Yes, AI is cheaper than humans in many situations

The costs of artificial intelligence (AI) systems versus human workers is a complex issue with many nuances. However, in many common business use cases, AI solutions can indeed provide significant long-term cost savings compared to humans performing the same tasks.

Key Reasons AI is Cheaper Than Humans

Here are 5 key reasons why AI is generally cheaper than human labor:

1. AI has lower ongoing costs – Once an AI system is built, the marginal cost per automated task is negligible compared to human labor costs.

2. AI excels at high volume repetitive tasks – The more repetitive a task is, the greater cost savings AI provides over humans.

3. AI needs no salaries or benefits – Unlike human workers, AI solutions require no health insurance, payroll taxes, pensions, or other costs.

4. AI can operate 24/7 – AI systems can work around the clock without shift breaks, vacation days, or sick time.

5. AI increases worker productivity – AI augments humans, allowing workers to get more done rather than directly replacing them.

Now let‘s explore the details and data behind the economics of AI versus human workers.

High Upfront Costs, But Low Operating Costs

Developing AI solutions requires major upfront investments, often costing over $100,000 for enterprise systems. But once deployed, marginal costs per task are minimal.

For example, according to research from OpenAI, the computational power required to train a large language model in 2018 cost about $3.65 per 1,000 words generated. In 2020, costs dropped to just $0.59 per 1,000 words, a 6x cost improvement in just 2 years.

Once a language model like GPT-3 is trained, generating text costs essentially nothing beyond minimal electricity and cloud computing fees.

Let‘s compare that to human writing costs. The average freelance writing rate is $0.10-0.30 per word according to ZipRecruiter. For a 5,000 word blog article, AI generation would cost $3-10 versus $500-1,500 for a human writer. The AI completes the work 50-100x cheaper in this example.

And that comparison is for a single one-time project. The cost savings compound dramatically when AI automates tasks at massive scale over years of continuous operation.

Ideal for Repetitive, Rules-Based Tasks

AI shines when automating repetitive tasks with clear underlying patterns and logic. Structured data and rules-based processes are what AI models excel at learning and mimicking.

Use cases that involve high volumes of recurring decisions, predictions, classifications, recommendations, or translations are ideal for AI automation. For example:

• Customer service – Processing common account inquiries and handling routine requests. AI chatbots can respond 20x faster than humans according to PwC.

• IT support – Diagnosing and fixing standard technical issues like network errors or password reset requests.

• Content moderation – Reviewing massive volumes of content like social media posts or product listings to identify policy violations.

• Predictive analytics – Forecasting future outcomes like customer churn, inventory demand, or credit risk.

• Process automation – Streamlining workflows by automatically executing repetitive digital clerical tasks across applications.

The more rules-based and repetitive the task, the greater efficiency gains AI provides over human capabilities.

Dramatically Lower Overhead Costs

Employing human workers involves major overhead costs that simply don‘t apply to AI solutions.

According to the Bureau of Labor Statistics, employee benefits account for over 30% of total compensation for civilian workers in the United States. These include healthcare, retirement contributions, paid leave, and other perks. Employers also pay 7.65% of wages into Social Security and Medicare.

In addition, human workers require management time for recruiting, onboarding, training, performance reviews, and HR administration. Employees also need office facilities, equipment, software licenses, security, compliance monitoring and other infrastructure.

AI solutions eliminate nearly all these overhead costs associated with human workers. After deployment, AI systems can scale and grow with minimal incremental overhead. This gives AI a massive advantage in cost-efficiency as automation increases.

Always-On Capacity

Humans can work a maximum of 40-50 hours per week, less vacation, sick time, and breaks. In contrast, AI solutions can operate 24/7/365 with no downtime.

Let‘s say a business needs phone support covered from 8am – 8pm daily. Humans working in shifts would require 3 full-time staff. An AI chatbot could handle the exact same workload with no downtime using a small fraction of the computing resources.

The more uptime required for a task, the greater the potential for AI to reduce labor costs through around-the-clock operation.

