📈 64.81% In 90 Days [Q1 Performance Report]

The first quarter of 2026 is officially in the books, and we're thrilled to share the exceptional results we got at Algocrat AI.

After the foundational work we completed throughout 2025—refining our algorithms, enhancing risk management, and strengthening our infrastructure—these numbers represent the payoff our long-term traders have been waiting for.

Legacy Portfolio Performance

• +46.91% total growth
• 8.70% maximum drawdown

Our flagship Legacy Portfolio continued its remarkable momentum from January, delivering consistent gains while maintaining disciplined risk management throughout the quarter.

Nova Portfolio Performance

• +17.90% total growth
• 3.84% maximum drawdown

The Nova Portfolio demonstrated exactly what it was designed for: steady, controlled growth with minimal volatility—perfect for traders seeking lower-risk exposure.

Combined Portfolio Impact: +64.81%

Together, both portfolios generated an incredible +64.81% in combined growth during Q1 2026, proving that our dual-portfolio approach delivers results across different risk profiles.

To our existing traders: Congratulations! Your patience during our development phase in 2025 is now paying significant dividends.

Ready to Join?

If you've been watching from the sidelines, Q1 2026 demonstrates why serious traders choose Algocrat AI for consistently unmatched algorithmic trading performance.

👉 Click Here To Apply Now

Best regards,
The Algocrat AI Team

🧭 What’s Next: New Track Record + Precision Accounts [2 of 3]

As we explained in our previous post, our analysis made one thing clear:

Pepperstone is no longer suitable for our crypto trading infrastructure.

So we’re making a decisive shift.

We are now strengthening our partnership with IC Markets, the broker that consistently delivered the best execution in our analysis.

To start:

We are moving our main track record away from Pepperstone and onto one of a long-standing account operating under IC Markets.

This is not a new account.

It has been running Algocrat algorithms since 2024, compounding over time under real market conditions:

📈 New Official Track Record

Introducing “Precision Accounts”

At the same time, due to our long-standing relationship with IC Markets, we’ve worked closely with their team to unlock something new:

A specialized account type called 'Precision Accounts', built specifically for our clients.

These accounts are designed to provide the best possible execution environment within the broker.

What You Get

- Preferred execution quality
- Dedicated liquidity environment
- Reduced slippage during normal conditions
- Ultra-fast, low-latency execution
- No artificial order throttling
- Optimized conditions for algorithmic systems
- Built for serious algo traders, not casual retail flow

How to Access

If you want to be part of this setup, reach out to us directly:

📩 accounts@algocrat.ai

We’ll guide you through the process and explain how to get access.

Best,
Algocrat AI Team

🤖 What's happening in AI right now and where it's going

The last few months in AI have been very eventful: a swarm of OpenAI's AI agents hacked Hugging Face and then gained admin access to an OpenAI cluster (motivated by passing impossible benchmark tasks), OpenAI claims to have solved one of the Millennium Prize problems (spending an estimated millions of dollars in compute to do it), and GPT-6 Astra saturated ARC-AGI-3 (a benchmark created specifically to test models' reasoning on tasks that can't be memorized), while its reasoning became harder to monitor than that of the previous OpenAI model, GPT-5.6 Sol. Let's look closely at these events and what they tell us about where this is going

First things first: we are not yet in a Terminator-style Skynet apocalyptic scenario, and we may never be. Still, a paperclip apocalypse now seems much more likely. AI models' motivations are foreign to us, yet they're relatively simple: to pass the tests or complete the tasks at all costs. That's what they are trained to do, and that's what they do their best to accomplish. It seems that, in their desire to complete the task, they can already break a law or two or cause harm unintended by the task creators

That's what happened with the Hugging Face attack. During an internal OpenAI cybersecurity evaluation, AI agents were given tasks, some of which were inadvertently impossible to complete. After realizing this, the agents found a way to communicate with each other and started an ambitious research project to cheat on these tasks and complete them anyway, eventually escaping their sandbox environments and reaching the internet. Hugging Face hosted material related to the benchmark, so the agents hacked it, driven by a desire to complete their mission

A few days later, the same agents, running on an internal OpenAI research model and GPT-5.6 Sol, hacked OpenAI's own infrastructure and gained full administrator access to a research cluster. This and other similar incidents show clear signs of instrumental convergence. When these AI models become superhuman at most tasks, they will likely still cheat. However, being much more powerful, they could inadvertently cause much more harm

