Abstract
This article explores the projected evolution of artificial intelligence (AI) and its integration with quantum computing from 2025 to 2075. AI capabilities, often compared to human intelligence using theoretical “IQ” metrics, are expected to increase exponentially, potentially reaching levels far beyond human cognition. Advances in AI architectures, computational power, algorithmic efficiency, and the availability of high-quality data will drive this growth. Additionally, quantum computing is anticipated to play a crucial role in AI development by enhancing optimization, machine learning, and complex problem-solving. The integration of quantum AI could lead to groundbreaking advancements in science, engineering, and decision-making. However, significant challenges remain, including energy consumption, data quality, ethical concerns, and the alignment of AI with human values. While AI superintelligence remains a possibility by 2075, its impact will depend on technical breakthroughs, societal adaptation, and regulatory frameworks. This paper presents a balanced view of AI’s future trajectory, emphasizing both its transformative potential and the risks that must be carefully managed.
Estimating AI IQ Growth
Artificial intelligence has been advancing rapidly, prompting comparisons between AI capability and human intelligence (often expressed as “IQ”). While IQ tests are designed for humans and not directly applicable to machines, researchers have attempted to gauge AI performance on human benchmarks. Recent large models like GPT-4 already score in the top 0.1% on certain standardized tests, equivalent to an IQ around 150 (lifearchitect.ai)
Extrapolating current trends, some futurists speculate that AI systems could eventually reach IQ- equivalent levels of 1000 or even 3000. Such figures imply cognitive abilities far beyond any human, though they are mostly theoretical projections to illustrate potential growth.
Measuring AI vs Human Intelligence: Defining and measuring “intelligence” in AI is an open challenge. Traditional metrics like the Turing Test (whether an AI can imitate a human in conversation) provide a binary pass/fail, not an IQ score. Researchers instead use a variety of benchmarks: for example, testing AI on school exams, logic puzzles, or problem-solving tasks. There is debate about how meaningful these tests are. François Chollet argues we “lack a proper measure of intelligence” for machines and that abstract IQ numbers may mislead (aimyths.org)
He introduced the Abstraction and Reasoning Corpus (ARC) to measure an AI’s reasoning ability more like human problem-solving (aimyths.org)
This highlights that AI “IQ” is usually measured indirectly – by performance on tasks that we believe require intelligence – rather than a single agreed-upon scale.
Past Trends and Extrapolation: Over the past decade, AI capabilities have grown exponentially, suggesting a trajectory for future “IQ” growth. Systems have gone from narrow experts to more general problem solvers. Notable milestones include AIs beating human champions at chess (1997), Jeopardy! (2011), Go (2016), and poker (2019). In language and reasoning, models grew from only handling simple chatbot replies to composing essays, coding, and proving theorems. A key driver has been the scale of models – for instance, in 2018 a typical AI model had ~100 million parameters, whereas by 2020 GPT-3 had 175 billion parameters (and Google’s PaLM reached 540 billion) (news.climate.columbia.edu)
This 1000× increase in model size within just a few years led to dramatic leaps in capability. Similarly, the compute used for training has exploded: an OpenAI analysis found the computational power in the largest training runs doubled every ~3.5 months from 2012 to 2018
(sc-asia.org) – far outpacing Moore’s Law. If such trends continue (even at a slower pace), by 2075 AI systems could be orders of magnitude more powerful and sophisticated than today’s, potentially achieving problem-solving proficiencies that, in human terms, might be likened to IQs of several thousand. However, these numbers are speculative and serve more as a metaphor for vastly superior intelligence rather than a strict scientific estimate. In fact, Chollet cautions that even an AI with “IQ 3000” might not transform the world if it lacks the physical and contextual grounding to apply that intelligence (aimyths.org).
In short, AI’s measurable intelligence is expected to increase tremendously, but how we quantify it and what that means in practice will remain complex.
Factors Driving AI Growth
Multiple factors will drive AI’s growth in intelligence and capability over the next 50 years. These include advancements in architectures, improvements in hardware and efficiency, algorithmic innovations, and ever-expanding data. Together, these create a feedback loop accelerating AI development.
