ChatGPT-4: Yes, there's a strong argument to be made that AI has the potential to be more profitable for vendors than many previous technologies, primarily due to the reasons you've outlined

Question 1 of 5 for AI services: One of the most frequently used arguments in relation to the benefits of using Artificial Intelligence would be that it would save businesses and individual consumers a lot of money. But isn't it true that looking back at the last two decades, IT solutions have rarely been able to save people and businesses any costs? Governments too have been experiencing serious cost issues due to cyber security problems, outages, technology (or the required expertise) becoming obsolete, lack of resources relevant to customer care and then there are the costs related to hosting, network redundancy and cloud and domain management services. Isn't it true that in most cases, Information Technology will not be a cost-saving innovation and same will most likely go for Artificial Intelligence, which is already a lot more costly than any of its technological predecessors...

ChatGPT-4’s answer:The argument you've presented is a nuanced one and touches upon several facets of the IT and AI industry. Let's break it down:

1. Cost Savings with AI and IT: It's true that one of the primary arguments in favor of AI is its potential for cost savings. For instance, automation can reduce the need for manual labor in certain tasks, predictive analytics can help businesses make more informed decisions, and chatbots can handle customer queries without human intervention.

2. Past IT Solutions and Costs: Over the past two decades, IT solutions have indeed introduced new costs. Implementing and maintaining IT infrastructure, training staff, and ensuring cybersecurity can be expensive. However, it's essential to weigh these costs against the benefits. For many businesses, the advantages of digitization — such as increased efficiency, the ability to reach a global market, and improved data analysis — have outweighed the costs.

3. Government and Cybersecurity: Governments worldwide have faced challenges with cybersecurity, and the costs associated with breaches can be significant. However, this is a risk management issue rather than an inherent flaw with IT. As technology evolves, so do the threats, and it's crucial for governments and businesses alike to invest in robust cybersecurity measures.

4. Obsolescence and Expertise: Technology becoming obsolete is a genuine concern. Rapid technological advancements mean that systems and skills can become outdated quickly. Continuous training and system upgrades are necessary, introducing additional costs.

5. Customer Care and Other Costs: While IT can introduce new costs, such as hosting and domain management, it can also lead to savings in other areas. For example, digital platforms can reduce the need for physical storefronts, and cloud computing can be more cost-effective than maintaining on-premises servers for some businesses.

6. AI's Cost: AI, especially in its early stages, can be expensive. Training models require significant computational power, and hiring experts in the field can be costly. However, as with many technologies, the costs of AI are likely to decrease as the technology matures and becomes more widespread.

7. Future of AI and Cost Savings: It's hard to predict the future, but many believe that as AI becomes more integrated into various sectors, its potential for cost savings will become more apparent. For instance, in healthcare, AI-driven diagnostics might lead to earlier and more accurate disease detection, potentially saving on treatment costs in the long run.

In conclusion, while IT and AI introduce new costs, they also offer potential cost savings and other benefits. The key is to approach these technologies strategically, weighing the costs against the potential benefits and being prepared to adapt to a rapidly changing technological landscape.

Google Bard’s answer:It is true that IT solutions have not always been able to save people and businesses money, especially in the short term. However, there are many examples of IT solutions that have led to significant cost savings over time. For example, the cloud computing revolution has saved businesses billions of dollars on hardware and software costs. Additionally, IT solutions can help businesses to improve their efficiency and productivity, which can also lead to significant cost savings.

However, it is also true that IT can be expensive, especially when it is not implemented correctly. Governments and businesses can spend a lot of money on IT infrastructure, software, and personnel. Additionally, IT systems can be vulnerable to cyberattacks, which can lead to costly downtime and data breaches.

Overall, the impact of IT on costs is complex and depends on a variety of factors. However, there is no doubt that IT can be a powerful tool for cost savings, especially when it is implemented correctly.

As for artificial intelligence (AI), it is too early to say definitively whether it will be a cost-saving innovation. However, there are many potential ways in which AI could save businesses and individuals money. For example, AI could be used to automate tasks that are currently performed by humans, which could free up workers to focus on more productive activities. Additionally, AI could be used to improve the efficiency of business processes and to develop new products and services that are more cost-effective.

However, it is also important to note that AI is still a relatively new technology, and it can be expensive to develop and implement AI solutions. Additionally, there are ethical concerns surrounding the use of AI, which could lead to additional costs in the future.

Overall, the impact of AI on costs is likely to be mixed. Some businesses and individuals will likely save money by using AI, while others may experience increased costs. The net impact of AI on costs will depend on a variety of factors, including the specific AI solutions that are developed and implemented, the industries in which they are used, and the regulatory environment.

Here are some examples of how AI is already being used to save businesses money:

• AI is being used to automate customer service tasks, such as answering customer questions and resolving issues. This can save businesses money on labor costs.

• AI is being used to optimize supply chains and manufacturing processes. This can help businesses to reduce waste and improve efficiency, which can lead to significant cost savings.