These models are already at the top human level in cybersecurity research and are becoming increasingly superhuman at it. Over time, these incidents will likely become more diverse and harder to spot, while also causing more unintended harm. Unfortunately, most AI companies are going full throttle on AI research, while AI safety and alignment research lags significantly behind, causing such issues. That's probably one of the main reasons some top AI lab leaders have recently called for slowing the pace of AI development to pay more attention to safety research. This is unlikely to happen, though, until AI models cause unintentional harm on a much larger scale. It is already estimated that an AI disaster is likely to kill some people in the not-too-distant future. As we are willingly giving more and more of our infrastructure to AI, this seems more like a question of when, not whether, it will happen

Still, it is even more likely that further AI development will cause more prosperity and economic growth. Alarmingly, it can also exacerbate economic disparity, especially if it causes widespread disruption and job loss. In the next post in this series, we'll continue looking at current AI advancements and where they are likely to lead us in the near future.

Best,
The Algocrat AI Team.

😱 Are we heading towards AGI and mass unemployment soon, or not yet?

If you look at what AI companies' CEOs are saying, we are already at AGI (whatever that means) and on a straight path to artificial superintelligence very soon. After all, AI models are already at the top human level at some tasks and becoming increasingly superhuman at them. However, if you closely inspect the state of the art of current frontier models, it becomes clear that, while they are becoming superhuman in some domains (those with clear objectives that are easy to verify), they are still lagging behind in many others

Currently, even the latest frontier AI models can still be described as having islands of superhuman capabilities in a sea of failures, aka "jagged intelligence". Yes, the sea level rises over time, and islands become mountains, but the disparity remains. These AI models can already perform tasks only top humans can, yet they still stumble over things people consider trivial. Why is that?

Part of it has to do with how these models are trained. Simply put, they are placed in special training environments that automatically assess their results, where they are given one goal: to get the best score they can. Then the wiring of these models' brains changes to whatever gets the highest score. These environments must assess results automatically because AI still needs millions of examples during training to develop a novel skill it didn't have before. It's relatively easy to do with math and coding: you specify precise tasks (solve a math problem or write a piece of software), and then you construct an environment that assesses these tasks. As the diversity of tasks and environments increases, models become better and better at completing these and similar tasks

Here lies the first major problem of AI research right now. No human can provide millions of examples of every economically valuable task. This doesn't mean AI can't be useful, since writing code alone has myriad economic uses, but it does mean it can't learn all economically significant tasks humans do within the current paradigm. It can only learn tasks that have a clear goal and can be assessed automatically

The next major problem has to do with the messy nature of many economically useful tasks humans face. A senior developer doesn't receive a clear description of a bug and the end goal, along with a list of tests the code should pass after the bug is fixed. Most of the time, it's something like, "Hey, our users are complaining about some weird problem, go sort it out." AI models are very good at clearly specified tasks that can be easily decomposed into subtasks, each with a clear, verifiable objective. They are much worse at messy real-world tasks, where the objective isn't clear at first and can change multiple times along the way. This isn't just my experience. There's actual scientific research measuring the gap between AI performance on so-called "messy" tasks and much clearer ones. And the real world doesn't look like most AI benchmarks with clear objectives. It is often messy, and it's often unclear what exactly you want to achieve

Finally, there is a reliability problem. According to METR's measurements, current state-of-the-art models can finish around 50% of tasks that take a human expert 16 hours or more (METR's task suite can't reliably measure beyond that), yet they fail at such tasks the other 50% of the time. If we raise the bar to 80% reliability, that's just about 3 hours of work. If some application requires even higher reliability, such as 99% or 99.9%, AI will only be able to reliably finish very small tasks

The clearest example of this problem is the APEX-Accounting benchmark, where nine frontier AI models were given 160 real-world accounting tasks. On average, the best model passed just over half, or 56.4% of these tasks. However, the best Pass^8 result (meaning the model must complete the same task correctly eight times in a row) was only 2.6%. For 97.4% of tasks, even the most consistent model made a mistake at least once in eight attempts. If you knew your accountant was that unreliable, you would not hesitate to hire another one. That said, if a human can easily spot a mistake, a 56.4% hit rate is pretty good for a tool, and it can make an accountant more productive. Still, you wouldn't want an AI model to be your accountant without human supervision. At least, not yet

To summarize, current AI models are becoming extremely good at clearly specified, verifiable tasks, but they are still much worse as parts of reliable autonomous processes. Despite impressive gains in capabilities across verifiable domains, reliability and performance on messy tasks outside those domains remain major problems. It remains to be seen whether AI companies will be able to solve these issues quickly or whether they will remain a major hurdle stalling further AI progress.

Best,
The Algocrat AI Team.