哈萨比斯在2024年获得诺贝尔化学奖，距离AlphaFold发布不到4年，从蛋白质折叠研究起算仅约10年，成为近70年来化学领域从发现到获奖速度第二快的案例。他强调科学而非财富驱动，明确表示即使给予100亿美元也不会交换诺贝尔奖。其个人消费极低：车辆使用约10年，仅少量收藏（约5000英镑），娱乐支出有限（足球赛季约5场），但捐赠数百万英镑用于奖学金。其核心观点是财富与权力只是实现科学目标的工具，而AGI应被视为影响全人类的公共资源。
在科学愿景上，他提出利用超大规模设施推进基础物理研究，例如构想类似大型强子对撞机（周长约27千米）的空间版本，规模可达“类月球级”，利用恒星能量与引力系统。他关注普朗克尺度问题，即现实是连续还是离散，这一争论已持续超过100年，而现有对撞机分辨率仍远低于该尺度。他还提出关键假设：自然界所有可生成模式都可被经典（非量子）算法高效建模，从而将AI能力与宇宙结构联系起来。
在计算理论争论中，他反对量子意识与量子计算的必要性，认为经典图灵机通过深度学习与强化学习已具备处理不确定性的能力。以蛋白质折叠为例，约10^300种可能结构可被AlphaFold准确预测，显示无需量子叠加也可解决复杂问题。他认为自然界因“进化筛选”必然包含可学习模式，因此经典计算在合理时间内可逼近解空间极限。这一立场直接挑战量子优先论，并将AI发展视为验证图灵机潜力的现实路径。
Hassabis won the 2024 Nobel Prize in Chemistry less than 4 years after AlphaFold’s release and roughly 10 years after beginning protein-folding research, making it the second-fastest discovery-to-award case in chemistry in nearly 70 years. He rejects financial motivation, stating he would not exchange the Nobel Prize even for $10 billion. His personal spending is minimal: a ~10-year-old car, limited collectibles (~£5,000), modest leisure (~5 football matches per season), while donating several million pounds to scholarships. He frames wealth and power strictly as instruments for scientific goals and argues AGI should be treated as a global public good.
His scientific vision includes extreme-scale infrastructure, such as a space-based collider analogous to the Large Hadron Collider (~27 km circumference), potentially at “moon-scale,” powered by stellar energy and gravitational systems. He targets Planck-scale physics—whether reality is continuous or discrete—a debate unresolved for over 100 years, with current collider resolution far above that scale. He advances a central hypothesis: all generable patterns in nature can be efficiently modeled by classical (non-quantum) algorithms, linking AI progress to fundamental cosmology.
In computational theory, he rejects the necessity of quantum computation and quantum consciousness, arguing classical Turing machines augmented by deep learning and reinforcement learning can handle uncertainty. In protein folding, roughly 10^300 possible conformations are narrowed accurately by AlphaFold without quantum superposition. He claims natural systems, shaped by evolutionary selection, inherently contain learnable patterns, enabling classical computation to approach solution limits within feasible time. This stance challenges quantum primacy and positions AI as empirical validation of Turing machine scalability.