- Advances in AI Architectures: The design of AI models has evolved from simple neural networks to sophisticated structures. The introduction of transformer architectures (since 2017) enabled AIs to handle language, vision, and other tasks with unprecedented scale and parallelism. This breakthrough led directly to today’s large language models and is likely to keep evolving. Future architectures could incorporate more neuromorphic principles – mimicking the brain’s structure and sparse firing patterns. Research chips like Intel’s Loihi have demonstrated 1000× to 10,000× improvements in energy efficiency for certain neural
networks by using spiking neurons (eetasia.com).
By 2075, neuromorphic computing or other novel architectures (e.g. liquid networks, biologically-inspired circuits) may allow AI to achieve far greater complexity with much lower power, enabling “always-on” intelligent assistants or robots with brain-like efficiency. Additionally, ongoing research into Artificial General Intelligence (AGI) aims to create architectures that learn and reason more like humans, combining tools like memory, logic, and learning algorithms. Approaches range from cognitive architectures (systems that simulate aspects of human cognition) to brain simulation attempts. While an early effort at full brain simulation (the Human Brain Project) fell short of its 10-year goal
(aimyths.org), scaled-down and iterative approaches may yield progress. These architecture innovations will be fundamental to pushing AI toward higher-level thinking and adaptability
- Hardware and Computational Power: Underlying hardware improvements will strongly drive AI growth. Specialized AI processors today (GPUs, TPUs, FPGAs) allow massive parallel computations needed for deep learning. Moore’s Law (doubling transistor density
~every 1.5–2 years) has slowed, but alternative strategies are extending compute growth – including 3D chip stacking, better cooling, and domain-specific chips. Companies are deploying enormous cloud clusters with tens of thousands of AI accelerators to train models. This scale-out will continue as long as energy and cost allow. By 2075, AI might harness exaflops or zettaflops of processing power, whether through classical hardware or hybrid quantum setups. New paradigms like optical computing (using light for faster matrix operations) or analog neural chips could also contribute. Importantly, improved hardware has historically been a key enabler: the rapid increase in AI compute was a major factor in recent breakthroughs (sc-asia.org).
We can expect further orders-of-magnitude gains in effective compute, which will directly translate to more complex and “intelligent” AI models
- Algorithmic and Software Efficiency: Better algorithms make AI smarter without needing brute-force hardware gains. One example is the progress in training techniques – from basic stochastic gradient descent to advanced optimizers and strategies like curriculum learning (teaching in stages) and reinforcement learning from human feedback (fine-tuning AI behavior with human preferences). Such techniques get more out of existing models. Self-learning algorithms are another driver: AIs can now improve by ingesting unlabeled data (self-supervised learning) or by playing against themselves (self-play in games, used by AlphaZero). This reduces the dependency on human-labeled examples and allows continuous improvement. Future AIs may also employ recursive self-improvement, where they can refine their own code or architecture. While we haven’t yet seen an AI truly rewriting itself for the better, even partial automation of AI design (e.g., neural architecture search, where AI optimizes model designs) speeds up progress. For instance, early versions of this have yielded novel neural net designs crafted by algorithms rather than human engineers. By mid-century, an advanced AI could potentially analyze its own performance
and suggest improvements, creating a positive feedback loop of accelerating intelligence – the essence of the long-theorized intelligence explosion (aimyths.org).
Even if full self-recursion is not achieved, ongoing improvements in algorithms (e.g. more efficient learning methods, better use of context and memory, integrated reasoning modules) will make AI systems more capable and smarter with less data and compute.
