In the very first episode of our monhtly Explainable AI podcas, hosts Paul Anthony Claxton and Rohan Hall sat down with James Chalmers, Chief Revenue Officer of Novo Power, to discuss one of the most pressing issues in AI today: energy consumption and its environmental impact.

Together, they explored how AI’s rapid expansion is placing significant demands on global power infrastructures and what leaders in the tech industry are doing to address this.

The conversation covered various important topics, from the unique power demands of generative AI models to potential solutions like neuromorphic computing and waste heat recapture. If you’re interested in how AI shapes business and global energy policies, this episode is a must-listen.

Why this conversation matters for the future of AI

The rise of AI, especially generative models, isn’t just advancing technology; it’s consuming power at an unprecedented rate. Understanding these impacts is crucial for AI enthusiasts who want to see AI development continue sustainably and ethically.

As James explains, AI’s current reliance on massive datasets and intensive computational power has given it the fastest-growing energy footprint of any technology in history. For those working in AI, understanding how to manage these demands can be a significant asset in building future-forward solutions.

Main takeaways

AI’s power consumption problem: Generative AI models, which require vast amounts of energy for training and generation, consume ten times more power than traditional search engines.Waste heat utilization: Nearly all power in data centers is lost as waste heat. Solutions like those at Novo Power are exploring how to recycle this energy.Neuromorphic computing: This emerging technology, inspired by human neural networks, promises more energy-efficient AI processing.Shift to responsible use: AI can help businesses address inefficiencies, but organizations need to integrate AI where it truly supports business goals rather than simply following trends.Educational imperative: For AI to reach its potential without causing environmental strain, a broader understanding of its capabilities, impacts, and sustainable use is essential.

Meet James Chalmers

James Chalmers is a seasoned executive and strategist with extensive international experience guiding ventures through fundraising, product development, commercialization, and growth.

As the Founder and Managing Partner at BaseCamp, he has reshaped traditional engagement models between startups, service providers, and investors, emphasizing a unique approach to creating long-term value through differentiation.

Rather than merely enhancing existing processes, James champions transformative strategies that set companies apart, strongly emphasizing sustainable development.

Numerous accolades validate his work, including recognition from Forbes and Inc. Magazine as a leader of one of the Fastest-Growing and Most Innovative Companies, as well as B Corporation’s Best for The World and MedTech World’s Best Consultancy Services.

He’s also a LinkedIn ‘Top Voice’ on Product Development, Entrepreneurship, and Sustainable Development, reflecting his ability to drive substantial and sustainable growth through innovation and sound business fundamentals.

At BaseCamp, James applies his executive expertise to provide hands-on advisory services in fundraising, product development, commercialization, and executive strategy.

His commitment extends beyond addressing immediate business challenges; he prioritizes building competency and capacity within each startup he advises. Focused on sustainability, his work is dedicated to supporting companies that address one or more of the United Nations’ 17 Sustainable Development Goals through AI, DeepTech, or Platform Technologies.

About the hosts:

In the latest episode of The Generative AI Podcast, host Arsenii Shatokhin sat down with Karanveer Anand, a Technical Program Manager at Google, to explore how AI is reshaping the field of program management.

They dove into everything from the role of AI in cloud computing to the evolving balance between AI innovation and safety. If you’re curious about how AI is influencing the future of technical program management, this discussion is a must-listen.

Catch the full episode right here.

Why AI enthusiasts should care

AI is no longer just a buzzword; it’s becoming a critical part of how businesses operate, making it essential for technical program managers to understand its implications.

As Karanveer explains, AI is revolutionizing how program management is done, introducing new efficiencies and ways to optimize workflows. For those working in AI and tech, understanding the intersection of AI and program management can provide a significant competitive edge.

Main takeaways

AI in program management: AI requires deep technical understanding and brings new challenges to program management.AI’s role in cloud computing: AI optimizes cloud resource allocation for better efficiency and cost management.AI as a necessity: Tools like Gemini integrate AI into everyday tasks, making it indispensable.Prioritizing AI safety: Safety should be built into AI frameworks from the start.AI’s future across industries: AI is set to transform sectors like healthcare and finance, offering new opportunities for innovation.

Meet Karanveer Anand

Karanveer Anand is a Technical Program Manager at Google, specializing in software reliability. His role focuses on ensuring that Google’s services remain highly reliable and accessible to billions of users worldwide.

With a background in cloud infrastructure and AI, Karanveer is passionate about using AI to improve software reliability and streamline processes. He is always exploring innovative ways to apply AI and is a thought leader in the intersection of AI, cloud computing, and program management.

As businesses seek to leverage AI to enhance their offerings and streamline operations, the ability to effectively implement AI solutions is critical to achieving sustained success. 

AI and deep learning inference demand powerful AI accelerators, but are you truly maximizing yours?

GPUs often operate at a mere 30-40% utilization, squandering valuable silicon, budget, and energy.

In this live session, NeuReality’s Field CTO, Iddo Kadim, tackles the critical challenge of maximizing AI accelerator capability. Whether you build, borrow, or buy AI acceleration – this is a must-attend.

Date: Thursday, December 5
Time: 10 AM PST | 5 PM GMT
Location: Online

Iddo will reveal a multi-faceted approach encompassing intelligent software, optimized APIs, and efficient AI inference instructions to unlock benchmark-shattering performance for ANY AI accelerator.