AI Augments Humans

In most cases, AI will enhance human productivity rather than directly replace workers. According to a McKinsey survey, less than 5% of occupations could be fully automated using current AI technology.

But most occupations could automate 30-70% of tasks, allowing humans to focus on the work only they can do. For example:

• In recruiting, AI handles sourcing and screening applicants so recruiters can focus on interviews and hiring decisions.

• For customer support, chatbots handle routine inquiries while human agents provide specialized expertise.

• In radiology, AI flags anomalies in scans so doctors can better diagnose conditions.

• In factories, robots take over dangerous repetitive work so employees can manage higher-level production processes.

Rather than a pure replacement, AI amplifies human strengths and mitigates human limitations. Combining the best of both produces overall labor cost savings.

Limitations of AI Solutions

While AI certainly provides major cost benefits in many situations, it has limitations that can make human workers a better choice in certain cases.

Unlike people, today‘s AI lacks cognitive abilities like reasoning, common sense, creativity, and empathy. AI solutions must be narrowly customized to solve one specific problem in one domain. General intelligence remains elusive.

Humans also have unique capabilities like dexterity, mobility, and social skills that make them irreplaceable for any customer or employee-facing role. People are also better at handling novel, unpredictable situations.

In addition, AI has no self-awareness or ability to self-improve. Maintaining and updating AI systems requires ongoing human supervision, testing, bug fixing, and model retraining.

Finally, in situations where only a low volume of tasks need completion, the high fixed overhead of developing and deploying an AI solution likely won‘t justify the upfront investment required.

For now and the foreseeable future, many business functions still require uniquely human traits and domain expertise. But there is ample opportunity for AI and humans to productively co-exist.

When Does AI Become More Cost-Effective?

Determining the exact breakeven point where AI becomes cheaper than human labor is challenging. The crossover point varies dramatically based on factors like:

• Upfront AI development costs
• Number of tasks/transactions automated
• Cost of human labor for the equivalent task
• Related overhead costs like infrastructure and management

In simple terms, dividing the upfront AI cost by the potential annual savings per task automated provides a rough payback period estimate.

For example, if an AI system costs $100,000 to build and saves $10,000 annually by automating a task, the payback period is 10 years. That may not be compelling.

But if the same $100,000 AI system saves $1 million annually by automating high volumes of tasks, the payback is just 1 month.

The chart below illustrates how the payback period shortens as task volume increases:

Note: Chart is for illustrative purposes only and not exact costs. Assumes AI system lasts 5 years.

The key takeaway is that for high volume repetitive tasks, AI becomes cheaper than humans fairly quickly. But for low task volume, it may never break even.

Optimizing an AI and Human Workforce

The best path forward is not AI or humans – it‘s optimizing both together.

By combining the scalable task automation of AI with human skills like creativity, emotional intelligence, and troubleshooting, companies can achieve far greater performance at lower costs.

Some best practices to optimize an AI-human workforce partnership include:

• Let AI handle high volume repetitive tasks to free up human capacity for higher value work.

• Have humans provide personalized service for complex, non-routine customer issues.

• Use human oversight to validate AI decisions and catch errors.

• Implement AI to surface insights from massive datasets that humans could never analyze on their own.

• Maintain open communication between AI and humans to build transparency and trust.

• Provide AI training data and feedback to continuously improve its decision making.

• Keep humans involved in creative vision, strategy, ethics and governance.

With thoughtful integration, AI automation and human talent can complement each other far better than either could individually.

Key Takeaways

• AI has higher fixed costs but lower variable costs than human workers for many common business use cases.

• AI is ideal for automating repetitive, rules-based tasks at high volumes.

• The breakeven point depends on upfront AI costs and the savings per task automated.

• For maximum cost-effectiveness and performance, companies should optimize how AI and human workers collaborate.

The question of "is AI cheaper than humans" requires nuanced analysis of multiple factors. But in many situations, AI can indeed deliver significant labor cost reductions and productivity improvements. Companies who strategically leverage AI can gain competitive advantage through reduced expenses and optimized human workforces.