The Next Generation of AI: Smarter Models and the Quantum Frontier
By: Richard Murray, Co-founder and CEO of ORCA Computing
The meteoric rise of large language models (LLMs), like OpenAI’s GPT series, has revolutionized how we interact with technology. These models are already rewriting the rules of productivity and problem-solving. However, it’s no surprise that as their capabilities grow, people have been putting them to the test and are finding that, like all human-made systems, they are not perfect. They can, and they do, make mistakes.
From logical inconsistencies to hallucinations, LLMs sometimes falter in ways that can be frustrating, particularly when these errors creep into applications requiring high precision. Despite the opinions of online doom-mongers who seize upon these issues to declare AI’s limitations, this does not signal the end of our interest in or our reliance on AI.
What these flaws do suggest is the need for a new generation of AI models—ones designed not just to be bigger but also to be fundamentally smarter.
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Bigger Isn’t Always Better. Moving Toward Smarter AI
What interests me most about the future of AI isn’t just the race to create bigger models (cf GPT-5) but the focus on making them smarter. Bigger isn’t always better; it’s becoming increasingly clear that quality, not just scale, will define the next wave of AI innovation.
“Smarter” in this context doesn’t just mean better at mimicking human behavior or generating text. It means building more deterministic logical engines within existing large models. Determinism ensures that AI systems are consistent in their reasoning and decision-making processes. Rather than producing answers that occasionally vary or wander off course, deterministic AI is grounded in foundational rules and patterns that lead to reliability and trustworthiness.
For example, when tasked with solving mathematical problems or explaining the laws of physics, smarter AI models will not only aim for correctness, they in fact will be designed to avoid fundamental errors.
The Rise of Physics-Embedded Models
The pinnacle of this evolution is what are known as “physics-embedded models.” These models represent a leap in AI design by integrating immutable truths, such as numerical principles and the laws of physics, into their core framework.
Unlike current models, which rely on statistical patterns learned from vast datasets, physics-embedded models will possess the ability to discern and uphold truths that cannot be fabricated. Much like humans understand that gravity always pulls objects downward or that two plus two equals four, these AI systems will be hardwired not to make basic mistakes about the physical and numerical realities of our universe.
If you believe what you read (and I do!), this shift will revolutionize AI applications, making them more relevant to business and scientific domains.
From Creativity to Precision
The AI models we use today excel in tasks like copywriting, journalism, and creating images. They’re remarkable tools for creativity, enabling writers, marketers, and designers to amplify their productivity and output.
But the smarter models on the horizon promise much more. They will be capable of performing numerical calculations with precision, analyzing complex datasets, and even advancing scientific discovery. These capabilities will unlock new opportunities for industries that demand accuracy and analytical rigor.
In finance, for example, smarter AI could transform how companies model risk, forecast markets, and optimize investments. In healthcare, it could accelerate breakthroughs in diagnostics and treatment development. And in engineering, it could revolutionize how we design everything from sustainable energy systems to advanced aerospace technologies.
This transition from creative assistant to indispensable problem-solver will redefine AI’s role in business and society.
Quantum Computing at the Frontier of AI
On the frontier of this evolution is the work to embed quantum computing within future AI models. This step goes further by embedding some of the laws of (quantum) physics within the models themselves.
Quantum computing isn’t just a faster version of classical computing. It’s an entirely new paradigm, one rooted in the principles of quantum mechanics. Concepts like superposition and entanglement enable quantum systems to process information in ways that classical computers simply can’t match.
It is my view that quantum computing has a permanent role to play in supercharging generative AI models. This is especially true for models trying to replicate the statistics and correlations that apply to the quantum world.
Also Read: Why Quantum AI is the Next Big Thing for the Future of AI
A Quantum + AI Revolution in Drug Discovery
One fantastic example of this potential is in the realm of drug discovery. The process of designing new drugs is incredibly complex, involving countless variables and requiring simulations of molecular behavior that push classical computers to their limits. Quantum computing, combined with AI, could revolutionize this process. By simulating molecular interactions at a quantum level, quantum-AI systems can identify promising compounds faster and more accurately than traditional methods.
We have only just seen the start of this quantum-AI synergy, but its promise is undeniable. It will bring not only a much-needed new dimension and perspective to AI, but also the first relevant and commercial application of quantum computing. In doing so, it could save lives, reduce costs, and accelerate innovation in pharmaceuticals and beyond.
A Future Within Reach
The evolution of AI toward smarter, physics-informed, and quantum-augmented models is nothing short of transformative. These advancements promise to reshape industries, redefine the limits of technology, and unlock solutions to problems that once seemed insurmountable.
From advancing our understanding of the universe to tackling climate change, the ability to model complex systems with precision will have a profound impact on science, education, and policymaking. By reducing errors and enhancing our capacity to analyze and predict, smarter AI models could help humanity address some of its most pressing challenges.
We are on the cusp of incredible advancements in AI and quantum computing. The possibilities these smarter, physics-informed models unlock are too exciting to ignore. The next generation of AI will redefine what’s possible and will accelerate progress in ways we are only beginning to imagine.
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