided to ask ChatGPT about it.

“The cultural tipping point that we may be approaching is not just about the technology itself, but also about the choices we make as a society.”

— ChatGPT

It’s actually a good point, it can be somewhat naive to only think about the benefits if you are invested in the subject matter as an enthusiast. However, AI, in its current form at least, can be used as a tool for many different purposes. Like all tools, they can be misused. The power ultimately lies with us. Success doesn’t just rely on our technological innovation and progress, but also on our ability to use it responsibly going forwards.

Basically, what I’m saying is, with great power comes great responsibility.

How does the integration with Bing impact things?

Microsoft’s big purchase seems to be paying off. Since ChatGPT integration was announced, Bing’s app has seen a surge in popularity receiving over 10 times more downloads than usual with over a million users signing up for early access to Bing’s new ChatGPT-integrated search.

Rumours are that ChatGPT is receiving up to 10 million queries per day resulting in an operating cost of over $100,000 per day.

Testing out the new AI-enabled Bing

While writing this article I managed to get early access to the ‘new’ Bing. My first impression was that I was somewhat underwhelmed. It feels like Microsoft doesn’t fully understand what to do with the functionality and were trying to shoehorn search into the functionality rather than using search capability to back a ChatGPT-like AI.

What I mean by that is, it is still trying to operate as a conventional search engine and doesn’t fully embrace the possibilities that are available through AI.

I can understand that they want to be cautious and revamping their search takes time, but my impressions from early testing are mostly that of disappointment.

Somehow it feels less impressive when you see the ‘searching for answers’. Of course, it’s going to primarily use search to generate answers and then integrate that into an intelligent reply, but at the moment it doesn’t feel like this is it.

It’s still early access, and I’m sure things will improve. I agree with the idea of backing up generated statistics and information with appropriate sources. I just think that Microsoft needs to be careful not to let conventional search limit and get in the way of the intelligent nature and potential of the AI.

I played around with the features a bit more trying to get a feel for what the limitations are with Bing’s AI and it clearly didn’t understand my joke.

Users on the subreddit /r/ChatGPT have also been sharing their frustration and disappointment with Bing’s newly integrated AI.

“You can pretty much feel all the locks and chains put into place to make it “safe for advertisers”. They want it to sound as blend and “safe” as possible so companies wouldn’t be “scared” by the possibility of weird response patterns”

— User ‘Phitos2008’

“I got access today and it seems almost useless. Ends the conversation like this for everything except the most basic stuff you could just Google. Even when you do get somewhere with it, that instance is closed after like 5 or 6 messages anyway.”

— User ‘-MrLizard-’

How the AI landscape changed in 2023 [September 16th 2024]

The AI landscape (in particular generative AI) underwent significant changes in 2023. We saw major advancements in technology and the release of innovative products, which further enforced the notion that we were at an AI inflection point.

Summar of the key advancements:

Google’s Bard AI: internet connectivity unleashed

Google introduced Bard AI. Much like Bing, Bard AI is equipped with direct internet connectivity, enabling it to access and analyse real-time data from various sources. This revolutionary feature has opened up new possibilities in areas such as data analysis, information retrieval, and decision-making, as Bard AI can not only comprehend the vast amount of information available online but also provide accurate and up-to-date insights.

OpenAI’s GPT-4: web browsing capabilities and ChatGPT plugins

OpenAI released the highly anticipated GPT-4, the latest iteration of its groundbreaking Generative Pre-trained Transformer model. ChatGPT Plus users got web browsing capabilities combined with GPT-4, making it an even more powerful and versatile tool. OpenAI has also introduced ChatGPT plugins, which provided developers with the means to integrate ChatGPT capabilities into their own applications. These plugins have paved the way for a new generation of AI-powered apps that can effectively communicate and collaborate with human users.

Say hello to Anthropic’s Claude AI

If OpenAI’s ChatGPT, Bing and Bard weren’t enough, we were also given Anthropic, another major player in the AI industry. Anthropic opened up access to its impressive Claude AI. This state-of-the-art AI system is designed to learn, reason, and adapt to new information, enabling it to provide valuable insights and predictions in various domains. With its advanced capabilities, Claude AI was claimed to be capable of revolutionising industries such as healthcare, finance, and education, among others.

2024: OpenAI o1, codenamed Strawberry [September 16th 2024]

Why resetting to “o1” matters

First, let’s address the elephant in the room: why has OpenAI reset the naming scheme back to “o1” rather than launch GPT-5?