The result?

You’ll get more from the GPUs buy, rather than buying more GPUs to make up for the limitations of today’s CPU and NIC-reliant inference architectures. And, you’ll likely achieve superior system performance within your current energy and cost constraints. 

Your key takeaways:

The urgency of GPU optimization: Is mediocre utilization hindering your AI initiatives? Discover new approaches to achieve 100% utilization with superior performance per dollar and per watt leading to greater energy efficiency.Factors impacting utilization: Master the key metrics that influence GPU utilization: compute usage, memory usage, and memory bandwidth.Beyond hardware: Harness the power of intelligent software and APIs. Optimize AI data pre-processing, compute graphs, and workload routing to maximize your AI accelerator (XPU, ASIC, FPGA) investments.Smart options to explore: Uncover the root causes of underutilized AI accelerators and explore modern solutions to remedy them. You’ll get a summary of recent LLM real-world performance results – made possible by pairing NeuReality’s NR1 server-on-a-chip with any GPU or AI accelerator.

You spent a fortune on your GPUs – don’t let them sit idle for any amount of time.

Did you know that 27.1% of AI practitioners and 32.5% of AI tools’ end users don’t specifically address artificial intelligence’s biases and challenges? The technology is helping to improve industries like healthcare, where diagnoses can be improved through rapidly evolving technology. 

However, this raises ethical concerns about the potential for AI systems to be biased, threaten human rights, contribute to climate change, and more. In our Generative AI 2024 report, we set out to understand how businesses address these ethical AI issues by surveying practitioners and end users.

With the global AI market size forecast to be US$1.8tn by 2030 and AI being deeply intertwined with our lives, it’s vital to address potential issues. Ethical AI is developing and deploying systems to highlight accountability, transparency, and fairness for human values. 

Understanding AI bias

Bias can occur throughout the various stages of the AI pipeline, and one of the primary sources of this bias is data collection. Outputs are more likely to be biased if the data collected to train AI algorithms isn’t diverse or representative of minorities.

It’s also important to recognize other stages where bias can occur unconsciously, such as: 

Data labeling. Annotators can have different interpretations of the same labels.Model training. The data collected must be balanced, and the model architecture capable of handling diverse inputs must be balanced, or the outputs could be biased.Model deployment. The AI systems must be monitored and tested for bias before deployment.

As we increasingly utilize AI in society, there have been situations where bias has surfaced. In healthcare, for example, computer-aided diagnosis (CAD) systems have been proven to provide lower accuracy results for black female patients when compared to white female patients.

With Midjourney, academic research found that, when asked, the technology generated images of people in specialized professions as men looking older and women looking younger, which reinforces gendered bias. 

A few organizations in the criminal justice system are using AI tools to predict areas where a high incidence of crime is likely. As these tools can often rely solely on historical arrest data, this can reinforce any existing patterns of racial profiling, leading to an excessive targeting of minority communities. 

Challenges in creating ethical AI

We’ve seen how bias can exist in AI, but that isn’t the only one it faces. AI can potentially improve business efficiency, but there are a few challenges to ensuring that the ethics of AI solutions are a key focus.

1. Security

AI can be susceptible to hacking; the Cybersecurity Infrastructure and Security Agency (CISA) mentions documented times when attacks have led to objects being hidden from security camera footage and autonomous vehicles acting poorly.

2. Misinformation

With the potential to cause severe reputational damage, it’s essential to curb the likelihood of AI tools spreading untrue facts by establishing proper steps when developing the technology. Misinformation can affect public opinion and spread the wrong information as if it is true.

3. Job displacement

AI can automate various work activities, freeing up valuable worker time. However, this could lead to job loss, with lower-wage workers needing to upskill or change careers. Creating ethical AI also includes making sure that tools complement jobs and not replace them.

4. Intellectual property

OpenAI had a lawsuit involving multiple famous writers who stated their platform, ChatGPT, illegally used their copyrighted work. The lawsuit claimed that AI exploits intellectual property, which can lead to authors being unable to make a living from their work.

5. Ethics and competition

With the constant need to innovate, companies may need to take more time to ensure their AI systems are designed to be ethically sound. Additionally, strong security measures must be in place to protect businesses and users.

Strategies to address AI bias

We wanted to know how practitioners and end users of AI tools addressed biases and challenges, as companies need to be aware of steps that need to be taken when using this technology.

1. Regular audits and assessments

44.1% of practitioners and 31.1% of end users stated they addressed bias by regular auditing and assessing. This often includes a comprehensive evaluation of AI system algorithms, where the first step is to understand where bias is more likely to occur.

Following this, it’s vital to examine for unconscious bias, such as disparities in how AI systems handle age, ethnicity, gender, and other factors. Recognizing these issues allows businesses to create and implement strategies to minimize and remove biases for improved fairness. This could be changing the training data for AI models or proposing new documentation.

