What If Your AI Could Remember Everything?
Matteo Aurelio Arellano, CFA
Strategic Data Solutions to Companies | Founder @ Foresight Fintelligence | Remote Opportunities
Imagine sitting with your CFO and asking, “What if we could give our AI models the memory of a senior analyst who remembers every document, every deal, and every risk flag from the past five years—almost instantly?” That scenario is now possible thanks to longer context windows being rapidly deployed across new LLM models. While this could become a strategic advantage, it also brings significant responsibilities and risks. Building a strong AI-aware culture across organizations is crucial to managing the high compute costs involved and actively protecting company data and privacy.
The Rise of the “AI Memory” Game in Finance
When ChatGPT launched, traditional LLMs (Large Language Models) could barely remember two or three pages of 1,500 words each, equivalent to around 4,000–8,000 tokens. In under five years, tech companies have released highly capable models such as Microsoft’s LongRoPE, which can now handle up to 2 million tokens. The implications are massive.
These models can digest, analyze, and process entire earnings reports, credit memos, investment decks, and lengthy project finance legal tenders, all in one go. This capability has the potential to boost productivity by 20x and capture details that previously required an entire team of experts to review.
In finance, that changes everything.
Forget rigid workflows. Now you can ask your model to:
Knowing how to implement these new LLM features effectively across the organization requires both technical expertise and people management. But if done right, it can become a competitive edge by enabling better results, faster responsiveness to customers and partners, and improved decision-making.
What Are Tokens, Really?
Think of tokens as word pieces. Tokenizers are tools that convert words into tokens, which are then processed by LLMs. According to IBM, one word is roughly equal to 1.5 tokens.
Long words like “Internationalization” might be split into five tokens, while “the dog” is three. When we talk about the context window of an LLM, we’re referring to the number of tokens the model can remember during a conversation. It defines the token limit the model can use to incorporate previous data into its responses.
The larger the context memory, the more the model can recall from the inputs or documents used to calibrate your responses. Most models operate with a token limit (context window), and every character counts.
OpenAI’s GPT-4-Turbo handles 128k tokens; Mistral and LLaMA2 go even further with LongRoPE, reaching 2M tokens.
Token Calculation Guide:
But, Why Should It Matter?
The more tokens your model can process, the better it can understand context:
You’re using AI to provide grounded, more relevant responses to your team’s specific needs at a fraction of the cost it used to require. The tool is here; the difference lies in how you choose to use it.
A Brief Timeline: The Explosion of Context Windows
As humans, we quickly forget how things were before tools like AI entered the scene. Progress in GenAI happens almost daily, yet it’s humbling to realize that in under five years, we’ve gone from models that could remember just 2,000 tokens to ones capable of handling 2 million. Imagine what’s coming in the next year or two.
From 2k to 2M tokens in four years. That’s a 1,000x leap in model memory capacity.
Is More Context Always the Best Choice?
More is not always better. Like anything in life, there are pros and cons to having more powerful and refined LLM models at your disposal.
Start by asking why you want a financial assistant or CFO-AI copilot in your organization.
Be clear and critical about the actual benefits it would bring. A cost-benefit analysis helps, but gathering quantitative and qualitative input from corporate teams is also crucial to understanding bottlenecks and how much information lives in those areas.
Once you have a compelling case, you need to think through the implications of using large-context LLMs.
Recommended by LinkedIn
Extended windows provide the benefit of processing larger documents. Before extended context windows, teams had to split documents into chunks and use Retrieval-Augmented Generation (RAG) pipelines to simulate memory. That method often introduced hallucinations, mismatches, and fragile logic chains.
Now, you can feed an entire investor deck, legal document, or multi-quarter forecast into a single prompt. With more context in memory, the model doesn’t lose track of what came earlier. You get summaries that understand the full story, not just paragraph 3 out of 10.
Another benefit of larger context models is that there are less abrupt drop-offs halfway through a contract or document scanned. The model can “see” and reason over the beginning, middle, and end, without guessing or skipping logic.
But What’s the Cost?
First: compute. Doubling the context window can nearly quadruple the compute power required. If you’re running private models on GPUs or relying on hosted APIs, you’ll see:
Next: latency. Running a model with a 2 million-token context window means longer wait times. For time-sensitive workflows like algorithmic trading or trade operations, this is critical to consider.
Then comes security. While all LLMs face these risks, larger context windows often lead to broader use across teams. That means more documents, files, spreadsheets, and code being submitted, any of which could reveal trade secrets, intellectual property (IP), or other sensitive business data. Even generic-looking content can be dangerous in the wrong hands.
Prompt injections from external users are real risks.
Even in enterprise environments, long prompts can be hijacked in several ways: • An internal LLM app might allow user-supplied prompts to manipulate system instructions. • Embedded internal documents could be unintentionally summarized and leaked in responses.
Another area of risk lies in plugins, external tools, third-party integrations, and retrieval layers. Enterprise LLMs often use:
These components may store or transmit data outside your private environment if not properly configured. The model itself might be secure, but the ecosystem around it might not be. It’s essential to vet the privacy and security policies of all third-party vendors involved.
Ongoing refinement of your internal AI policy is crucial. Teams need to understand when it’s appropriate to paste trade contracts, customer files, or sensitive info into chat windows, and when it’s not.
As the cybersecurity saying goes: “Security is only as strong as your weakest tool.”
Finally, let’s talk about hallucinations and decision risk.
With huge context windows, it’s easy to become overly confident and skip verifying facts. When major investments or decisions rely solely on LLM outputs, the risk of costly errors rises. A human sanity-check policy is vital, especially before executing trades, signing supplier contracts, or finalizing strategic moves.
LLMs often sound confident, as if they know everything. In reality they still hallucinate. AI evolves fast, and many vulnerabilities are still undiscovered.
Best Practices to Avoid Information Leaks
A misconfigured LLM can expose client accounts, deal sheets, or M&A documents.
Tips for CFO and managers:
McKinsey notes that context windows don’t just expand technical capabilities—they also expand compliance liabilities. IBM adds, “Think of the context window as a whiteboard. Once full, what stays and what gets erased matters.”
Why You Should Act Now Extended context models are not just technical upgrades—they’re strategic multipliers. The next generation of CFO dashboards, fund manager copilots, and internal bots will depend on them.
At Foresight Fintelligence, I help firms to help firms to:
📩 Book a clarity call at foresightfintelligence.com, I will assess your company needs and help you drive change with AI in a responsible and meaningful way.
First assessment at no cost, limited available spots. Secure it today.