AI’s Hidden Limits: How Token Caps Mirror the Early Days of Computing and Hinder Progress

In the early days of computers, especially in universities during the 1960s and 1970s, computers were massive and expensive machines. Because of their limited availability, individual users couldn't have personal access. Instead, a system called time-sharing was developed. This allowed multiple users to access the same computer by dividing its processing power into small time slots. Each user would get a brief window to run programs or calculations, making it seem like the computer was serving everyone simultaneously.

This was a major advancement over earlier systems where one person could monopolize a computer for hours or days. However, it also meant users had strict limits on how long and how deeply they could interact with the system.

Fast forward to today, and we see a similar pattern with advanced AI systems. Instead of physical access limits, users now face session Cap limits, token caps, or time restrictions when interacting with large language models like ChatGPT, DeepSeek, and Copilot. These limits control how much you can interact with the model in a single session or over a certain period. For example, token limits restrict how much data (text) you can send and receive, while session timeouts force you to restart your work after a certain time.

For heavy AI users—especially those who depend on multiple AI tools simultaneously—these limitations become frustrating. Imagine trying to work on complex projects that require deep, continuous engagement, only to be interrupted by system limits. It feels very similar to how early computer users were cut off when their time-sharing slot ended.

Coping with Token Limits: Personal Strategies to Stay Productive

Dealing with token limits in large language models can be frustrating, especially when working on complex, long-term projects. Starting over or switching to a different model often isn’t practical because even with summaries, it’s difficult to rebuild the context. This is why I prefer to continue within the same session for as long as possible.

From my experience, Claude Sonnet 3.5 is the most advanced model available today, but ironically, it also has one of the lowest token limits. This creates a challenge for deep, continuous work. To manage this, I use a simple but effective strategy: I ask Claude to notify me when I reach about 75% of my token limit. This early warning gives me time to prepare—either by summarizing the current conversation for the next session or deciding if I need to transition to another model.

However, managing token limits isn't just about switching models. Sometimes, the best solution is to open a new chat within the same model. This is necessary because, with every interaction, these models reprocess the entire conversation history to maintain context. While this makes sense from a front-end engineering perspective, it causes the token cap to fill up quickly. Starting a new chat can reset this buildup and extend productive interaction.

Another effective workaround I use is leveraging GitHub Copilot (or similar tools) to access Claude Sonnet 3.5 through what I assume is its API. This version seems slightly inferior to the standard Claude interface, but it offers a more generous usage limit. More importantly, it allows me to interact directly with my GitHub repositories, which is a powerful feature for integrating AI support into software development. Upscaling my subscription isn't an option, and creating multiple accounts doesn’t make sense, so this integration is an efficient solution.

Additionally, I maximize productivity by using four different AI models integrated into my development environments, including JetBrains, Visual Studio Code, and other IDEs. This multi-tool setup helps balance model limitations and enhances my workflow across various tasks.

By using these strategies—setting early warnings, starting new chats, integrating AI models into IDEs, and leveraging tools like GitHub Copilot—I can effectively manage token limits and stay productive.

The Future of AI Access: When Tokens Become the New Currency

As AI language models continue to advance, we may witness a future where access to these powerful tools becomes increasingly commodified. The concept of tokens—units of interaction with AI models—could evolve into a form of digital currency, dictating who can afford to utilize superior AI capabilities.

In this scenario, individuals or organizations with greater financial resources would have the means to purchase more tokens, thereby gaining access to advanced AI models with extensive capabilities. This could lead to a disparity in AI accessibility, where only the affluent can leverage cutting-edge technology, potentially widening existing socio-economic gaps.

Moreover, the commodification of AI access might give rise to a marketplace where tokens are bought, sold, or traded, similar to cryptocurrencies. This could introduce complexities related to market regulation, ethical considerations, and equitable distribution of AI resources.

The Role of Open-Source Models

In contrast to proprietary AI models controlled by a few corporations, open-source AI models present an opportunity to democratize access to advanced AI technologies. Open-source initiatives allow developers worldwide to collaborate, innovate, and improve AI models, potentially leading to more efficient and accessible solutions.

However, challenges persist. Open-source models may struggle to compete with the vast resources and data available to large corporations, potentially limiting their performance and scalability. Additionally, concerns about the misuse of open-source AI for malicious purposes necessitate the implementation of robust ethical guidelines and governance structures.

Balancing Efficiency and Scale

The debate between model efficiency and scale is ongoing. While efficient models aim to deliver high performance with fewer resources, large-scale models often achieve superior results due to their extensive training on vast datasets.

Recent advancements suggest that increasing the context window of AI models can enhance their capabilities. For instance, Google's latest Gemini model has expanded the context window to 1 million tokens, allowing for more comprehensive processing of information. However, this increase comes with significant computational costs, as processing requirements scale quadratically with the window size.

Therefore, the future may involve a hybrid approach, combining efficient algorithms with scalable architectures to achieve optimal performance. This balance could enable broader access to AI technologies without necessitating prohibitive computational resources.

Ethical and Societal Implications

The evolution of AI language models raises several ethical and societal questions:

• Equity of Access: How can we ensure that advancements in AI benefit all segments of society, rather than exacerbating existing inequalities?
• Regulation and Governance: What frameworks are necessary to oversee the commodification of AI access and prevent potential abuses?
• Sustainability: Given the substantial energy consumption of large AI models, how can we develop sustainable practices in AI development and deployment?

Still in the Early Days of AI Access

It’s clear that while AI has made incredible progress, we are still far from fully harnessing its potential. In many ways, we are living through a modern version of the time-sharing era—where access to computing power was limited and carefully rationed. Today, the same concept applies to AI through token limits and session caps, restricting how deeply and continuously we can interact with these powerful models.

For casual users, these limits might not seem significant. But for those of us who work with AI intensively—5 or more hours a day—these constraints become frustratingly clear. Over time, I believe more people will begin to understand this challenge as AI becomes a bigger part of daily workflows. Hitting token limits, losing context, and struggling to maintain complex conversations will become more common, raising broader concerns about how AI access is managed.

There’s hope that, in the future, tokens will become cheaper and usage limits more generous. But I remain skeptical. Scaling AI models to be larger and more capable is fundamentally a computational problem. Bigger models demand more resources—more processing power, more data, and more energy. This means the more access companies provide, the more they need to invest in improving and maintaining these systems. It creates an endless cycle: advancing AI leads to higher costs, which then justifies tighter restrictions.

As we move forward, it’s important to recognize this balance between technological progress and equitable access. Without thoughtful solutions, we risk creating a future where only a privileged few can fully leverage the most advanced AI tools, while others are left behind.

The path ahead will be shaped by how we solve these challenges—whether through more efficient models, fairer pricing, or innovations that break this cycle. Until then, we are still in the early stages of what AI can truly offer.