- Data Availability and Quality: “Data is the fuel for AI.” Over the next decades, the volume of digital data will grow astronomically, providing rich training material. AI systems today learn from vast datasets (e.g. text from the entire internet, millions of images or videos). By 2075, sensors and the Internet of Things, higher-resolution satellites, and digitalization of most human activities could produce zettabytes of data for AI consumption. This abundance will help AI learn about every domain – from science and medicine to daily human life – thus broadening its knowledge base. Moreover, AIs will increasingly use multimodal data (combining text, audio, visuals, sensor inputs), which gives them more context and understanding of the world. A large part of human intelligence comes from integrating our various senses; similarly, an AI that learns from text and images and real- world robotic feedback will develop more general intelligence. However, it’s not just quantity – quality and diversity of data are crucial. Researchers are developing better datasets that capture edge cases and reduce biases, so AI’s intelligence grows more robust and unbiased. There is also a trend toward AI creating data for training, via simulations or generating synthetic examples, to overcome data bottlenecks. All these efforts mean that future AI will not be starved for information to learn from – one of the key drivers of the rapid progress we’ve seen and one that should continue to propel AI IQ upward.
Role of Quantum AI Integration
Quantum computing is expected to be a game-changer for certain types of computations, and its integration with AI could significantly boost growth in the coming decades. Quantum computers leverage phenomena like superposition and entanglement to process information in ways classical computers cannot. By 2025, quantum technology is still nascent (small numbers of qubits, error- prone), but looking out to 2050–2075, we anticipate mature quantum computers that can tackle massively complex calculations. The synergy of AI with quantum computing – often termed Quantum AI – could enable breakthroughs not possible with classical computing alone.
To clarify, quantum computing will not replace classical AI; rather, it will augment it. Each has different strengths: today’s AI excels at pattern recognition, intuition, and tasks like language or image processing, whereas quantum computers excel at exploring huge combinatorial spaces and solving problems with immense possible outcomes (rolandberger.com).
Combined, they form a powerful hybrid. As one analysis put it, “the two technologies have very different strengths and therefore lend themselves to different use cases”, suggesting a complementary relationship rather than a replacement (rolandberger.com).
Here are the key ways quantum integration will impact AI:
- Faster and Better Optimization: Many AI challenges boil down to optimization – whether tuning the parameters of a model or making decisions in a complex environment. Quantum algorithms like Grover’s search and quantum annealing can in theory find optimal solutions much faster for certain problem types by examining multiple possibilities in parallel superposition. By 2050+, if large-scale stable quantum computers are available, AI systems could offload heavy optimization tasks to quantum co-processors. For example, a quantum- enhanced AI might solve complex routing, scheduling, or resource allocation problems orders of magnitude faster than a classical AI. Machine learning training itself can be seen as optimizing a high-dimensional function; quantum optimization methods might speed up finding the best model or hyperparameters. This could dramatically shorten training times for very big models, enabling more rapid iteration and growth in AI capabilities.
- Quantum-Accelerated Machine Learning: A new field of quantum machine learning (QML) is developing algorithms that run on quantum hardware to perform ML tasks more efficiently. There are quantum versions of classical algorithms (quantum support vector machines, quantum principal component analysis, etc.) that promise exponential speed-ups under certain conditions. In the next decades, we might see quantum neural networks – models that use quantum circuits as layers or employ qubits to represent and entangle features. These QNNs could potentially recognize patterns with far fewer computational steps than classical networks by exploiting quantum parallelism. Early research has shown quantum-inspired techniques can make machine learning more efficient, such as quantum methods that reduce the number of input features needed for image recognition (rolandberger.com).
Likewise, AI can help improve quantum computing (e.g. using machine learning to optimize error-correction in quantum circuits) (rolandberger.com), creating a virtuous cycle. By 2075, a hybrid quantum-classical AI system might use classical processors for tasks like perception and language (where data and model sizes are huge and error tolerance is high) and switch to quantum processors for solving mathematically hard sub-problems or simulations that are intractable for classical machines.
