OpenAI backed by Google has announced the release of a new generation of AI models, named OpenAI Pro and Claude 3.7 (the premium version with tools and “thinking” unlocked). These models represent a significant step forward in the evolution of AI technology, offering improved performance, creativity, and practical value. As someone who relies on AI for various tasks, from research and knowledge retrieval to sports analysis and daily decision-making, it’s immediately clear that OpenAI’s commitment to innovation and the development of superior models is greatly deliberated upon by its users.
Before diving into my first impressions, it’s important to acknowledge that AI performance is influenced by factors such as usage cases, dataset availability, and user expertise, among others. For instance, my reliance on AI for research questions, finding local sports information, or answering everyday inquiries are, for the most part, normal. However, the power and effectiveness of AI systems vary significantly depending on how they are tasked to perform. For example, while I’ve certainly used AI in professional settings—not driving a car, organizing a committee, or even analyzing business trends—most users encounter AI tools at its best when designing or solving significant, real-world problems.
Let’s dive into my first personal exploration of these models. Initially, I was intrigued by the capabilities of OpenAI’s models—being able to generate creative content, solve complex equations, and analyze text in ways that would be remarkable in the context of today’s rapidly evolving information landscape. However, as I became more familiar with the models, I began to realize the limitations and constraints that come with AI processing.
One of the key distinguishing features of OpenAI’s models is their ability to perform tasks that are more specific and user-centric, rather than general-purpose. For instance, when I asked the models for queries like, “Is there scientific merit to drinking alkaline water?” or “Are there lacrosse clubs around San Jose that have produced D1 commitments?” or “Request driving directions to a specific location,” the models responded in a thoughtful, intelligent, and nuanced manner. While they didn’t always provide a simple yes or no answer, their responses often included detailed explanations, context, and references to the content they were analyzing. This level of depth and specificity can be invaluable for users seeking to understand complex issues.
When approaching a more technical or detailed problem, such as generating mathematical proofs or solving intricate equations, the models performed exceptionally well. For example, when I asked OpenAI to solve complex calculus problems or equations that required intricate reasoning, the responses were precise, clear, and often provided alternative approaches or heuristics for solving the problem. In contrast, my personal interaction with AI to solve such equations might have been less insightful or deliberative.
However, as I progressed, I noticed that even experts on a problem or highly technical consumers of the platforms struck a balance between technical power and user utility. For instance, while OpenAI’s models might generate highly detailed solutions that could be useful for researchers or students, they are still merely substitutes for human expertise in many cases. This duality can be both a challenge and an opportunity, depending on the context of the user’s interaction.
One of the most striking aspects of OpenAI’s models is their ability to accelerate insights. When I sought quick answers to daily questions, such as procrastination about work-related tasks or stress-related issues, the models achieved results quickly and accurately. This feature is particularly useful for users looking to eliminate lengthy processes or quickly generate information for brainstorming or decision-making.
Another area of strength for OpenAI’s models is their adaptability to a wide range of applications. Whether a user is working with complex scientific data, analyzing large datasets, or engaging in creative projects like writing poetry or composing music, the models performed consistently well. This adaptability translates into versatility, as the same set of tools can be applied across various tasks, as long as they are sufficiently tailored to the problem at hand.
As my testing experience progressed, I began to observe how these models performed in varied scenarios. Though my primary users were academic and professional, I encountered unique situations where slight variations in the input, even minor details, could significantly impact the output. For example, when I asked each model how to prove the Pythagorean theorem, the responses were often distinct, but in most cases, they were driving the same ideas or alternative proofs.
In one, for instance, Claude 3.7 showed a slightly more methodical approach, whereas OpenAI’s models often offered highly original or creative solutions, though they still relied on well-established mathematical principles. This trade-off between generalization and innovation raises important questions about how users of these models traditionally influence their outputs. For some, the preference for simplicity or depth in responses can drive driving directions or detailed explanations, while others value original ideas or precise algorithms.
Interestingly, even as I improved my ability to utilize these models, I realized that there are still limitations. For tasks that require higher-level thinking or creativity, particularly in highly technical or highly creative fields, the models’ responses still often relied heavily on pre-defined algorithms or patterns. Without external guidance, these models most often produced outputs that were either too general or too focused on procedural steps, making creativity and, for the most part, unique thinking difficult.
When considering the future of AI, OpenAI’s models represent a tangible step forward, given their superior capabilities and the significant, practical challenges they can address. As more users adopt these models, the field of AI will continue to evolve, driven by the unique needs of the individuals and organizations it serves. For me, encountering the tools is not just an announcement of progress but a glimpse into the vitality and power of driving the future of human interaction and problem-solving.
In the next chapter of this journey, I look ahead to the potential of models like OpenAI’s Deep Research and Claude 3.7, and how they may shape, organize, and enhance our ability to move towards truly personal, interactive, and creatively driven experiences.