2. Rely on tool providers’ ethical guidelines

According to UNESCO, there are ten core principles to make sure ethical AI has a human-centered approach:

Proportionality and do no harm. AI systems are to be used only when necessary, and risk assessments need to be done to avoid harmful outcomes from their use.Safety and security. Security and safety risks need to be avoided and addressed by AI actors.Right to privacy and data protection. Data protection frameworks need to be established alongside privacy.Multi-stakeholder and adaptive governance & collaboration. AI governance is essential; diverse stakeholders must participate, and companies must follow international law and national sovereignty regarding data use.Responsibility and accountability. Companies creating AI systems need to have mechanisms in place so these can be audited and traced.Transparency and explainability. AI systems need appropriate levels of explainability and transparency to ensure safety, security, and privacy.Human oversight and determination. AI systems can’t displace human accountability and responsibility.Sustainability. Assessments must be made to determine the impact AI systems have on sustainability. Awareness and literacy. It’s vital to ensure an open and accessible education for the public about AI and data.Fitness and non-discrimination. To ensure AI can benefit all, fairness, social justice, and non-discrimination must be promoted.

28.6% of end users and 22% of practitioners rely on AI tool providers to follow appropriate ethical guidelines, so it’s essential that AI systems have ethical AI in all stages of development and deployment of their technology.

3. Don’t specifically address

A substantial percentage of end users and practitioners, 32.5% and 27.1%, respectively, said they don’t specifically address biases when using AI tools. With this technology being widely used across various industries, not addressing concerns and challenges could lead to further issues.

In addition to data bias, privacy is a top concern; smart home software, for example, must have robust privacy settings to prevent hacking or tampering. Similarly, AI systems can often make decisions that have a profound impact—autonomous vehicles must keep everyone on the road safe, and ensuring that AI doesn’t make mistakes is essential.

Crafting better AI tools

When creating AI tools, it’s important to focus on all aspects—ethical AI is, perhaps, the most vital component, as it affects output and how various minorities, such as gender and ethnicity, may be treated in industries like healthcare and law.

Our Generative AI 2024 report offers a comprehensive overview of how practitioners and end users use AI tools and how the sentiment is on the ground. Trust is fundamental for AI technology, so make sure to get your copy to learn how much confidence users currently have.

Navigating the field of large language model operations (LLMOps) is more important than ever as businesses and technology sectors intensify utilizing these advanced tools. 

LLMOps is a niche technical domain and a fundamental aspect of modern artificial intelligence frameworks, influencing everything from model design to deployment. 

Whether you’re a seasoned data scientist, a machine learning engineer, or an IT professional, understanding the multifaceted landscape of LLMOps is essential for harnessing the full potential of large language models in today’s digital world. 

In this guide, we’ll cover:

What is LLMOps?How does LLMOps work?What are the benefits of LLMOps?LLMOps best practices

What is LLMOps?

Large language model operations, or LLMOps, are techniques, practices, and tools that are used in operating and managing LLMs throughout their entire lifecycle.

These operations comprise language model training, fine-tuning, monitoring, and deployment, as well as data preparation.  

What is the current LLMops landscape?

LLMs. What opened the way for LLMOps.Custom LLM stack. A wider array of tools that can fine-tune and implement proprietary solutions from open-source regulations.LLM-as-a-Service. The most popular way of delivering closed-based models, it offers LLMs as an API through its infrastructure.Prompt execution tools. By managing prompt templates and creating chain-like sequences of relevant prompts, they help to improve and optimize model output.Prompt engineering tech. Instead of the more expensive fine-tuning, these technologies allow for in-context learning, which doesn’t use sensitive data.Vector databases. These retrieve contextually relevant data for specific commands.

What are the key LLMOps components?

Architectural selection and design

Choosing the right model architecture. Involving data, domain, model performance, and computing resources.Personalizing models for tasks. Pre-trained models can be customized for lower costs and time efficiency. Hyperparameter optimization. This optimizes model performance as it finds the best combination. For example, you can use random search, grid research, and Bayesian optimization.Tweaking and preparation. Unsupervised pre-training and transfer learning lower training time and enhance model performance. Model assessment and benchmarking. It’s always good practice to benchmark models against industry standards. 

Data management

Organization, storing, and versioning data. The right database and storage solutions simplify data storage, retrieval, and modification during the LLM lifecycle.Data gathering and processing. As LLMs run on diverse, high-quality data, models might need data from various domains, sources, and languages. Data needs to be cleaned and pre-processed before being fed into LLMs.Data labeling and annotation. Supervised learning needs consistent and reliable labeled data; when domain-specific or complex instances need expert judgment, human-in-the-loop techniques are beneficial.Data privacy and control. Involves pseudonymization, anonymization techniques, data access control, model security considerations, and compliance with GDPR and CCPA.Data version control. LLM iteration and performance improvement are simpler with a clear data history; you’ll find errors early by versioning models and thoroughly testing them.

Deployment platforms and strategies

Model maintenance. Showcases issues like model drift and flaws.Optimizing scalability and performance. Models might need to be horizontally scaled with more instances or vertically scaled with additional resources within high-traffic settings.On-premises or cloud deployment. Cloud deployment is flexible, easy to use, and scalable, while on-premises deployment could improve data control and security. 

LLMOps vs. MLOps: What’s the difference?

Machine learning operations, or MLOps, are practices that simplify and automate machine learning workflows and deployments. MLOps are essential for releasing new machine learning models with both data and code changes at the same time.