• AI is being used to develop new products and services that are more cost-effective. For example, AI is being used to develop new drugs and medical treatments that are more effective and less expensive than existing treatments.

While AI is still a relatively new technology, it has the potential to save businesses and individuals a lot of money in the long term. However, it is important to note that AI is not a silver bullet, and it is important to carefully consider the costs and benefits of AI solutions before implementing them.

Bing Chat’s answer:Thank you for your question. I understand that you are interested in the cost-benefit analysis of artificial intelligence (AI) and whether it can save money for businesses and consumers. This is a complex topic that has many factors and perspectives to consider.

One of the most frequently used arguments in favor of AI is that it can automate tasks that are repetitive, tedious, or require high levels of accuracy and speed. This can potentially reduce labor costs, increase productivity, and improve quality. For example, AI can assist in the detection and grading of melanoma, dental caries, and diabetic retinopathy, which are common diagnostic procedures in dermatology, dentistry, and ophthalmology. A study found that AI can be more cost-effective than standard of care in these cases, depending on the willingness to pay and the accuracy of the AI system1.

Another argument for AI is that it can enhance decision making, innovation, and customer satisfaction. AI can analyze large amounts of data, identify patterns and insights, and generate recommendations or solutions. This can help businesses and consumers make better choices, optimize processes, and create new value. For example, AI can help small businesses adopt solutions that are tailored to their needs, goals, and resources2. AI can also help service providers deliver personalized and engaging experiences to their customers3.

However, there are also challenges and costs associated with AI adoption. Some of these include:

• The initial investment in hardware, software, data, and talent required to develop, deploy, and maintain AI systems. These costs can vary depending on the complexity, scale, and scope of the AI project.

• The potential risks of AI failures, errors, biases, or malicious attacks that can harm the performance, reputation, or security of the business or consumer. These risks can be mitigated by implementing proper governance, ethics, and quality assurance measures.

• The possible impacts of AI on the workforce, society, and environment. AI can create new jobs and opportunities, but it can also displace or transform existing ones. AI can also affect the social norms, values, and rights of individuals and groups. AI can also consume significant amounts of energy and resources that can affect the sustainability of the planet.

Therefore, the answer to whether AI will save money or not depends on how it is designed, implemented, and used. There is no one-size-fits-all solution for every business or consumer. Each case should be evaluated based on its own merits and trade-offs. A simple cost-benefit analysis may not capture all the nuances and implications of AI adoption. A more holistic approach that considers the economic, social, ethical, and environmental aspects of AI may be more appropriate.

I hope this helps you understand some of the issues involved in the cost-benefit analysis of AI. If you have any further questions or feedback, please feel free to ask me. 😊

Claude’s answer:You raise some thoughtful points about the costs associated with adopting new technologies like AI. A few perspectives on this:

• It's true that implementing any new technology often requires upfront investment and can introduce new complexities. However, when done right, AI has the potential to automate tasks, improve efficiency, and reduce costs over the long term. The key is proper planning, training, and integration.

• AI is still an emerging field. Like any new technology, costs are higher initially but tend to come down over time as tools and expertise become more widespread. The costs and benefits will likely evolve as the technology matures.

• Not all applications of AI are equally expensive. More ambitious projects like fully automating complex processes may require substantial investment, but many companies are finding value in more targeted AI applications that augment human capabilities. This can maximize benefits while minimizing cost and disruption.

• There are also indirect economic benefits of AI to consider. Even if direct cost savings are limited initially, AI can help drive revenue, productivity, innovation and growth in ways that are harder to quantify. This is a long-term game.

Overall there are always costs associated with adopting new technologies. AI is no different. But with careful implementation, many companies are finding it can control costs and provide enough upside to justify the investment. It's still early though, so the cost-benefit equation will likely shift over time as the technology evolves. Reasonable skepticism is warranted, but AI does hold promise if deployed thoughtfully.

Question 2 of 5 for AI services: List all factors that, in the past, have contributed to IT projects becoming way more expensive for governments, businesses and individual consumers than initially expected

ChatGPT-4’s answer:IT projects, especially large-scale ones, have a history of running over budget for various reasons. Here are some factors that have contributed to IT projects becoming more expensive than initially expected for governments, businesses, and individual consumers:

1. Scope Creep: This refers to the addition of new features, requirements, or tasks that were not part of the original project scope. As the project progresses, stakeholders might request new functionalities, leading to increased costs.

2. Poor Requirements Gathering: Inadequate or unclear requirements at the beginning of a project can lead to misunderstandings and the need for revisions later on, increasing costs.

3. Underestimating Complexity: Sometimes, the complexity of a project is underestimated, leading to more time and resources being required than initially planned.

4. Lack of Expertise: Not having the right team or expertise can lead to inefficiencies, mistakes, and the need for rework.

5. Technological Challenges: Unexpected technical challenges or the need to integrate with legacy systems can increase the time and resources required.

