By James Eliot, Markets & Finance Editor
Last updated: June 04, 2026

5 Reasons AI Is Not Conscious — Ted Chiang’s Controversial Take

The belief that artificial intelligence (AI) has achieved consciousness has permeated investment strategies and public discourse alike. Yet, as author Ted Chiang argues, this notion is fundamentally flawed. In a recent article for The Atlantic, Chiang dismantles the misconceptions surrounding AI consciousness, highlighting how attaching this label misrepresents AI’s current capabilities and has dire implications for ethics and investments.

In a world where every new AI development prompts either excitement or disillusionment, understanding the distinction between human cognition and machine computation is crucial for professionals navigating the financial landscape. Chiang’s insights could spell significant changes in how businesses integrate AI into their operations.

What Is AI Consciousness?

AI consciousness refers to the hypothetical ability of artificial intelligence systems to possess self-awareness and subjective experiences akin to humans. While the idea captivates the imagination, it oversimplifies the complexities of human thought and emotion. Attributing consciousness to AI mistakenly elevates algorithmic performance to a level of meaning it does not actually possess, akin to mistaking a calculator’s outputs for comprehension of mathematics.

This misunderstanding matters now more than ever as companies pour investments into AI with inflated expectations, as seen in discussions surrounding the necessity of AI governance frameworks.

How AI Consciousness Works in Practice

The technologies that people often misconceive as “conscious” are, in reality, powerful algorithms calculating probabilities and generating responses based on input data.

1. OpenAI’s ChatGPT: OpenAI’s flagship model, ChatGPT, engages in conversations that mimic human interaction. Yet, this interaction is purely algorithmic. In a Stanford study, it was found that while ChatGPT performs well on various tasks, it lacks any self-awareness, estimating it engages in roughly 100 million conversations a month, without any true understanding of language.

2. Amazon’s Hiring Algorithms: In 2018, Amazon abandoned an AI recruitment tool that demonstrated bias against women. The algorithm, trained on past resume submissions, discovered patterns that led to it favoring male-centric language, a clear reflection of human biases rather than independent decision-making. The company ultimately recognized that AI’s “intelligence” on hiring decisions often led to flawed outputs, making the case for improving AI systems as discussed in resources on AI ethics.

3. Theranos: The infamous health tech startup raised $700 million based on inflated promises regarding its capabilities. While claiming to have developed a revolutionary blood-testing technology, it relied on faulty results and misrepresentations. Such blindness to the limitations of technology underscores a common mistake: confusing AI capabilities with significant understanding, akin to attributing sentience to a malfunctioning machine.

These examples illustrate how mistakes in interpreting AI’s capabilities can lead to disastrous outcomes, both ethically and financially.

Top Tools and Solutions

Given the pitfalls of misattributing consciousness to AI, here are some practical tools that facilitate better decision-making in the AI landscape:

Gamma — AI-powered presentation and document builder perfect for professionals needing to create impactful presentations quickly.

Instantly — A cold email outreach and lead generation platform ideal for marketers looking to enhance their engagement strategies.

Catalister — Product catalog and listing management platform designed for e-commerce businesses to streamline their offerings.

AWeber — Professional email marketing and automation platform with AI-powered email writing capabilities for businesses seeking improved communication.

Trainual — Business playbook and employee training platform great for organizations looking to enhance their onboarding processes.

HighLevel — All-in-one sales funnel, CRM, and automation platform designed for agencies and entrepreneurs to boost their productivity.

Common Mistakes and What to Avoid

Mistakes surrounding AI’s perceived consciousness are widespread and can have serious consequences. Here are three notable examples:

1. Misleading AI Capabilities: Tech companies often misrepresent AI’s understanding, resulting in products like Theranos. The company claimed to produce accurate and fast blood tests but could not deliver on these promises because its technology was misunderstood. This led to fraud allegations and the eventual dissolution of the company.

2. Ethical Blind Spots: Amazon’s hiring algorithm continued to reflect biases present in historical hiring data, ultimately resulting in the cancellation of the tool. Misjudging the outputs of such algorithms created an image of a fair recruitment process, misleading both candidates and recruiters.

3. Over-Dependence on AI in Decision-Making: Many businesses believe that advanced AI tools can autonomously drive their strategies, neglecting to apply human oversight. This blind trust can lead to significant losses, as evidenced in various sectors relying on imperfect technologies that they interpret as infallible.

Understanding these common pitfalls is essential for investors and business leaders to safeguard their strategies in the evolving world of artificial intelligence.

Where This Is Heading

Investments in AI will continue to surge over the next decade, but the path is fraught with challenges. Three key trends include:

1. Greater Regulatory Oversight: Companies will face increasing scrutiny as governments recognize the ethical implications of AI technologies. This could lead to compliance costs and impact operational frameworks. Analysts expect regulations to formalize by 2025, pushing companies to rethink operations.

2. Focus on Explainability: The demand for transparency in algorithmic decision-making will drive companies to adopt AI solutions that can explain their outputs in human terms. A 2023 report from Goldman Sachs predicts that the explainability sector will see significant growth as stakeholders push back against opaque algorithms.

3. Shift Toward Hybrid AI Models: Businesses will increasingly blend human intelligence with AI capabilities to improve accuracy and efficacy. As FAANG companies develop their frameworks, the analytics market will expand to see a 40% growth in demand for explainable AI solutions.

FAQ

Q: What is AI consciousness?
A: AI consciousness refers to the hypothetical ability of AI systems to possess self-awareness and subjective experiences like humans. However, current AI lacks true self-awareness despite its sophisticated algorithmic capabilities.

Q: How do I implement AI in my business?
A: To implement AI in your business, start by identifying processes that can benefit from automation. Choose AI tools that align with your business goals, and gradually integrate them while training your staff to use these technologies effectively.

Q: How does AI compare to human intelligence?
A: AI excels in processing data and performing repetitive tasks, but it lacks emotional understanding and consciousness, which are integral to human intelligence. This fundamental difference affects how each can be applied in decision-making processes.

Q: What is the cost of implementing AI solutions in a company?
A: The cost of implementing AI solutions can vary widely based on the application’s complexity and the technology used. Initial investments may range from thousands to millions, depending on the scale of the project and ongoing maintenance required.

Q: What are some advanced AI implementations in industry?
A: Advanced AI implementations include predictive analytics for inventory management, personalized customer service chatbots, and AI-driven financial forecasting tools that help businesses make data-informed decisions.

Q: What common mistakes do companies make with AI?
A: Companies often misjudge AI capabilities, overestimating how much autonomy AI systems can provide. This can lead to failures in product delivery and ethical oversights if not monitored properly.

Q: What is the future trend of AI in business?
A: The future trend of AI in business is likely to focus on explainability and hybrid models that combine human intelligence with AI for improved decision-making and operational efficiency.

Q: What is the best AI tool for small businesses?
A: For small businesses, tools like Gamma for presentations or AWeber for email marketing provide user-friendly, powerful solutions without requiring extensive resources or expertise in AI.