There are a few key principles of MLOps:

1. Model governance

Managing all aspects of machine learning to increase efficiency, governance is vital to institute a structured process for reviewing, validating, and approving models before launch. This also includes considering ethical, fairness, and ethical concerns.

2. Version control

Tracking changes in machine learning assets allows you to copy results and roll back to older versions when needed. Code reviews are part of all machine learning training models and code, and each is versioned for ease of auditing and reproduction.

3. Continuous X

Tests and code deployments are run continuously across machine learning pipelines. Within MLOps, ‘continuous’ relates to four activities that happen simultaneously whenever anything is changed in the system:

Continuous integrationContinuous deliveryContinuous trainingContinuous monitoring 

4. Automation

Through automation, there can be consistency, repeatability, and scalability within machine learning pipelines. Factors like model training code changes, messaging, and application code changes can initiate automated model training and deployment.

MLOps have a few key benefits:

Improved productivity. Deployments can be standardized for speed by reusing machine learning models across various applications.Faster time to market. Model creation and deployment can be automated, resulting in faster go-to-market times and reduced operational costs.Efficient model deployment. Continuous delivery (CI/CD) pipelines limit model performance degradation and help to retain quality. 

LLMOps are MLOps with technology and process upgrades tuned to the individual needs of LLMs. LLMs change machine learning workflows and requirements in distinct ways:

1. Performance metrics

When evaluating LLMs, there are several different standard scoring and benchmarks to take into account, like recall-oriented understudy for gisting evaluation (ROUGE) and bilingual evaluation understudy (BLEU).

2. Cost savings

Hyperparameter tuning in LLMs is vital to cutting the computational power and cost needs of both inference and training. LLMs start with a foundational model before being fine-tuned with new data for domain-specific refinements, allowing them to deliver higher performance with fewer costs.

3. Human feedback

LLM operations are typically open-ended, meaning human feedback from end users is essential to evaluate performance. Having these feedback loops in KKMOps pipelines streamlines assessment and provides data for future fine-tuning cycles.

4. Prompt engineering

Models that follow instructions can use complicated prompts or instructions, which are important to receive consistent and correct responses from LLMs. Through prompt engineering, you can lower the risk of prompt hacking and model hallucination.

5. Transfer learning

LLM models start with a foundational model and are then fine-tuned with new data, allowing for cutting-edge performance for specific applications with fewer computational resources.

6. LLM pipelines

These pipelines integrate various LLM calls to other systems like web searches, allowing LLMs to conduct sophisticated activities like a knowledge base Q&A. LLM application development tends to focus on creating pipelines, not new ones. 

How does LLMOps work?

LLMOps involve a few important steps:

1.  Selection of foundation model

Foundation models, which are LLMs pre-trained on big datasets, are used for downstream operations. Training models from scratch can be very expensive and time-consuming; big companies often develop proprietary foundation models, which are larger and have better performance than open-source ones. They do, however, have more expensive APIs and lower adaptability.

Proprietary model vendors:

OpenAI (GPT-3, GPT-4)AI21 Labs (Jurassic-2)Anthropic (Claude)

Open-source models:

LLaMAStable DiffusionFlan-T5

2. Downstream task adaptation

After selecting the foundation model, you can use LLM APIs, which don’t always say what input leads to what output. It might take iterations to get the LLM API output you need, and LLMs can hallucinate if they don’t have the right data. Model A/B testing or LLM-specific evaluation is often used to test performance.

You can adapt foundation models to downstream activities:

Model assessmentPrompt engineeringUsing embeddingsFine-tuning pre-trained modelsUsing external data for contextual information

3. Model deployment and monitoring

LLM-powered apps must closely monitor API model changes, as LLM deployment can change significantly across different versions.

What are the benefits of LLMOps?

Scalability

You can achieve more streamlined management and scalability of data, which is vital when overseeing, managing, controlling, or monitoring thousands of models for continuous deployment, integration, and delivery.

LLMOps does this by enhancing model latency for more responsiveness in user experience. Model monitoring with a continuous integration, deployment, and delivery environment can simplify scalability.

LLM pipelines often encourage collaboration and reduce speed release cycles, being easy to reproduce and leading to better collaboration across data teams. This leads to reduced conflict and increased release speed.

LLMOps can manage large amounts of requests simultaneously, which is important in enterprise applications.

Efficiency

LLMOps allow for streamlined collaboration between machine learning engineers, data scientists, stakeholders, and DevOps – this leads to a more unified platform for knowledge sharing and communication, as well as model development and employment, which allows for faster delivery.

You can also cut down on computational costs by optimizing model training. This includes choosing suitable architectures and using model pruning and quantization techniques, for example.

With LLMOps, you can also access more suitable hardware resources like GPUs, allowing for efficient monitoring, fine-tuning, and resource usage optimization. Data management is also simplified, as LLMOps facilitate strong data management practices for high-quality dataset sourcing, cleaning, and usage in training.

With model performance able to be improved through high-quality and domain-relevant training data, LLMOps guarantees peak performance. Hyperparameters can also be improved, and DaraOps integration can ease a smooth data flow. 

You can also speed up iteration and feedback loops through task automation and fast experimentation. 

3. Risk reduction

Advanced, enterprise-grade LLMOps can be used to enhance privacy and security as they prioritize protecting sensitive information. 