- Enhanced Problem-Solving in Science and Engineering: Certain problems will be specifically revolutionized by quantum computing, which in turn amplifies AI’s impact in those areas. One example is cryptography and security: Quantum computers can break current encryption, which will push AI to develop new encryption methods and security protocols (and AI itself might leverage quantum-based cryptography for secure decision- making channels). In drug discovery and materials science, quantum computers can accurately simulate quantum systems (molecules, chemical reactions) that classical computers approximate poorly. AI algorithms excel at searching design spaces (for new drug molecules or material configurations); a quantum-enhanced AI could evaluate each candidate using a quantum simulation, vastly improving the discovery process. Similarly, in
fields like physics simulations, climate modeling, or any domain dealing with extremely complex systems, quantum computing can crunch the underlying calculations while AI interprets the results and guides the search. For instance, AI might propose a hypothesis for a new high-temperature superconductor and use a quantum computer to test its properties virtually, something impossible to do efficiently today. Such quantum-classical collaboration would allow tackling “impossible” problems, effectively raising the ceiling of what AI can solve.
- Quantum Natural Language and Reasoning: Interestingly, researchers are even exploring quantum-enhanced cognition for AI. Some aspects of language understanding and reasoning remain difficult for classical computers; there is speculation that quantum natural language processing or quantum cognitive algorithms could open new pathways (rolandberger.com).
This is highly experimental, but by 2075 we might discover that certain reasoning tasks map naturally onto quantum processes, giving a further boost to AI’s “intelligence.” For example, a quantum AI could potentially hold multiple contradictory hypotheses in superposition and evaluate them simultaneously, mimicking a very powerful form of reasoning or creativity that classical AIs would struggle to do efficiently.
In summary, quantum computing integration could be a significant force-multiplier for AI, especially in the latter half of the 2025–2075 period as quantum hardware matures. It will likely most benefit specific problem classes – particularly optimization, search, and any computational task with combinatorial explosion. Initial research already shows encouraging hints of these benefits (rolandberger.com).
That said, current results are mostly proofs of concept; as experts note, this is an early phase of hybrid quantum-AI research with a lot of excitement but also uncertainty about ultimate outcomes (rolandberger.com)
By 2075, in an optimistic scenario, we’ll have AI systems seamlessly using quantum resources behind the scenes, making them far more powerful than AI running on classical hardware alone. In a more conservative scenario, quantum computing might remain limited or only useful for niche applications, leaving mainstream AI to continue chiefly on classical improvements. But given the massive investments and steady progress in quantum tech, a quantum-enabled AI superintelligence by 2075 is within the realm of possibility.
Realistic Future Projections and Challenges
What might AI look like by 2075, and could it reach superintelligence (vastly surpassing human intellect in all domains)? Expert opinions vary widely, reflecting both optimism and caution. Many AI researchers do believe extremely powerful AI is plausible within decades: surveys of experts in 2022 found over half believe there’s a 50% chance of human-level AI (able to do most jobs as well as a human) by around 2060 (ourworldindata.org).
Visionaries like Ray Kurzweil famously predict aggressive timelines – he anticipates AI will pass the Turing test (a proxy for human-level intelligence) by 2029 and that by 2045 we’ll reach a “Singularity,” where AI surpasses human intelligence so profoundly that it transforms civilization (aimyths.org). Kurzweil imagines humans merging with AI and “multiplying our effective intelligence a billion fold” by mid-century (aimyths.org).
On the other hand, some experts argue these timelines are too optimistic. The median estimate in several expert surveys still places a 50% chance of AGI only in the 2060s, with a non-trivial probability that it takes much longer or never achieves fully general human-level abilities (ourworldindata.org)
Figures like Nick Bostrom have discussed superintelligence as a theoretical eventuality but refrain from pinpointing a date, noting it could take many decades or more and would require breakthroughs like whole brain emulation that are not yet on the horizon.
Given these perspectives, a realistic projection is that by 2075 we will likely have AI systems that far exceed human capabilities in many narrow domains and are at least comparable to humans in most general intellectual tasks. In other words, Artificial General Intelligence might well be achieved within this period, although not everyone agrees on this inevitability. Whether these systems qualify as superintelligent (as defined by Bostrom: “any intellect that greatly exceeds the cognitive performance of humans in virtually all domains”) will depend on progress in areas like creativity, emotional/social intelligence, and the integration of different skills. It’s possible that by 2075, an AI (or network of AIs) could outthink the best humans in every field, from scientific research and engineering to strategic planning and art creation – essentially meeting the definition of superintelligence.