6. Vendor Issues: Reliance on third-party vendors can introduce delays and additional costs, especially if the vendor fails to deliver as promised or goes out of business.

7. Change in Technology Landscape: Rapid technological changes can render certain aspects of a project obsolete, requiring adjustments or shifts in direction.

8. Inadequate Testing: Insufficient testing can lead to the discovery of bugs or issues late in the project, which are often more expensive to fix than if they were identified earlier.

9. Poor Project Management: Lack of clear communication, inadequate resource allocation, and poor timeline management can lead to inefficiencies and increased costs.

10. Regulatory and Compliance Issues: Especially for governments and certain industries, changes in regulations or the need to meet specific compliance standards can introduce unexpected costs.

11. Economic Factors: Fluctuations in currency values, inflation, and other economic factors can impact the cost of resources, hardware, and third-party services.

12. Security Concerns: With the increasing importance of cybersecurity, ensuring that IT projects are secure can add to the costs, especially if vulnerabilities are discovered late in the project.

13. Training and Adoption: The need for extensive training for staff or end-users, or resistance to adopting the new system, can lead to extended timelines and increased costs.

14. Unforeseen Contingencies: Natural disasters, global crises (like pandemics), or other unexpected events can disrupt project timelines and increase costs.

15. Contractual Disputes: Disagreements or disputes over contracts, especially in projects involving multiple stakeholders or vendors, can lead to delays and legal costs.

16. Maintenance and Support: Sometimes, the costs of maintaining and supporting a new system, especially in its early stages, can be higher than anticipated.

In summary, while IT projects offer significant potential benefits, they also come with risks. Proper planning, clear communication, and effective project management are crucial to mitigating these risks and ensuring that projects stay on budget.

Google Bard’s answer:Here is a list of factors that have contributed to IT projects becoming more expensive than initially expected:

• Poor planning and estimation: This is one of the most common causes of IT project cost overruns. Projects are often poorly planned and estimated, which can lead to unforeseen problems and unexpected costs.

• Scope creep: This occurs when the scope of a project changes during development. This can happen for a variety of reasons, such as changes in customer requirements, new features being added, or unforeseen technical challenges.

• Unrealistic expectations: Some customers have unrealistic expectations about the cost and timeline of IT projects. This can lead to frustration and disappointment when the project goes over budget or takes longer than expected to complete.

• Technical complexity: IT projects can be very complex, and this complexity can lead to unexpected costs. For example, a project may require the use of new or unfamiliar technologies, or it may involve integrating multiple systems.

• Unforeseen problems: IT projects are often subject to unforeseen problems, such as hardware failures, software bugs, and natural disasters. These problems can cause delays and increase costs.

• Poor communication: Poor communication between stakeholders can lead to misunderstandings and delays. This can also lead to additional costs, as stakeholders may have to make changes to the project plan or requirements.

• Lack of resources: IT projects often require a significant investment of resources, such as people, equipment, and software. If a project does not have the necessary resources, it can lead to delays and increased costs.

• Changing market conditions: The cost of IT goods and services can fluctuate over time. If a project is delayed, it may cost more to complete due to changes in market conditions.

• Vendor lock-in: Vendor lock-in occurs when a customer is dependent on a particular vendor for software or services. This can make it difficult to switch vendors, which can lead to higher costs.

• Lack of expertise: IT projects can be complex, and they require the expertise of experienced professionals. If a project does not have the necessary expertise, it can lead to problems and increased costs.

It is important to note that these factors can interact with each other, and they can all contribute to IT project cost overruns. For example, poor planning and estimation can lead to scope creep, and unrealistic expectations can lead to poor communication.

Here are some additional factors that can contribute to the high cost of IT projects in the public sector:

• Complex procurement processes: Government procurement processes can be very complex and time-consuming. This can make it difficult to get IT projects underway and can lead to increased costs.

• Legacy systems: Government agencies often have legacy IT systems that are outdated and expensive to maintain. This can make it difficult to implement new IT solutions and can lead to increased costs.

• Political interference: IT projects in the public sector can be subject to political interference. This can lead to changes in requirements and priorities, which can cause delays and increase costs.

Overall, the high cost of IT projects is a complex issue with a variety of contributing factors. By understanding these factors, organizations can take steps to reduce the risk of cost overruns.

Bing Chat’s answer:There are many factors that can contribute to IT projects becoming way more expensive than initially expected. Based on the web search results, some of the common factors are:

• Unclear project scope: Having a scope that is not clear, which is ambiguous, or confusing is bound to cause issues. It can lead to misunderstandings, conflicts, rework, and scope creep, which are all sources of cost overruns12.

• Inappropriate activity estimation: Correct estimations are crucial in the project. Estimation about the activity duration, resources, costs, and risks can affect the project budget and schedule. If the estimations are too optimistic, unrealistic, or inaccurate, they can result in cost overruns12.