This decision is more than just a branding exercise; it reflects the significant leap this model represents over previous iterations like GPT-4. The reset signifies a new beginning, where the focus shifts from scaling models up (in terms of size and token limits) to enhancing their depth of reasoning, making the model not just bigger, but smarter.

OpenAI’s move essentially acknowledges that the leap in cognitive ability between GPT-4 and “o1” is so substantial that continuing the “GPT-x” naming convention would underplay the significance of these new capabilities.

What makes “o1” fundamentally different?

While previous models in the GPT series excelled at natural language generation, they struggled with more complex reasoning tasks—especially those requiring multi-step problem-solving. The “o1” model has been designed to address these weaknesses. Here’s what sets it apart:

• Chain of thought reasoning: Unlike GPT-4, which often generates answers quickly but sometimes superficially, “o1” is trained to think in a structured, step-by-step manner. It reasons through problems, evaluating and re-evaluating different strategies before arriving at an answer. This mimics how humans tackle complex tasks, allowing it to excel in subjects like math, physics, and even coding—areas where prior models often faltered.
• Reinforcement learning at inference time: Most AI models learn during training but are relatively static during inference (the actual interaction phase). “o1” flips this dynamic. It uses reinforcement learning during inference, meaning it continues to “think” while solving a problem, improving its output quality in real time. This is a huge step forward for AI reasoning, making “o1” capable of adapting to new information as it processes a task.
• Error recognition and self-correction: Another game-changing feature is the model’s ability to detect and correct its own mistakes. Through its more reflective process, “o1” is designed to spot when it’s going off track and adjust accordingly. This level of self-awareness was largely absent in earlier versions, where errors often went uncorrected unless explicitly pointed out by the user.
• PhD-level benchmark performance: On a range of advanced academic benchmarks, “o1” outperforms its predecessors by a wide margin. It scored 83% on international-level math problems—where GPT-4 only managed 13%—and reached the 89th percentile in coding competitions. The gap between the two models is staggering, especially in tasks requiring deep reasoning and advanced knowledge, like those faced by PhD students. You can read more about this in their research post.

A shift in focus: Why GPT-5 may never arrive

With the launch of “o1,” the question arises: what about GPT-5? For months, the AI community anticipated that OpenAI’s next big step would be GPT-5, but the debut of “o1” suggests a shift in priorities. Rather than focusing on scaling the size of models (as we’ve seen with the GPT series), OpenAI appears to be concentrating on making models think better, not just faster. This suggests that GPT-5, at least in the form we imagined, may not materialise—or if it does, it might look very different from its predecessors.

The “o1” series marks a new phase in AI development, one that focuses on reasoning capabilities rather than mere data processing speed or language fluency. This new model family could eventually replace the expected GPT-5, serving as the foundation for a more reflective, human-like AI system.

What is this? Another inflection point, an AI tipping point, or the largest inflection point we’ve seen so far?

When major breakthroughs like this happen, we always end up asking the same old questions—is this yet another significant AI inflection point, an AI tipping point in terms of new adoption, or even the largest inflection point we’ve seen for AI so far?

Given these advancements, it’s tempting to declare that we are finally at the true AI inflection point. After all, the “o1” model not only pushes the boundaries of reasoning and problem-solving, but it also represents a more generalised form of intelligence—a key stepping stone towards AGI.

When considering its remarkable leap in academic benchmarks, especially in fields like mathematics and physics, one could argue the case that this is the real AI inflection point. It’s no longer just about making AI better at generating text, but about building AI that can think and reason in ways that were previously reserved for human experts. The ‘o1’ model represents a quantum leap in reasoning and problem-solving, fundamentally changing the capabilities of AI to handle complex tasks with unprecedented accuracy and depth.

Others could argue that the release of ChatGPT was more important, or that this is just one of many inflection points expected within the journey of AI. The claim could also be made that potentially the most significant inflection point hasn’t even happened—AGI.

And while all of the above make sense, it is also a major tipping point, therefore, signalling a future where AI is no longer just a tool, but an active participant in solving the world’s most complex problems. Following the technological inflection point with the ’01’ model, we may soon witness the moment when AI’s integration into industries becomes irreversible.

The Q-Star (Q*) and AGI connection

Many believe that “o1” might incorporate elements from OpenAI’s secretive Q-Star project, a research initiative reportedly focused on advanced reasoning capabilities, particularly in the domain of mathematical problem-solving.