With transparency and faster responses to regulatory requests, you’ll be able to comply with organization and industry policies much more easily.

Other LLMOps benefits

Data labeling and annotation GPU acceleration for REST API model endpointsPrompt analytics, logging, and testingModel inference and servingData preparationModel review and governance

LLMOps best practices

These practices are a set of guidelines to help you manage and deploy LLMs efficiently and effectively. They cover several aspects of the LLMOps life cycle:

Exploratory Data Analysis (EDA)

Involves iteratively sharing, exploring, and preparing data for the machine learning lifecycle in order to produce editable, repeatable, and shareable datasets, visualizations, and tables.

Be part of a community

Stay up-to-date with the latest practices and advancements by engaging with the open-source community.

Data management

Appropriate software that can handle large volumes of data allows for efficient data recovery throughout the LLM lifecycle. Making sure to track changes with versioning is essential for seamless transitions between versions. Data must also be protected with access controls and transit encryption.

Data deployment

Tailor pre-trained models to conduct specific tasks for a more cost-effective approach.

Continuous model maintenance and monitoring

Dedicated monitoring tools are able to detect drift in model performance. Real-world feedback for model outputs can also help to refine and re-train the models.

Ethical model development

Discovering, anticipating, and correcting biases within training model outputs to avoid distortion.

Privacy and compliance

Ensure that operations follow regulations like CCPA and GDPR by having regular compliance checks.

Model fine-tuning, monitoring, and training

A responsive user experience relies on optimized model latency. Having tracking mechanisms for both pipeline and model lineage helps efficient lifecycle management. Distributed training helps to manage vast amounts of data and parameters in LLMs.

Model security

Conduct regular security tests and audits, checking for vulnerabilities.

Prompt engineering

Make sure to set prompt templates correctly for reliable and accurate responses. This also minimizes the probability of prompt hacking and model hallucinations.

LLM pipelines or chains

You can link several LLM external system interactions or calls to allow for complex tasks.

Computational resource management

Specialized GPUs help with extensive calculations on large datasets, allowing for faster and more data-parallel operations.

Disaster redundancy and recovery

Ensure that data, models, and configurations are regularly backed up. Redundancy allows you to handle system failures without any impact on model availability. 

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I’m part of NVIDIA’s strategy team, and my background is in data.

My last role was as a data lead at Trade Republic, and I decided to change gears a little bit, do an MBA, and join NVIDIA in strategy. 

I have experience working in data in the US and then working in Germany. One of the reasons I felt frustrated with working in data is that I felt like, in Germany specifically, data teams were not necessarily at the forefront and weren’t necessarily building data products or being taken seriously.

That’s part of why I decided to join NVIDIA; it resonated with me, as well as the mission of pushing AI forward in Europe.

The slow adoption of generative AI in Europe

Europe is too slow at adopting Gen AI. 

The reality is that Europe is way behind the US in implementing the technology. 

The reason I’m highlighting this is because experimenting now is extremely important. The technology is moving forward very fast. 

Last year, we were talking about LLMs. This year, we’re talking about agentic workflows. So, it’s important to start experimenting with the technology now. Otherwise, it might be too advanced for us to catch up, and business models might become irrelevant.

It’s a very low risk to experiment with the technology because there’s a lot of proven value out there. If you look at research, you see that. The technology brings a lot of productivity gains.

A research paper from McKinsey that came out two weeks ago talks about forty percent of developers’ and product managers’ time being saved.

We’re also talking about three to fifteen percent higher revenues and ten to twenty percent ROI for companies implementing Gen AI.

Evolving customer expectations and business models

Customer expectations will keep evolving, and this technology will change business models.

They’re already expecting a high level of personalization from companies that they buy from regularly. About seventy percent of customers have that expectation, which will keep evolving. 

I use Amazon when ordering online in Berlin, and I have such great customer service that it’s hard to use another application. It becomes an expectation that you get that level of service. It’s important to think about this technology and how it will affect your industry. And, like I said, we’re yet again behind our American counterparts.

Background

The United States of America (USA) is one of numerous major economies taking forward a program for Artificial Intelligence (AI) Regulation. To ensure the USA plays a leading role in Artificial Intelligence research and development, the National Artificial Intelligence Initiative Act of 2020 was introduced and became law in 2021.

The Act’s overarching aim, inter alia, was to provide a broader initiative within the United States to ensure academia, the public, and private sectors could monitor and evaluate the performance of AI-based systems both before and post-deployment [1], [2].

Following this in 2022, the White House Office of Science and Technology Policy introduced the Blueprint for an AI Bill of Rights [3]. Following a year of public engagement to inform the creation of the framework, it outlines five core principles and associated practices to guide the creation, management, and iteration of automated systems while ensuring the protection of the American public’s rights [3].

With OpenAI introducing ChatGPT (Chat Generative Pre-Trained Transformer) in November 2022 and its forecasted economic potential exceeding $2.1 trillion [4], concerns were raised about how the technology at its current and continuous speed could work within the parameters of the justice system.

With the increasing adoption of Generative AI, President Biden in October 2023 issued an executive order on safe, secure, and trustworthy Artificial intelligence, stipulating (in amongst other requirements) that those developing the most powerful AI systems share their safety test data with the US government. For example, any companies developing foundation models that risk national security, national economic security, or national public health are required to inform the federal government during model training in addition to sharing red-team safety tests. [5].