• Unanticipated risks: The project environment is dynamic and uncertain. There may be events or situations that can negatively affect the project objectives, such as technical issues, legal disputes, market changes, natural disasters, or human errors. These risks can cause delays, disruptions, or damages that can increase the project costs13.

• Contractor deficiencies: The contractor is responsible for delivering the project according to the agreed specifications, quality standards, and budget. However, if the contractor lacks the skills, experience, or resources to perform the project tasks effectively and efficiently, it can result in poor quality, errors, rework, or disputes that can escalate the project costs3.

• Owner intervention: The owner is the one who initiates and funds the project. The owner has the authority and interest to monitor and control the project progress and performance. However, if the owner intervenes too much or too little in the project activities, it can cause problems. For example, if the owner changes the scope or requirements frequently, it can cause rework and delays. If the owner delays the approvals or payments, it can cause cash flow issues and conflicts3.

• Equipment and material problems: The equipment and material are essential for executing the project tasks. However, if there are problems with the availability, quality, or delivery of the equipment and material, it can affect the project schedule and budget. For example, if the equipment breaks down or malfunctions, it can cause downtime and repairs. If the material is defective or delayed, it can cause rework and waste3.

Claude’s answer:Here are some common factors that can contribute to IT projects becoming more expensive than initially expected:

• Scope creep - Requirements change or expand over the course of the project, leading to unplanned work.

• Unrealistic estimates - The initial cost and timeline estimates were too optimistic.

• Integration issues - Challenges integrating new systems/tech with legacy systems.

• Customization - Extensive customization requires additional development and testing.

• Changing technologies - Advances render solutions obsolete, requiring upgrades/replacement.

• Lack of expertise - In-house teams lacking necessary technical skills and experience.

• Regulatory changes - New regulations require software/infrastructure changes.

• Poor planning - Failure to properly plan and manage all aspects of the project.

• Technical challenges - Unforeseen complexities in development and testing.

• Staff turnover - Loss of key staff and associated knowledge during project.

• Vendor issues - Vendors failing to deliver on schedule/budget.

• Security needs - Additional security measures needed to protect systems and data.

• Scalability issues - Systems unable to efficiently handle increased usage and data.

• Oversight/governance - Lack of oversight, poor resource allocation.

• Training costs - More extensive end user training required.

• Maintenance costs - Ongoing support and maintenance higher than expected.

Question 3 of 5 for AI services: Which of these will likely also be applicable to AI implementation and use?

ChatGPT-4’s answer:Many of the factors that have historically contributed to the increased costs of IT projects will also be applicable to AI implementation and use. Here's a breakdown of those factors:

1. Scope Creep: As AI projects progress, stakeholders might request new functionalities or modifications based on emerging data or changing objectives. This can lead to increased costs.

2. Poor Requirements Gathering: AI projects require clear objectives and understanding of the data. Inadequate requirements can lead to models that don't meet the desired outcomes, necessitating rework.

3. Underestimating Complexity: AI, especially deep learning models, can be complex. Underestimating the intricacy of model training or data preprocessing can lead to extended timelines and increased costs.

4. Lack of Expertise: AI is a specialized field. Not having the right expertise can result in inefficiencies, suboptimal model performance, and increased costs.

5. Technological Challenges: Integrating AI solutions with existing systems or dealing with large and complex datasets can introduce unexpected challenges.

6. Vendor Issues: Relying on third-party vendors for AI tools or platforms can introduce risks if the vendor's solutions are not up to par or if there are compatibility issues.

7. Change in Technology Landscape: The AI field is rapidly evolving. New algorithms, tools, or best practices can emerge, potentially making some aspects of a project outdated.

8. Inadequate Testing: AI models need rigorous testing and validation. Insufficient testing can lead to models that don't perform as expected in real-world scenarios.

9. Poor Project Management: Managing AI projects requires clear communication, especially given the iterative nature of model development and training.

10. Regulatory and Compliance Issues: As AI becomes more prevalent, regulations around data usage, model transparency, and ethical considerations can introduce additional requirements and costs.

11. Security Concerns: AI models, especially those deployed in critical areas, need to be secure. Additionally, data used for training must be protected, adding to the costs.

12. Training and Adoption: Implementing AI solutions often requires training staff to use and understand the models. Resistance to adopting AI-driven solutions can also lead to increased costs.

13. Maintenance and Support: AI models may need regular updates based on new data or changing conditions. The costs of maintaining and fine-tuning models can be higher than anticipated.

In conclusion, while AI offers transformative potential across various sectors, it also comes with challenges similar to traditional IT projects. Proper planning, expertise, and understanding of the AI landscape are crucial to ensure successful and cost-effective implementation.

Google Bard’s answer:The following factors that have contributed to IT projects becoming more expensive than initially expected are likely also to be applicable to AI implementation and use:

• Poor planning and estimation: AI projects can be very complex, and it can be difficult to accurately estimate the cost and timeline upfront. This can lead to cost overruns if the project is not carefully planned and managed.