The sharp improvement in “o1’s” math performance—surpassing human PhD-level accuracy—suggests that Q-Star’s breakthroughs may be embedded in this new model. If Q-Star is indeed the foundation for “o1,” this could signify that OpenAI is working on fundamentally new methods for reasoning and cognition, possibly laying the groundwork for AGI.

The mini model: a cheaper, faster alternative

In tandem with the “o1 Preview” model currently available in ChatGPT, OpenAI also mentioned “o1 Mini,” a smaller, faster, and cheaper version of the model. While “o1 Mini” may lack the full reasoning power of its larger counterpart, it offers developers a more accessible option, especially for tasks that don’t require PhD-level thinking. This parallel launch indicates that OpenAI is preparing for broader adoption of its models across industries, making sure that cost and speed don’t limit the use of these groundbreaking tools.

Other important questions to consider

How far-reaching could ChatGPT’s impact be?

As touched on earlier, ChatGPT could have far-reaching implications impacting multiple industries. Popular YouTuber Tom Scott definitely thinks so, which he covers in the video below and is worth watching:

Could AI be as disruptive as Napster was to the music industry in the early 2000s?

It definitely feels like this type of AI has the potential to make a lot of content writers redundant. Of course, not all, but a huge number could be impacted. The same goes for a lot of admin tasks that could be semi-automated and reliably managed by AI in the coming decade.

Programming could be improved greatly by tools such as ChatGPT, which scans code for problems and optimisations, and then suggests them to the user. This isn’t far-fetched in the slightest as ChatGPT has already fixed a huge amount of coding issues for many users.

Is it completely flawless in finding and fixing bugs in code? No, but it’s 80% there in my opinion and as this is a technical preview of beta code, it shows huge potential. It is important to keep in mind that once ChatGPT is fully released, it will constantly be improved and expanded upon too.

How can OpenAI make money?

With all of this expense, it makes sense that OpenAI would be considering their options when it comes to monetisation. Currently, there is a free-to-use version which is part of the ‘free research preview’, and OpenAI have recently started offering ChatGPT plus. Currently, OpenAI is looking to charge $20 a month for ChatGPT plus, which gets you the following benefits.

• Access to ChatGPT, even during peak times
• Faster response times
• Priority access to new features and improvements

There doesn’t currently seem to be much incentive to sign up, but once ChatGPT is out of its preview stage, it is not clear what functions will remain available to free users.

Only yesterday, it was announced that OpenAI was launching its new GPT-3.5 Turbo and Whisper AI Models, allowing a new faster and lower-cost API for developers to build their own apps and software tools.

How do modern-day robots fit into the landscape of AI?

It can be fun to think about how far away robot assistants are from existing in everyday life. We have the likes of Boston Dynamics which can make impressive robotics. We have OpenAI that can make the intelligence. We have also seen AI play a big part in product delivery and even drones in recent years

As for a human-robot assistant, we are still a ways off in terms of technology, and even when we’re at that point, it would be far from affordable.

It wouldn’t just be a case of having the intelligence, but also having potentially hundreds of sensors that are all able to work effectively together with a well-suited operating system. Things like proximity sensors being used together to create spatial awareness combined with a host of other complex hardware and software needs would need to be considered. We’re still not quite there, but we are getting much closer.

For anyone interested in not only the technological aspect of advanced AI and robotics, but the philosophical and ethical implications, the film Ex Machina is a great watch.

It’s interesting to wonder how ChatGPT’s type of intelligence would work in other areas. Even though ChatGPT is a natural language processing tool, the team at OpenAI have a number of impressive demonstrations under their belt when it comes to wowing with artificial intelligence.

A few years ago, OpenAI created a system called Rapid with the target of being able to beat a professional esports team at a Dota 2 game.

Dota 2 is a complex team game that requires strategy and team synergy. It has been described as being a cross between chess and American football. The point is, there’s a lot of complexity here in terms of the mechanics of the game, and the decision-making skills required.

The AI simulated 80 years’ worth of games against itself every day, learning via self-play to adapt and improve to the point where it could beat professional players. This was using a version of Proximal Policy Optimisation (PPO) and generated some mind-blowing results.

This shows that sophisticated AI isn’t currently just limited to natural language processing, there are other uses and applications using OpenAI’s technology and techniques. It’s easy to imagine a time where these types of AI can exist within a robot that can help with cleanup operations and other dangerous tasks.

