As AI systems reshape our world, a lesser-known term is emerging in technical circles—but what separates “artificial” from “synthetic” intelligence, and does the distinction matter?

In boardrooms, laboratories, and policy chambers worldwide, artificial intelligence has become the defining technology of our era.

According to Grand View Research the global AI market reached $196.63 billion in 2023 and is projected to expand at a compound annual growth rate of 37.3% through 2030,

Yet as the technology permeates everything from healthcare diagnostics to financial trading, a parallel term has surfaced in technical discourse: synthetic intelligence.

For most observers, the terms appear interchangeable—both describing machines that perform tasks typically requiring human cognition.

But computer scientists, philosophers, and AI researchers increasingly argue that conflating these concepts obscures fundamental differences in how we build, deploy, and understand intelligent systems.

Defining the Divide –

The Difference Between Artificial Intelligence & Synthetic Intelligence

Artificial intelligence, the umbrella term that has dominated public consciousness since John McCarthy coined it at the 1956 Dartmouth Conference, refers broadly to machines capable of performing tasks that normally require human intelligence.

This encompasses everything from the recommendation algorithms on Netflix to the large language models powering chatbots to the computer vision systems in autonomous vehicles.

The AI industry now employs approximately 4.5 million people globally, according to 2024 figures from the International Labour Organisation, while AI-related patent filings increased by 62% between 2019 and 2023, per the World Intellectual Property Organisation.

Synthetic intelligence, by contrast, represents a more specific philosophical and technical framework, rather than simply mimicking human cognitive outputs, synthetic intelligence describes systems built from the ground up to replicate the underlying processes and architectures of biological intelligence.

The Engineering Philosophy

Current AI systems—particularly the deep learning neural networks that have driven recent breakthroughs—operate through statistical pattern recognition.

These systems analyze vast datasets to identify correlations and make predictions. GPT-4, the large language model released by OpenAI in 2023, was trained on approximately 13 trillion tokens of text data, according to industry estimates.

These models have achieved remarkable performance benchmarks.

In medical imaging, AI diagnostic systems now match or exceed radiologist accuracy in detecting certain cancers, with one 2024 study in Nature Medicine showing 94.5% accuracy in breast cancer screening across a dataset of 25,000 mammograms.

In protein folding, DeepMind’s AlphaFold has predicted structures for over 200 million proteins, work that would have taken human researchers centuries.

Yet these systems operate fundamentally differently from biological brains. They lack the neuroplasticity, energy efficiency, and general reasoning capabilities of even simple biological organisms.

The human brain operates on roughly 20 watts of power—about the same as a dim light bulb—while training GPT-3 reportedly consumed 1,287 megawatt-hours, equivalent to the annual electricity consumption of approximately 120 US homes.

Synthetic intelligence approaches the problem differently.

Rather than training statistical models on data, synthetic intelligence research attempts to recreate the computational principles underlying biological cognition: neuronal dynamics, synaptic learning rules, hierarchical processing architectures, and embodied sensorimotor interaction with environments.

The Neuromorphic Frontier

The most concrete manifestation of synthetic intelligence appears in neuromorphic computing—hardware specifically designed to mimic the structure and function of biological neural networks.

Unlike traditional computers that separate memory and processing, neuromorphic chips integrate these functions as biological neurons do.

Intel’s Loihi 2 neuromorphic research chip, released in 2021, contains 1 million artificial neurons and 120 million synapses, operating at energy levels 1,000 times lower than conventional processors for certain tasks.

IBM’s TrueNorth chip, containing 1 million programmable neurons and 256 million configurable synapses, consumes just 70 milliwatts during operation.

The neuromorphic computing market, valued at $6.34 billion in 2023, is projected to reach $34.89 billion by 2030, according to Fortune Business Insights—a growth rate reflecting both technical progress and recognition of current AI’s limitations.

Performance and Limitations

The practical differences emerge starkly in real-world applications. Current AI excels at narrow, well-defined tasks with abundant training data but struggles with:

• Generalisation: AI systems trained on specific datasets often fail catastrophically when conditions change. A 2023 Stanford study found that autonomous vehicle systems trained in California performed 43% worse when tested in different weather conditions and road infrastructures.
  
  
• Energy efficiency: Training a single large language model can generate 626,000 pounds of CO2 equivalent, according to research from the University of Massachusetts Amherst—roughly equal to the lifetime emissions of five average cars.
  
  
• Robustness: Adversarial attacks can fool state-of-the-art image recognition systems with imperceptible pixel changes. Research published in Science in 2024 demonstrated that adding carefully crafted noise to images caused leading AI systems to misclassify objects with 97% success rates.
  