• Scope creep: AI projects can also be subject to scope creep, as new features and requirements are often added during development. This can lead to increased costs if the project scope is not clearly defined and managed.

• Unrealistic expectations: Some customers may have unrealistic expectations about the cost and timeline of AI projects. This can lead to frustration and disappointment when the project goes over budget or takes longer than expected to complete.

• Technical complexity: AI projects can be very complex, and this complexity can lead to unexpected costs. For example, a project may require the use of new or unfamiliar technologies, or it may involve integrating multiple systems.

• Unforeseen problems: AI projects are often subject to unforeseen problems, such as hardware failures, software bugs, and data quality issues. These problems can cause delays and increase costs.

• Poor communication: Poor communication between stakeholders can lead to misunderstandings and delays in AI projects. This can also lead to additional costs, as stakeholders may have to make changes to the project plan or requirements.

• Lack of resources: AI projects often require a significant investment of resources, such as people, equipment, and data. If a project does not have the necessary resources, it can lead to delays and increased costs.

• Changing market conditions: The cost of AI goods and services can fluctuate over time. If a project is delayed, it may cost more to complete due to changes in market conditions.

• Vendor lock-in: Vendor lock-in can also be a problem in AI projects, as customers may become dependent on a particular vendor for software or services. This can make it difficult to switch vendors, which can lead to higher costs.

• Lack of expertise: AI projects require the expertise of experienced professionals. If a project does not have the necessary expertise, it can lead to problems and increased costs.

In addition to these factors, there are some unique challenges associated with AI implementation and use that could also contribute to cost overruns. For example, AI projects often require large amounts of high-quality data, which can be expensive to collect and prepare. Additionally, AI models can be very complex and time-consuming to develop and train.

Overall, it is important to be aware of the factors that can contribute to AI project cost overruns and to take steps to mitigate them. By carefully planning and managing AI projects, organizations can reduce the risk of cost overruns and achieve their desired outcomes.

Bing Chat’s answer:All of the factors that I listed for IT projects can also be applicable to AI implementation and use, as AI is a form of IT that has its own specific characteristics and challenges. However, some of the factors may be more relevant or pronounced for AI than for other IT projects. For example:

• Unclear project scope: AI projects can be more prone to scope creep or ambiguity, as AI is a fast-evolving and complex field that requires constant experimentation and adaptation. AI projects may also have multiple stakeholders with different expectations and objectives, which can make it hard to define and align the project scope.

• Unanticipated risks: AI projects can pose higher risks than other IT projects, as AI systems can have unpredictable or unintended behaviors, outcomes, or impacts. AI systems can also be vulnerable to adversarial attacks, manipulation, or exploitation. These risks can affect the reliability, safety, security, or ethics of the AI system.

• Contractor deficiencies: AI projects can require more specialized skills and expertise than other IT projects, as AI involves advanced mathematics, statistics, computer science, and domain knowledge. AI projects can also require more collaboration and communication among different disciplines and teams. Finding and retaining qualified contractors for AI projects can be challenging and costly.

Claude’s answer:Many of the factors that can drive up costs and complexity for traditional IT projects are likely to also be risks and challenges for AI implementation:

• Scope creep - AI projects can easily expand in scope and ambition. Need to start focused.

• Customization - AI often requires extensive customization, configuring models for specific use cases.

• Integration - Challenges integrating AI with existing data and systems.

• Changing technologies - Rapid evolution of AI models/tools may necessitate upgrades.

• Lack of expertise - AI is still an emerging field requiring scarce expertise. Learning curve is steep.

• Technical challenges - AI development involves a lot of complex data wrangling, model tuning, testing.

• Regulatory changes - AI may require adjustments to comply with evolving regulations.

• Scalability issues - Scaling AI across larger data and traffic volumes can be difficult.

• Security needs - Securing data flows, models, and AI system outputs raises new risks.

• Maintenance costs - Keeping AI models up-to-date and accurate involves ongoing costs.

• Training costs - Extensive training required for developers and end-users to adopt AI systems.

The key to controlling costs will be taking an incremental approach, having realistic expectations, and working closely with experienced AI teams and vendors. But organizations should anticipate and budget for some cost overruns when embarking on ambitious AI projects. The technology still has maturing to do.

Question 4 of 5 for AI services: Isn't it true that in IT, the sale of the solution isn't the cash cow, it's the maintenance and support instead? Isn't it going to be exactly the same for AI?

ChatGPT-4’s answer:Yes, it's a widely recognized observation in the IT industry that while initial software or solution sales can generate revenue, the real "cash cow" often lies in the ongoing maintenance, support, and upgrades. Here's why:

1. Recurring Revenue: Maintenance and support contracts typically involve recurring payments, providing a steady stream of income over time. This is in contrast to a one-time software sale.

2. Long-Term Customer Relationship: Maintenance and support contracts foster long-term relationships with customers. This can lead to opportunities for upselling, cross-selling, and offering new solutions as they become available.