The role of US Antitrust Laws

AI is seeing rapid expansion across sectors and organizations of all shapes and sizes, and therefore, the interoperability between AI as a tool and existing antitrust laws has been and will continue to be tested. While some states continue to work towards localized AI regulation, some argue that the pace and advancement around AI require a rewrite of antitrust laws.

For context, back in 1890, Congress passed the Sherman Act: a charter whose aim was to preserve free and unrestrained competition. Then, in 1914 a further two antitrust laws were passed, namely the Federal Trade Commission (FTC) Act (which created the Federal Trade Commission) and the Clayton Act [6], each of which are still in effect to this day.

Generally speaking, Antitrust laws exist to prevent unlawful mergers and business practices, with judgment being put in the hands of the courts to determine which cases are illegal based on the facts of each case. For over a century, these laws have retained the same core principle: protect competition to benefit consumers through the operation of operational efficiencies, fair pricing, and high-quality goods and services.

In summary, the Sherman Act makes illegal “every contract, combination, or conspiracy in restraint of trade” along with any “monopolization, attempted monopolization or conspiracy or combination to monopolize” [6].

The Supreme Court, however, ruled a while back that only unreasonable acts are prohibited: not every restraint of trade is included. For example, a partnership agreement between two individuals may restrain trade but not unreasonably and thus may be lawful under US antitrust law.

Any acts, though considered harmful to competition, are almost always illegal, and are known as per se violations, which include arrangements between businesses to fix prices, divide markets, or rig bids.

The Sherman Act can be enforced both in civil and criminal law, and both businesses and individuals can be prosecuted under it by the Department of Justice.

If a competitor fixes prices or rigs bids, penalties can include up to $100 million for corporations and $1 million for an individual, along with ten years imprisonment. Under federal law, the maximum fine can be increased to twice the amount the conspirators gained from the illegal activity or, on the other hand, twice the money lost by the victims if either of these amounts exceeds $100 million [6].

The Clayton Act, however, addresses more specific areas the Sherman Act does not clearly prohibit. Section 7 of the Clayton Act prohibits mergers and acquisitions demonstrating anti-competitive effects, to quote, “may be substantially to lessen competition, or to tend to create a monopoly.”

A further amendment in 1976 of the Clayton Act by the Hart-Scott-Rodino Antitrust Improvements Act requires advance notice from organizations planning a large merger or acquisition: they must notify the government of their plans.

It is important to note that private parties under the Clayton Act authorize private parties to sue for triple damages if they have been harmed by conduct that is in violation of either the Sherman or Clayton Act. Additionally, they can obtain a court order prohibiting the anticompetitive practice in the future. [6]

USA approaches to AI regulation example: Colorado AI Act

Antitrust laws aside, states are taking differing approaches in trying to regulate AI. The Colorado AI Act (also referred to as Consumer Protections for Artificial Intelligence), for example, was signed into law on the 17th of May 2024 but does not come into effect until the 1st of February 2026 [7].

While there are similarities between it and the EU’s AI Act, the Colorado AI Act specifically focuses on high-risk AI systems. Developers are required to put in place safeguards by sharing information with deployers, such as what data has been used for model training, risk mitigation measures, and reasonably foreseeable limitations of the system.

Additionally, developers must publicly share information on their website or in a public use case inventory two key pieces of information: 1) the type of high-risk system they developed and 2) what steps they are taking to manage risks of algorithmic discrimination. Most importantly, should algorithmic discrimination occur through the intended use of the system, developers must disclose this to both the Colorado Attorney General and the system developers in question. 

Alongside developers, deployers must follow a risk management policy such as the National Institute of Standards and Technology’s Artificial Intelligence Risk Management Framework and the International Organization for the Standardizations Standard ISO/IEC 420001.

Among these existing standards, the size and complexity of the deployer will need to be factored into the reasonability of the framework. Should the system undergo an intentional or substantial modification, deployers must conduct an impact assessment within 90 days of the modification taking place in addition to two additional impact assessments for the system, specifically an impact assessment for the system alongside an annual impact assessment for any system deployed. 

Similar to developers, deployers will need to publish information on their website and disclose any occurrences of algorithmic discrimination to the Colorado Attorney General. There are some cases, though, in which exemptions can be granted: if deployers have an employee headcount under 50, they can be exempt from most of the requirements, providing certain conditions have been satisfied. [7]

Does the USA have a long road ahead behind the AI Act?

In September 2024, the European Commission’s EU Competitiveness Report highlighted that 30% of unicorn startups founded in Europe between 2008 and 2021 were relocating abroad, with many of them to the USA [8].

It will therefore be imperative that while the technology conglomerates continue to innovate in the AI space, lead by example, and collaborate closely with the US government, safeguards for fundamental rights and product safety must be in place but not be excessively restrictive to the point they prevent smaller players from the development and adoption of frontier AI.

When it comes to digital competition, the European Union’s Digital Markets Act and Digital Services Act ensure fair online market practices are enforced. In contrast, in the US there are no digital-specific competition laws. However, two pending pieces of legislation, namely the American Innovation and Choice Online Act (“AICOA”) and the Open App Markets Act (“OAMA”), could, if passed, result in drastic changes to American regulation of digital competition with the aim of targeting companies such as Google, Apple, Meta, Amazon, Microsoft and possibly TikTok [9].

