OpenAI's Vision: AI That Can Do Anything

The Development of AI Reasoning at OpenAI

Following Hunter Lightman’s arrival at OpenAI in 2022 as a research scientist, he observed the rapid launch of ChatGPT, a product experiencing unprecedented growth. Simultaneously, Lightman was engaged in a focused project with a team dedicated to enhancing OpenAI’s models in the area of high school mathematics competitions.

MathGen's Role in AI Advancement

This team, now known as MathGen, is currently recognized as a pivotal component of OpenAI’s leading initiatives in AI reasoning model creation. These models represent the fundamental technology driving AI agents capable of performing computer-based tasks with human-like proficiency.

Lightman explained to TechCrunch that the initial objective of MathGen was to improve the models’ aptitude for mathematical reasoning, an area where they initially demonstrated significant limitations.

Progress and Remaining Challenges

While OpenAI’s models are still undergoing refinement – with ongoing issues like occasional inaccuracies and difficulties with intricate tasks – substantial progress has been made in mathematical reasoning capabilities.

Notably, an OpenAI model recently achieved a gold medal at the International Mathematical Olympiad (IMO), a prestigious competition for exceptionally gifted high school students globally. OpenAI anticipates that these enhanced reasoning skills will extend to other disciplines, ultimately enabling the development of versatile, general-purpose agents.

From Accidental Success to Deliberate Strategy

ChatGPT emerged somewhat unexpectedly – originating as a research preview that quickly gained viral popularity – but OpenAI’s agent development represents a long-term, intentional undertaking within the organization.

During the 2023 developer conference, OpenAI CEO Sam Altman articulated a vision where users could simply request tasks from a computer, which would then autonomously execute them. He referred to these capabilities as “agents,” emphasizing their potential for significant benefits.

The Impact of o1 and Talent Acquisition

The release of OpenAI’s first AI reasoning model, o1, in the autumn of 2024, generated considerable surprise within the industry. Within less than a year, the 21 core researchers responsible for this breakthrough became highly coveted assets in Silicon Valley.

Meta, under the leadership of Mark Zuckerberg, successfully recruited five of the o1 researchers to join a new unit focused on superintelligence, offering compensation packages exceeding $100 million. Shengjia Zhao, one of these recruits, has since been appointed as the chief scientist of Meta Superintelligence Labs.

• Key takeaway: OpenAI's focus on AI reasoning is driving significant advancements in the field.
• Important note: The talent behind these advancements is in high demand.

A Resurgence in Reinforcement Learning

The recent advancements in reasoning models and agents developed by OpenAI are fundamentally linked to a machine learning methodology known as reinforcement learning (RL). This technique provides AI models with evaluative feedback regarding the accuracy of their decisions within simulated environments.

While not a new concept, RL has a history spanning decades. As early as 2016, approximately a year after OpenAI’s founding in 2015, an AI system developed by Google DeepMind, named AlphaGo, achieved worldwide recognition by defeating a professional Go player.

Concurrently, one of OpenAI’s initial team members, Andrej Karpathy, began exploring the potential of RL to build an AI agent capable of interacting with computers. However, the development of the requisite models and training methodologies would require substantial time.

By 2018, OpenAI had introduced its first substantial language model, belonging to the GPT series. These models were pretrained using extensive internet data and powerful GPU clusters. GPT models demonstrated proficiency in text processing, ultimately contributing to the creation of ChatGPT, but initially faced challenges with even simple mathematical calculations.

Key Breakthroughs in 2023

A significant breakthrough occurred in 2023 for OpenAI, initially referred to as “Q*” and subsequently as “Strawberry.” This advancement involved integrating Large Language Models (LLMs), RL, and a method called “test-time computation.”

Test-time computation equipped the models with additional processing time and computational resources to meticulously plan and verify their steps before delivering a response.

This capability facilitated the introduction of a novel strategy termed “chain-of-thought” (CoT), which notably enhanced the AI’s performance on mathematical problems it had not previously encountered.

OpenAI researcher Ahmed El-Kishky observed, “The model began to exhibit reasoning capabilities.” He further noted the AI’s ability to identify errors, retrace its steps, and even express frustration, mirroring human thought processes.

Although each of these techniques existed independently, OpenAI’s unique combination of them resulted in Strawberry, which directly paved the way for the development of o1. The planning and verification skills demonstrated by these reasoning models were quickly recognized as valuable assets for powering AI agents.

Lightman stated, “A problem I had been working on for years was finally solved.” He described this moment as a particularly rewarding experience in his research career.

Enhancing AI Reasoning Capabilities

OpenAI identified two key areas for improving AI models: increasing computational resources during post-training and allocating more processing time and power to AI systems when responding to queries.

According to Lightman, OpenAI consistently considers not only the current state of AI but also its future scalability.

Following the advancements of 2023, OpenAI established an “Agents” team, spearheaded by researcher Daniel Selsam, to further explore this emerging approach. Two sources confirmed this to TechCrunch. Initially, OpenAI didn’t distinguish between reasoning models and agents as they are understood today.

The primary objective was to develop AI systems capable of handling intricate tasks.

The work undertaken by Selsam’s Agents team ultimately contributed to the development of the o1 reasoning model. Key leaders involved in this project included OpenAI co-founder Ilya Sutskever, chief research officer Mark Chen, and chief scientist Jakub Pachocki.