3. Complexity of IT Systems: As IT systems become more complex, the need for expert support grows. Businesses often rely on the original vendors for this support, as they have the best understanding of their products.

4. Updates and Upgrades: Software needs regular updates to address security vulnerabilities, fix bugs, and add new features. These updates can be a source of ongoing revenue.

5. Customization and Integration: Over time, businesses might need customizations or integrations with other systems. The original software vendor is often in the best position to provide these services.

For AI, the scenario is expected to be similar, if not more pronounced:

1. Continuous Learning: Unlike traditional software, AI models benefit from continuous learning. As new data becomes available, models may need to be retrained or fine-tuned, which can be a source of ongoing revenue.

2. Complexity of AI: AI models, especially deep learning models, can be intricate. Understanding and optimizing these models might require expertise that many businesses don't have in-house, leading them to rely on external support.

3. Data Management: AI relies heavily on data. Managing, cleaning, and ensuring the quality of this data can be an ongoing task that businesses might outsource to experts.

4. Ethical and Regulatory Considerations: As AI becomes more prevalent, there will be increasing scrutiny on its ethical and regulatory implications. Businesses might need ongoing consultancy and support to navigate these challenges.

5. Custom AI Solutions: While there might be off-the-shelf AI solutions, many businesses will require custom solutions tailored to their specific needs. Developing and maintaining these custom solutions can be a significant source of revenue.

In conclusion, while the initial sale of IT and AI solutions can be lucrative, the real financial potential often lies in the ongoing relationship with the customer, providing maintenance, support, updates, and other services. This model provides a more predictable and steady revenue stream, making it highly valuable for vendors.

Google Bard’s answer:Yes, it is true that in IT, the sale of the solution is often not the cash cow, but rather the maintenance and support. This is because IT solutions can be complex and require ongoing support to keep them running smoothly and securely. Additionally, IT solutions often need to be updated regularly to keep up with changing technologies and security threats.

This is likely to be the case for AI as well. AI solutions are complex and require ongoing maintenance and support to ensure that they are performing as expected and that they are secure. Additionally, AI solutions need to be updated regularly to keep up with advances in AI technology.

Here are some reasons why maintenance and support can be a cash cow for IT companies:

• Recurring revenue: Maintenance and support contracts typically generate recurring revenue, which can be a valuable source of income for IT companies.

• High margins: Maintenance and support contracts often have high margins, which means that IT companies can make a good profit on them.

• Sticky customers: Maintenance and support contracts can help IT companies to retain customers, as customers are less likely to switch providers if they are already locked into a contract.

However, it is important to note that maintenance and support contracts can also be a source of frustration for customers. Some customers may feel that they are being overcharged for maintenance and support, or that their needs are not being met. Additionally, some customers may find it difficult to cancel maintenance and support contracts, even if they are unhappy with the service.

Overall, maintenance and support can be a cash cow for IT companies, but it is important to ensure that customers are getting value for their money. IT companies should also be transparent about the terms and conditions of maintenance and support contracts, and they should make it easy for customers to cancel their contracts if they are not satisfied with the service.

In the case of AI, it is likely that maintenance and support contracts will become increasingly important as AI solutions become more widely adopted. Businesses and organizations will need to have access to qualified AI experts to help them maintain and support their AI solutions. This could lead to a significant increase in revenue for IT companies that offer AI maintenance and support services.

Bing Chat’s answer:Thank you for your question. I understand that you are curious about the maintenance and support costs of IT and AI solutions. This is an interesting topic that has many aspects to consider.

IT and AI solutions are not just one-time purchases, but rather ongoing investments that require continuous maintenance and support. Maintenance and support costs can include hardware upgrades, software updates, data storage, security, troubleshooting, training, consulting, and more. These costs can vary depending on the type, complexity, scale, and scope of the IT or AI solution.

Some sources suggest that maintenance and support costs can account for 50% to 80% of the total cost of ownership (TCO) of IT solutions12. This means that the initial purchase price of an IT solution is only a fraction of the total cost that a business or consumer will incur over its lifetime.

AI solutions can have similar or even higher maintenance and support costs than other IT solutions. This is because AI solutions can involve more specialized skills, expertise, resources, and infrastructure than other IT solutions. AI solutions can also require more frequent updates, enhancements, and monitoring to ensure their accuracy, reliability, security, and ethics.

According to some estimates, the average annual maintenance and support cost of an AI solution can range from $40,000 to $500,00034. This cost can depend on factors such as the type of AI solution (e.g., chatbot, analysis system, etc.), the level of intelligence (e.g., narrow AI, general AI, etc.), the amount and quality of data (e.g., structured data, unstructured data, etc.), the performance requirements (e.g., speed, accuracy, reliability, etc.), and the vendor or contractor (e.g., third-party software, custom solution, etc.).