A multilateral approach to managing, understanding, and implementing AI regulation will be required to in the long run assess whether laws around AI technologies can be fairly but rigorously enforced.

The recent executive order, the introduction of state-level AI-specific laws, and the voluntary commitment from influential AI companies (i.e., OpenAI, Meta, and Google) to increase testing of AI systems alongside sharing information on managing AI risks are important steps in understanding this fast-paced technology.

It will not, however, change the challenge of a lack of a singular definition of AI, and instead, it could be seen as a hard yard ahead in identifying that any material shift in antitrust regulation more closely aligned to AI innovation may only be feasible if regulating the outcome of AI becomes the focus instead of the attempt to holistically regulate AI. 

Bibliography

[1] Parker Lynne, Director of the National AI Initiative Office, Deputy United States Chief Technology Office, ‘National Artificial Intelligence Initiative’ (Artificial Intelligence and Emerging Technology Inaugural Stakeholder Meeting, June 29, 2022) < www.uspto.gov/sites/default/files/documents/National-Artificial-Intelligence Initiative-Overview.pdf > accessed 1st October 2024. (OSCOLA)

[2] H.R.6216 – 116th Congress (2019 – 2020): National Artificial Intelligence Initiative Act of 2020, 116th Cong. (2020), https://www.congress.gov/bill/116th-congress/ house-bill/6216 (BlueBook – change to OSCOLA)

[3] ’Blueprint for an AI Bill of Rights’ (Office of Science and Technology Policy, The White House) < www.whitehouse.gov/ostp/ai-bill-of-rights/ > accessed 1st October 2024. (OSCOLA)

[4] ’Economic Potential of Generative AI: The Next Productivity Frontier’ (McKinsey Digital, 14 June 2023 ) < www.mckinsey.com/capabilities/mckinsey-digital/our insights/th e-economic-potential-of-generative-ai-the-next-productivity-frontier#/> accessed 3rd October 2024.

[5] ’President Biden Issues Executive Order on Safe, Secure and Trustworthy Artificial Intelligence’ (The White House, Briefing Room Statements and Releases) < www.whitehouse.gov/briefing-room/statements-releases/2023/10/30/fact-sheet president-biden-issues-executive-order-on-safe-secure-and-trustworthy-artificial intelligence/ > accessed 1st October 2024.

[6] ’The Antitrust Laws’ (Federal Trade Commission – Competition Guidance) < www.ftc.gov/advice-guidance/competition-guidance/guide-antitrust-laws/antitrust laws> accessed 30 September 2024.

[7] ‘Colorado Governor Signs Comprehensive AI Bill’ (Mayer Brown, Insights) <www.mayerbrown.com/en/insights/publications/2024/06/colorado-governor-signs comprehensive-ai-bill > accessed 1st October 2024.

[8] ’The future of European Competitiveness: Part B’ (European Commission) <https://commission.europa.eu/document/download/ec1409c1- 

d4b4-4882-8bdd-3519f86bbb92_en?filename=The future of European competitiveness_ In-depth analysis and recommendations_0.pdf > accessed 1st October 2024.

[9] B Hoffman, ‘Digital Markets Regulation Handbook’ (Cleary Gottlieb, January 2024)

< https://content.clearygottlieb.com/antitrust/digital-markets-regulation-handbook/ united-states/index.html > accessed 3rd October 2024.

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Few companies have made as significant an impact on the creative sectors as Runway has. As a beacon of innovation in generative AI, Runway has carved out a reputation for transforming art, entertainment, and human creativity. 

At the helm of these groundbreaking endeavors is Anastasis Germanidis, the Co-Founder and Chief Technology Officer of Runway, who has played a pivotal role in shaping the future of creative technologies.

Recently recognized by Time Magazine as one of the “100 most influential companies” in 2023, Runway continues to be at the forefront of the AI revolution, creating tools that enhance the capabilities of creatives worldwide and redefining what is possible in storytelling and artistic expression. 

In this exclusive interview, Anastasis shares his journey from the inception of Runway at NYU’s ITP program, alongside co-founders Cristobal and Alejandro, to the cutting-edge developments that continue to push the boundaries of computational creativity.

Psst. Anastasis will be at our summit in Boston.

Why not get your tickets and attend his talk?

Please introduce yourself and briefly introduce your journey leading up to this point with Runway.

I met my two co-founders, Cristobal and Alejandro, at NYU’s ITP program in 2016. Our shared curiosity about the potential of computational creativity led us to build tools for our peers—we wanted to build tools that let them interact with what was, at the time, an emerging technology, and that’s ultimately how Runway was originally born. 

Today, we are a full-stack, applied AI research company that builds generative AI systems and tools for creatives of all backgrounds.

Runway has become a pioneering platform in creative AI tools. What was the company’s original vision, and how has that evolved?

Being artists ourselves, we started Runway as a company for artists by artists. That vision has been central to our ethos since the very beginning and remains true today.

As our research and capabilities have continued to advance over the last few years – most recently with the release of our newest foundational model, Gen-3 Alpha – our original vision has remained the same: we’re enabling new ways of bringing stories to life and opening doors for new storytellers.