  
• Common sense reasoning: Despite impressive language capabilities, AI systems lack basic physical and social understanding. They cannot reliably answer simple questions requiring implicit world knowledge that any human child possesses.

Synthetic intelligence approaches promise advantages in these areas by incorporating biological principles: continuous learning without catastrophic forgetting, extreme energy efficiency, robustness through redundancy, and emergent common sense from sensorimotor grounding.

However, synthetic intelligence remains largely experimental. No synthetic system has yet matched the scale or practical utility of leading AI applications. The human brain contains approximately 86 billion neurons and 100 trillion synapses—orders of magnitude beyond current neuromorphic chips.

The Investment Landscape

Despite these challenges, investment in synthetic intelligence and neuromorphic computing is accelerating.

The European Union’s Human Brain Project, a ten-year initiative that concluded in 2023, invested €607 million in brain simulation and neuromorphic computing. China’s Brain-Inspired Computing Research Center received $1.2 billion in government funding through 2025.

Major technology companies are hedging their bets. Intel maintains dedicated neuromorphic research divisions.

IBM’s neuromorphic computing group collaborates with universities worldwide. Even OpenAI, synonymous with conventional AI approaches, has begun exploratory research into biologically-inspired architectures.

This represents a recognition that current AI, despite revolutionary achievements, may not constitute the final word in machine intelligence.

Philosophical Implications

Beyond technical distinctions, the artificial versus synthetic divide raises profound questions about consciousness, understanding, and the nature of intelligence itself.

Philosopher John Searle’s famous Chinese Room thought experiment, proposed in 1980, argued that symbol manipulation—no matter how sophisticated—does not constitute genuine understanding.

Current AI systems, which process information through statistical associations without semantic comprehension, arguably exemplify this limitation.

Synthetic intelligence, by attempting to recreate the physical substrate of biological cognition, opens different philosophical territory.

If a system not only produces intelligent outputs but does so through mechanisms genuinely analogous to biological brains, does it possess something closer to genuine understanding?

Regulatory and Ethical Considerations

These distinctions carry practical policy implications. The European Union’s AI Act, which entered into force in August 2024, regulates AI systems based on risk categories but does not distinguish between artificial and synthetic approaches. Some researchers argue this overlooks important differences.

Synthetic intelligence systems, designed to operate more like biological brains, might exhibit more predictable failure modes, greater interpretability, and easier alignment with human values—or they might pose novel risks precisely because they more closely approximate human-like cognition, including its biases and limitations.

The U.S. National Artificial Intelligence Initiative, with a 2024 budget of $1.5 billion, funds both conventional AI and neuromorphic research, but policy frameworks remain heavily weighted toward addressing conventional AI systems.

The Convergence Question

Increasingly, researchers question whether artificial and synthetic intelligence represent divergent paths or convergent approaches to the same destination.

Recent developments in AI—including attention mechanisms in transformer models, multi-modal learning, and embodied AI systems—incorporate principles long advocated by synthetic intelligence researchers. Meanwhile, neuromorphic systems increasingly utilize training techniques developed for conventional AI.

By 2023, approximately 23% of AI research papers published in leading journals incorporated some biologically-inspired components, according to analysis by the Allen Institute for AI—up from just 11% in 2018.

Looking Forward

As AI capabilities continue their exponential growth—with global AI computing power doubling approximately every six months according to OpenAI’s analysis—the artificial versus synthetic distinction may become increasingly consequential.

The path toward artificial general intelligence (AGI)—systems with human-level intelligence across all cognitive domains—remains uncertain.

Current AI approaches, despite remarkable achievements, show no clear trajectory toward general intelligence. Synthetic intelligence offers an alternative route, though one still in its infancy.

What remains clear is that “artificial intelligence” as commonly understood describes a diverse ecosystem of approaches, philosophies, and implementations.

As these technologies reshape economies, societies, and perhaps human cognition itself, precision in terminology and understanding grows ever more vital.

The question may not be whether artificial or synthetic intelligence will prevail, but whether their synthesis produces something neither approach could achieve alone—a genuinely new form of intelligence drawing on both silicon and the lessons of biology.

For now, artificial intelligence dominates commercial applications and captures public imagination, while synthetic intelligence remains primarily an ambitious research program.

But as the limitations of current approaches become apparent and the promise of neuromorphic computing grows clearer, the intelligence divide may define the next era of cognitive technology.

The machines we build will reflect not just what we can engineer, but what we believe intelligence truly is—artificial mimicry or synthetic recreation, or perhaps ultimately, something entirely new.