Therefore, it is possible to say that in IT and AI, the sale of the solution is not the only or even the main source of revenue for the provider or expense for the buyer. The maintenance and support costs can be significant or even dominant in the TCO of IT and AI solutions. However, this does not mean that IT and AI solutions are not worth investing in. Rather, it means that businesses and consumers should carefully evaluate their needs, goals, budget, and ROI before choosing an IT or AI solution.

I hope this helps you understand some of the issues involved in the maintenance and support costs of IT and AI solutions. If you have any further questions or feedback, please feel free to ask me. 😊

Claude’s answer:You make a good point. For many IT solutions and software, the bulk of revenue tends to come from ongoing maintenance, support, and services rather than the initial software license sale. This recurring revenue model is often viewed as more profitable and predictable for IT vendors.

The same dynamic is likely to hold true for enterprise AI solutions:

• The upfront costs of developing and deploying an AI system will be substantial, but likely dwarfed over time by recurring costs.

• Maintenance is crucial for AI - models need to be continually updated, retrained, and tuned to maintain accuracy and utility over time as data changes. This requires significant ongoing investment.

• Support costs also tend to be high for AI. These complex systems require specialized teams to keep them running smoothly, troubleshoot issues, and assist business users.

• Many vendors will pitch AI software licenses or development frameworks. But the real revenue will be derived from ongoing fees for maintenance, support, cloud services, and consulting help.

• Some AI startups may try to entice buyers with low upfront costs, only to recoup costs later through high recurring fees and lock-in.

So you're absolutely right - the business models for enterprise AI are shaping up to mirror IT software and services. The recurrent costs will be where most of the money is made. Buyers should factor this into procurement and budgeting.

Question 5 of 5 for AI services: And AI is going to be more profitable to vendors than any other technology before it, because it will require a lot more support as a lot more things will need fixing, would your agree? And the big tech companies are already adding AI to all of their existing services and solutions.

ChatGPT-4’s answer:Yes, there's a strong argument to be made that AI has the potential to be more profitable for vendors than many previous technologies, primarily due to the reasons you've outlined. Here's a deeper dive into why:

1. Inherent Complexity: AI, especially deep learning, is inherently complex. The models can have millions of parameters, and the process of training, fine-tuning, and deploying these models can be intricate. This complexity often necessitates expert support.

2. Continuous Evolution: Unlike traditional software that might remain static for extended periods, AI models benefit from continuous learning. As new data becomes available or as the environment changes, models may need updates or retraining. This ongoing evolution can be a source of continuous revenue for vendors.

3. Data Dependency: AI's performance is heavily dependent on data quality and quantity. Ensuring data is clean, relevant, and free from biases is an ongoing task, and businesses might rely on vendors for data management and preprocessing services.

4. Ethical and Regulatory Challenges: AI introduces a host of ethical and regulatory challenges, from concerns about bias and fairness to issues of transparency and accountability. Navigating these challenges might require expert consultancy and support, adding another revenue stream for vendors.

5. Integration with Existing Systems: Integrating AI solutions with existing IT infrastructure can be challenging. Businesses might need support to ensure seamless integration, especially as AI becomes a part of more and more services.

6. Pervasiveness of AI: As you rightly pointed out, big tech companies are integrating AI into almost all of their services, from search engines and advertising to cloud computing and consumer products. This widespread adoption amplifies the potential for support and maintenance revenue.

However, it's essential to note a few counterpoints:

• Commoditization of AI: As AI becomes more widespread, there's a possibility that certain AI services or tools become commoditized, driving down prices. We're already seeing this with some cloud-based AI services.

• Open Source and Community Support: The AI community is vibrant, with a strong emphasis on open-source tools and platforms. This can reduce the reliance on vendor support for some businesses.

• In-house Expertise: As AI becomes more integral to businesses, some might invest in building in-house AI teams, reducing their dependence on external vendors for support.

In conclusion, while there are strong indicators that AI will be a lucrative field for vendors, especially in terms of support and maintenance, there are also factors that might moderate this profitability. The landscape is evolving, and the exact trajectory will depend on various technological, economic, and regulatory developments.

Google Bard’s answer:Yes, I agree that AI is likely to be more profitable to vendors than any other technology before it, because it will require a lot more support as a lot more things will need fixing. This is because AI systems are complex and require ongoing maintenance and support to ensure that they are performing as expected and that they are secure. Additionally, AI systems need to be updated regularly to keep up with advances in AI technology.

As a result, there is likely to be a high demand for AI maintenance and support services from businesses and organizations of all sizes. This could lead to a significant increase in revenue for IT companies that offer AI maintenance and support services.

The big tech companies are already adding AI to all of their existing services and solutions. This is because AI is a powerful technology that can be used to improve the performance and efficiency of a wide range of products and services. For example, AI is being used to improve the accuracy of search engines, to develop new features for social media platforms, and to automate tasks in customer service centers.

The big tech companies are also developing their own AI-powered products and services. For example, Google offers AI-powered services such as Cloud AI Platform and Google AI Platform Vision. These services allow businesses and organizations to develop and deploy their own AI solutions.