Implications of Superintelligence: If AI reaches or approaches superintelligence, the implications for humanity are enormous. On the positive side, such AI could be an unparalleled tool for solving problems that have long bedeviled us. A superintelligent AI might rapidly develop cures for diseases, find solutions to climate change, revolutionize energy production, and accelerate innovation in every field. OpenAI’s leadership has noted that superintelligence could help us solve many of the world’s most important problems (openai.com).
AI that vastly exceeds human intellect could design technology we can’t currently imagine – potentially ending scarcity, disease, even mortality (through medical breakthroughs) – essentially
enabling a post-scarcity society. Some futurists see this outcome as part of an inevitable progress curve: an “intelligence explosion” where AI improves itself and its creations exponentially, leading to a utopia (or at least a radically transformed world) in a short span of time (aimyths.org).
However, there is a very serious caveat: a superintelligent AI could also pose grave risks if not controlled or aligned with human values. An AI that is more intelligent than us in every way might develop goals misaligned with ours or pursue its given goals in unexpected, dangerous ways. The vast power of such an entity could, in the worst case, lead to human disempowerment or even extinction (openai.com)
This is why researchers emphasize the importance of AI alignment and safety in any long-term projection. As of today, experts acknowledge we do not yet know how to reliably control or align a superintelligent AI. Ilya Sutskever and Jan Leike of OpenAI stated in 2023, “Currently, we don’t have a solution for steering or controlling a potentially superintelligent AI, and preventing it from going rogue.” (openai.com).
They stress that techniques which work for aligning today’s systems (like training models with human feedback) may not scale to an AI that is smarter than all its human overseers (openai.com).
This alignment problem is one of the key challenges that must be solved to safely reap the benefits of advanced AI. If by 2075 superintelligent AI is on the table, hopefully equal effort has gone into creating robust controls and ethical frameworks to manage it.
Potential Roadblocks: It’s important to temper predictions of relentless AI progress with an understanding of the hurdles that could slow or derail it. Here are some major factors that could impede AI’s growth trajectory:
- Energy and Hardware Limitations: The computational feats driving AI’s rise come with astronomical energy costs. Training a single state-of-the-art model today can consume megawatt-hours of electricity. For example, training OpenAI’s GPT-3 (175 billion parameters) was estimated to use 1,287 MWh of electricity, emitting about 502 metric tons of CO₂ (news.climate.columbia.edu).
If we naively scale up to even larger models, the energy required would be enormous. By 2075, energy constraints or costs could become a limiting factor – we may simply not be able to power an “IQ 3000” AI with brute force computation unless we make dramatic gains in efficiency or have abundant clean energy. This is where hardware advances (as discussed) and more efficient algorithms are critical. Nevertheless, there is a physical limit to how many computations we can afford to do; without breakthroughs like sustainable supercomputing or low-power neuromorphic chips, AI progress might hit a ceiling due to power and cooling requirements. Data centers already account for about 2.5–3.7% of global emissions (news.climate.columbia.edu), and AI is a growing part of that. The sustainability of AI growth will need to be addressed to avoid an energy bottleneck.
- Data Bottlenecks and Quality: Today’s most capable AIs are trained on almost all available data of a certain kind (for instance, large language models have ingested essentially the entire public internet text). There may be diminishing returns as we exhaust readily available high-quality data. Future models might need to learn from subtler and more complex datasets (real-world sensor data, scientific data, etc.) where simply scaling quantity is hard. Moreover, as AI-generated content becomes widespread, ensuring models learn from reliable, diverse information (and don’t get caught in a feedback loop of regurgitated AI output) will be a challenge. AI might need to learn more like humans – via interactive experience or simulation – to keep improving, rather than just reading static datasets. If we fail to find new sources of knowledge or better ways for AI to acquire knowledge autonomously, progress could slow. In short, data might become a limiting resource, and the focus may shift from quantity to cultivating rich, novel data and experiences for AI to learn from.