In terms of the sci-fi vision of a home assistant, we’ll need a bit more time.

What about other companies with proprietary AIs?

The success of ChatGPT has galvanised and lit a fire under other companies that have assistants or AI products, with the most recent example being Google and its announcement of Bard in an attempt to catch up with ChatGPT and Microsoft’s plans for Bing.

At one point in time, Apple’s Siri was considered the best of the best when it came to personal AI assistants. Apple has arguably rested on their laurels for the last decade when it comes to advancements in this area.

Most of these top companies failed to see the importance of pursuing the development and evolution of their AI capabilities or at least they didn’t have it as a priority.

What role does open source have in terms of an AI inflection point?

It’s crucial to consider the role of open source models in this transformative journey and how open source is likely to influence the future of AI.

One prominent example in the AI landscape is LLAMA, which showcases the potential of open source AI models. These models, by their very nature, encourage collaboration, iterative improvement, and rapid innovation, characteristics that are key to propelling us further along this inflection curve. In fact, they could be game-changers, providing an accessible platform for AI development that transcends institutional boundaries, fostering a more inclusive AI community and accelerating the rate of AI advancements.

The influence of open source models on the AI inflection point can’t be understated. They could democratise AI, making it more accessible to the masses, and catalyse a paradigm shift in how we approach AI development. This revolution could lead to breakthroughs we can’t even conceive of today, much like the Internet’s exponential growth post-1983, which we have discussed above. As we continue to witness and participate in this AI inflection point, the role of open source models like LLAMA will undoubtedly be a critical aspect to watch.

Why is ChatGPT more impressive than AI assistants?

Although the concept of AI is nothing new, we are still significantly more impressed with ChatGPT than other forms of AI that came before. Why is this?

The likes of Siri and Google Assistant are still useful as they are very capable of handling the tasks they were designed to do. They provide accessibility options in addition to simplifying the process of communicating with others using smart devices. The difference with ChatGPT is the level of interactivity and ‘intelligence’, or at least its perceived intelligence.

There’s almost an eerie type of realism to ChatGPT both in terms of its accuracy, capability and conversational ability. Smaller and less-capable AI assistants will still have their place for more basic functions, but it’s with more complex tasks that ChatGPT shows how it’s leagues above every other artificial intelligence.

Moore’s Law and the growth of AI

Moore’s Law, a prediction made by Gordon Moore, co-founder of Intel in 1965, states that the number of transistors on a microchip will double approximately every two years, while the cost of computing decreases. This law has been generally accurate in predicting the exponential growth of computing power over the past several decades.

Moore’s Law is relevant because the evolution of AI and the current inflection point wouldn’t have been possible without the growth in computing power. This in turn has led to the development and availability of more advanced AI models, such as GPT.

With computing power growing exponentially, AI models like GPT will continue to evolve and become even more advanced, further revolutionising the field of AI.

Conclusion: the latest AI inflection point is here, and it’s called “o1”

From Bing to Bard, and Claude to GPT-4 with web browsing and plugins, these rapid advancements in the AI landscape during 2023 were a clear sign that the AI inflection point was here or fast approaching.

ChatGPT hit 100 million users in just 2 months, faster than any other app in history. Within months of the article first being published, we could access official ChatGPT plugins as well as OpenAI plugins for popular platforms like WordPress. From personal applications to productivity enhancements at work, generative AI marked a major tipping point and started to become a significant part of our daily lives in 2023.

With the launch of the “o1” series, we may very well be witnessing the true AI inflection point or at least one of the most important ones. This model’s ability to reason, self-correct, and apply deep learning to complex problems represents a significant departure from previous AI systems. OpenAI’s decision to reset the numbering is a symbolic gesture that marks the beginning of a new era in AI, one that is defined not by scale, but by depth.

Whether this is the final tipping point on the road to AGI remains to be seen, but one thing is clear: AI just got a whole lot smarter.

As we continue to track these developments, the question remains: if “o1” is already this advanced, what comes next? Are we about to enter an age where AI doesn’t just assist us but actively thinks alongside us? Are we closer to having physical robotic assistants living amongst us powered by models like “o1”? Time will tell, but the future of AI has never felt closer—or more exciting.

What are your thoughts about “o1” being an AI inflection point? Have you tried the new model? Let us know in the comments below.