The creation of o1 necessitated the allocation of significant resources, primarily skilled personnel and GPUs. Throughout its history, OpenAI researchers have needed to justify resource requests to company leadership, and demonstrable progress was crucial for securing these resources.

“A fundamental aspect of OpenAI is its bottom-up research approach,” Lightman explained. “Upon presenting the evidence supporting o1, the company readily agreed to prioritize its development.”

Several former employees believe that OpenAI’s overarching goal of achieving AGI was instrumental in the breakthroughs surrounding AI reasoning models. By concentrating on building the most advanced AI models possible, rather than focusing solely on product development, OpenAI was able to prioritize o1.

This level of investment in innovative ideas wasn’t consistently achievable at competing AI research facilities.

The decision to experiment with novel training methodologies proved to be forward-thinking. By late 2024, many prominent AI labs began experiencing diminishing returns from models developed through conventional pretraining scaling. Currently, a substantial portion of the AI field’s progress is driven by advancements in reasoning models.

What does it mean for an AI to “reason”?

A central aim of AI research involves replicating human intelligence within computer systems. Since the release of ChatGPT, advancements have introduced more human-like functionalities, including features suggesting “thought” and “reasoning” processes.

When questioned about the genuine reasoning ability of OpenAI’s models, El-Kishky offered a nuanced response, framing the concept through a computer science lens.

“Essentially, we are training the model to utilize computational resources effectively to arrive at solutions,” El-Kishky explained. “Therefore, if reasoning is defined in this manner, then it can be said that the model is indeed reasoning.”

Lightman adopts a different perspective, prioritizing the outcomes generated by the model over the underlying mechanisms or their parallels to the human brain.

“If a model successfully tackles complex tasks, it is employing whatever necessary approximations of reasoning are required to achieve those results,” Lightman stated. “We can legitimately refer to this as reasoning, as it exhibits patterns resembling reasoning processes, but ultimately it serves as a means to develop AI tools that are both potent and broadly beneficial.”

OpenAI’s researchers acknowledge that differing opinions may exist regarding their terminology or definitions of reasoning, and criticism has indeed surfaced. However, they contend that the capabilities of their models are more significant than semantic debates.

Other AI researchers generally concur with this viewpoint.

Nathan Lambert, an AI researcher at the non-profit AI2, draws a comparison between AI reasoning modes and airplanes in a blog post. He posits that both are artificial systems inspired by natural phenomena—human reasoning and bird flight, respectively—but function through distinct mechanisms.

This difference in operation does not diminish their utility or their capacity to achieve comparable results.

A collaborative position paper from AI researchers at OpenAI, Anthropic, and Google DeepMind recently highlighted the current lack of comprehensive understanding surrounding AI reasoning models. They emphasize the need for further investigation, suggesting it may be premature to definitively assert what transpires within these systems.

The Emerging Landscape: AI Agents and Subjective Challenges

Currently available AI agents demonstrate optimal performance in clearly defined and verifiable areas, such as software development. For instance, OpenAI’s Codex agent is designed to assist software engineers with routine coding assignments. Similarly, models from Anthropic have gained traction within AI coding tools like Cursor and Claude Code, representing some of the earliest applications for which users are actively paying.

However, general-purpose AI agents, including OpenAI’s ChatGPT agent and Perplexity’s Comet, often encounter difficulties with complex tasks requiring subjective judgment. Personal experience indicates that utilizing these tools for activities like online shopping or locating long-term parking can be time-consuming and prone to errors.

It is acknowledged that agent technology is still in its nascent stages and will inevitably evolve. Nevertheless, researchers must prioritize the development of improved training methodologies for underlying models to effectively handle more subjective assignments.

According to Lightman, the core issue lies in data availability. He stated that a significant focus of current research is on methods for training models on tasks where verification is challenging, with promising initial findings.

Noam Brown, an OpenAI researcher involved in the creation of the IMO model and o1, revealed to TechCrunch that OpenAI is pioneering novel, general-purpose reinforcement learning (RL) techniques. These techniques enable the instruction of AI models in skills that are not readily verifiable, a methodology employed in the development of the model that secured a gold medal at IMO.

OpenAI’s IMO model represents a newer AI system that generates multiple agents, allowing for concurrent exploration of various solutions before selecting the most optimal response. This approach is gaining prominence, with recent releases from Google and xAI showcasing state-of-the-art models utilizing similar techniques.

Brown anticipates increased capabilities in mathematical reasoning and other areas. He emphasizes the rapid pace of progress and foresees no indication of deceleration.

These advancements could translate into enhanced performance in OpenAI’s future models, potentially manifesting in the forthcoming GPT-5. OpenAI intends to solidify its market leadership with GPT-5, aiming to provide the premier AI model for powering agents utilized by both developers and end-users.

Alongside performance improvements, OpenAI is focused on enhancing usability. El-Kishky explained that the company’s goal is to create AI agents that intuitively grasp user intent, eliminating the need for complex configuration. This includes developing systems that autonomously determine which tools to employ and the appropriate duration for reasoning.

This vision outlines a future iteration of ChatGPT: an agent capable of executing any internet-based task on your behalf, while understanding your preferred approach. This represents a significant departure from the current functionality of ChatGPT, but the company’s research is firmly directed towards this objective.

While OpenAI previously held a dominant position in the AI industry, it now faces substantial competition. The central question has shifted from whether OpenAI can realize its agentic vision to whether it can achieve this before rivals such as Google, Anthropic, xAI, or Meta.