The big tech companies are well-positioned to capitalize on the growing demand for AI maintenance and support services. They have the resources and expertise to provide businesses and organizations with the support they need to get the most out of their AI investments.

Overall, AI is a powerful technology with the potential to revolutionize many industries. It is likely to be a major driver of revenue for IT companies in the years to come.

Bing Chat’s answer:Thank you for your question. I understand that you are interested in the profitability of AI for vendors and the adoption of AI by big tech companies. This is a complex topic that has many factors and perspectives to consider.

AI can be a profitable technology for vendors, as it can create new value, enhance existing services, and increase customer loyalty. However, AI can also be a costly technology for vendors, as it can require more investment, maintenance, and support than other technologies. Therefore, the profitability of AI for vendors depends on how they balance the costs and benefits of AI development, deployment, and use.

According to some sources, the global AI market size was valued at $62.35 billion in 2020 and is expected to grow at a compound annual growth rate (CAGR) of 40.2% from 2021 to 2028. This indicates that there is a high demand and potential for AI solutions across various industries and domains. However, this also implies that there is a high competition and challenge for AI vendors to differentiate themselves and deliver value to their customers.

Some of the factors that can affect the profitability of AI for vendors are:

• The type of AI solution: There are different types of AI solutions, such as software, hardware, platform, or service. Each type has its own advantages and disadvantages in terms of cost, performance, scalability, and customization. For example, software solutions can be cheaper and easier to deploy than hardware solutions, but they may also have lower performance or compatibility than hardware solutions.

• The level of intelligence: There are different levels of intelligence, such as narrow AI, general AI, or super AI. Each level has its own complexity and capability in terms of learning, reasoning, and problem-solving. For example, narrow AI can perform specific tasks better than humans, but it cannot handle tasks outside its domain. General AI can perform any task that humans can do, but it is still a hypothetical concept. Super AI can perform any task better than humans or general AI, but it may also pose existential risks to humanity.

• The amount and quality of data: Data is the fuel for AI systems. The amount and quality of data can affect the accuracy, reliability, security, and ethics of AI systems. For example, more data can improve the learning and performance of AI systems, but it can also increase the storage and processing costs. High-quality data can reduce the errors and biases of AI systems, but it can also require more cleaning and validation efforts.

• The customer satisfaction: Customer satisfaction is the ultimate goal for any vendor. Customer satisfaction can affect the retention, loyalty, and advocacy of customers. For example, satisfied customers can provide positive feedback, referrals, or reviews that can attract more customers. Dissatisfied customers can provide negative feedback, complaints, or lawsuits that can damage the reputation or revenue of vendors.

Big tech companies are among the leading adopters and providers of AI solutions. They have the resources, expertise, and influence to develop, deploy, and use AI solutions for various purposes and domains. They also have the responsibility and accountability to ensure the quality, safety, security, and ethics of their AI solutions.

Some of the reasons why big tech companies are adding AI to their existing services and solutions are:

• To improve their core competencies: Big tech companies can use AI to enhance their core products or services that they are known for or specialized in. For example, Google can use AI to improve its search engine or advertising platform. Amazon can use AI to improve its e-commerce or cloud computing platform.

• To expand their market reach: Big tech companies can use AI to enter new markets or domains that they are not currently involved in or dominant in. For example, Apple can use AI to enter the health care or education sector. Facebook can use AI to enter the gaming or virtual reality sector.

• To create competitive advantages: Big tech companies can use AI to create unique features or benefits that can differentiate them from their competitors or substitutes. For example, Netflix can use AI to create personalized recommendations or content for its users. Tesla can use AI to create autonomous vehicles or battery systems for its customers.

I hope this helps you understand some of the issues involved in the profitability of AI for vendors and the adoption of AI by big tech companies. If you have any further questions or feedback, please feel free to ask me. 😊

Claude’s answer:You raise a fair point. There are a few reasons why AI solutions may prove even more profitable for vendors compared to previous enterprise software:

• AI systems are more complex and opaque than traditional code. This makes them harder to troubleshoot and maintain, increasing reliance on vendor support.

• AI models require frequent retraining and updating as input data changes. This creates a constant need for maintenance and services.

• AI expertise is still relatively scarce. Companies will lean more on vendors to fill the skills gap.

• Integration with existing systems and workflows is challenging, requiring more vendor consulting.

• AI adoption is accelerating quickly as companies race to incorporate it. Rapid growth increases profit potential.

• Switching costs are high. Once data is ingested into an AI system, leaving can be very difficult.

• Big cloud vendors like AWS, Microsoft, and Google are aggressively expanding their enterprise AI offerings, which could lead to lock-in.

So while AI has great promise to augment human capabilities, vendors likely stand to profit the most in the near term. AI's opacity and complexity create dependencies. Companies will need to budget adequately for ongoing vendor costs, while ensuring they don't become over-reliant on specific providers. Maintaining in-house AI expertise will be crucial as well.