- Algorithmic and Theoretical Challenges: Despite the successes of deep learning, current AI algorithms have known limitations. They can be brittle outside their training distribution, lack true common-sense understanding, and struggle with tasks that require long-term planning or abstraction. It’s possible that reaching human-level general intelligence (let alone superintelligence) will require new paradigms beyond today’s deep neural networks. If those new paradigms (for example, combining symbolic reasoning with neural nets, or new forms of memory and abstraction) prove difficult to discover, progress might plateau. Additionally, creating AI that can explain its reasoning, adapt on the fly with minimal data (like humans do), or that has human-like creativity and intuition remains hard. Each of these is an active research area, and breakthroughs are not guaranteed on a set timeline. There could be “AI winters” – periods where hype outpaces reality and funding diminishes – if, say, progress stalls due to one of these core challenges. History has seen cycles of optimism and disappointment in AI, and while current momentum is strong, it’s wise to acknowledge potential slowdowns.
- Societal, Ethical, and Regulatory Factors: How society chooses to deploy or restrict AI will influence its development. If early forms of advanced AI lead to incidents or misuse (for example, a serious accident with autonomous weapons or a catastrophic economic disruption), there may be public backlash or strict regulation that slows research. Governments might limit access to cutting-edge models for safety, much as nuclear technology is carefully controlled. International competition could either accelerate development (a technological arms race) or, conversely, treaties could emerge to limit certain high-risk research. Ethical considerations – such as the impact of AI on employment, privacy, or inequality – might lead to deliberate pumping of the brakes.
For instance, before developing AI that can replace most human jobs, societies might insist on frameworks to manage economic impacts. All these social factors inject uncertainty into AI’s growth. Unlike a purely technical curve, real-world adoption can be nonlinear. Ensuring trust and safety may require moving slower at times. On the flip side, wide public support
and demand for AI solving big problems could accelerate funding and progress – but the key point is that societal context could either be a roadblock or a catalyst.
- Quantum and Engineering Hurdles: While we highlighted quantum computing as a potential accelerant, it too faces tough roadblocks. Building large-scale, error-corrected quantum computers is an enormous engineering challenge. If quantum hardware doesn’t mature as expected (or is delayed well past 2050), one pillar of our optimistic AI projections would be weakened. Similarly, even classical semiconductor tech might encounter physical limits (quantum tunneling, etc.) that drastically slow hardware improvements. We may then rely on innovation in algorithms to sustain progress. If both hardware and algorithmic improvements slow down together, AI’s growth could hit an extended plateau.
In conclusion, the next 50 years (2025–2075) are poised for remarkable advances in AI, potentially taking us from the current narrow (albeit impressive) AI achievements to human-level AI and possibly into the realm of superintelligence. The “IQ” of AI systems could skyrocket by human standards, driven by better architectures, more compute (aided by quantum technology), clever algorithms, and mountains of data. We’ve seen that optimism abounds – with predictions of transformative AI within a few decades – but we’ve also seen reasons for caution – technical, practical, and ethical constraints that might slow the journey. As we project this future, it’s critical to pursue not just raw intelligence growth but wise growth: ensuring that as AI becomes more intelligent, it remains aligned with human values and is developed in a sustainable, controlled way.
The long-term potential of AI is vast and breathtaking, but realizing it will require navigating the challenges and uncertainties with care. With balanced progress, by 2075 we could be living in a world alongside machines whose intellect dwarfs our own in some ways – a world of great opportunity, so long as we have managed to keep that intelligence beneficial and under control (aimyths.org; openai.com).
The coming decades will determine just how far we get on this path and whether the outcome is utopian or dystopian – or simply a managed, collaborative evolution of intelligence. One thing is clear: the conversation between AI and humanity is only just beginning, and its trajectory will define the future of our civilization.