Runway’s tools empower creatives to work with AI in previously unimaginable ways. What are the most exciting use cases that have emerged from your platform?

Everyone, from Fortune 500 and Global 2000 companies to freelancers, marketers, and Hollywood studios, uses our tools to tell new types of stories and streamline workflows.

The generative AI landscape is growing rapidly. In your opinion, what are the biggest challenges and opportunities facing the industry today?

When we started Runway back in 2018, we were some of the only ones building in this space, so it’s been incredible to see the advancements we’ve made as an industry in the last couple of years and to see the creativity that these tools have unlocked for artists. 

That said, we’re still very early in the lifecycle of these tools, and there is a lot still to be built and unlocked, especially when it comes to further improving quality and introducing new control mechanisms.

Something our team is currently focused on that will continue to unlock creativity is the development of General World Models – these are systems that understand the visual world and its dynamics. Gen-3 Alpha has been a major step toward this goal, but it’s still very early.

How do you see the relationship between human creativity and AI evolving over the next few years? Will AI enhance human creativity, or do you foresee it changing creative industries in more fundamental ways?

The history of art has always been deeply intertwined with the history of technology. As our technical capabilities continue to expand, the tools will continue to expand, but they’ll always be in the service of human artists and creators.

Runway is at the forefront of generative AI for creators. What innovations or features can users expect shortly that will further transform their workflows?

Gen-3 Alpha is the first and smallest of upcoming models and a major step toward building general world models, but there’s still more work to be done. For example, the model can struggle with complex character and object interactions, and generations don’t always follow the laws of physics precisely.

General world models will aim to represent and simulate a wide range of situations and interactions, like those encountered in the real world, and we’ll continue to build towards that future.

Looking ahead, what excites you the most about the future of generative AI, both in terms of its potential for creativity and broader applications?

Generative AI is still incredibly young, and we’re discovering new use cases daily. We recently announced a partnership with Lionsgate Studios, marking a significant milestone in the collaboration between AI and Hollywood and unlocking new opportunities to evolve workflows and offer brand-new tools to the entertainment industry.

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Sustainability vs scalability

Artificial Intelligence (AI) is the most disruptive technology of our time, but as its impact continues to unfold, many are questioning whether it’s just another tech fad, or if it’s here to reshape the future permanently.

The rapid rise of AI has been met with both excitement to revolutionize our world and skepticism and growing concern about the long-term feasibility and ethical risks.

So, are we being duped into believing AI is scalable enough to solve all of our problems? Or will it stand the test of time, proving itself sustainable to humanity and our evolving world? These are the questions I want to address in this writing.

Ahh the question of “Is AI sustainable? No, it is not sustainable, it is scalable, and that is what will make it sustainable.

I think digging into whether AI is sustainable is quite simply how we approach it. AI is a lot like engineered transportation. Vehicle mobility has taken us from being foot mobile or horse and buggy to traveling around the world in just a matter of hours.

This is what AI has and will continue to do for us. Just like engineered transportation has not gone anywhere, nor will it ever, nor will AI. The problems that exist with engineered mobility over the last 135-plus years still exist today by and large.

For example, cars still wreak havoc on our environment, and they are dangerous at points depending on how they are controlled, but that does not mean they have not been scalable or become obsolete. It is clear for humans the benefits of engineered mobility have far outweighed the collateral damage.

We have worked hard and continue to work hard to put laws and regulations in place at the organizational and consumer levels as to how cars and even planes and the like are built, used and operated. But cars are definitely not sustainable, if they were then we wouldn’t junk them every 10 years.

We cannot continue to operate engineered mobility the way we do because it does have its dangers, and it does wreak havoc on our earth. AI is much the same and it is not sustainable in its current existence. 

Scalability and sustainability are closely connected, but they are distinct concepts that often get confused very often. 

Most inventions start off in a phase where they are not inherently sustainable. At this early stage, they might be too costly, resource-intensive, or inefficient to sustain long-term.

However, as they scale—reaching larger markets, benefiting from economies of scale, improving their technology, and optimizing their production processes—they can become more efficient, less costly, and more adaptable to different environments. This scaling process is what ultimately feeds into making them sustainable.

The definition of sustainability

I think we would all agree that we cannot maintain engineered mobility in the way that we have in the past in an ongoing manner. We also cannot continue to deplete our resources and disrupt the ecological balance. It is not feasibly sustainable. It does not mean engineered mobility is going anywhere, rather, it is going to scale, which means it is going to get better, and eventually, sustainable. 

Sustainable efforts in transportation (e.g., electric vehicles, fuel efficiency, regulations) have aimed to improve this. In the same way, AI sustainability focuses on minimizing energy consumption, reducing environmental impact, and ensuring fair use of resources and more energy-efficient models, similar to how transportation is moving towards electric vehicles.

I think people tend to get scalability and sustainability mixed up. Scalability feeds sustainability. There is a difference between sustainability and scalability. Engineered mobility has scaled continuously over the last century and a half, but it has never reached a point of full sustainability.

AI is similar.

The problems and challenges we face in AI does not mean AI is going anywhere, it just means it is going to get better, and eventually, sustainable.

The definition of scalability

The parallel drawn between AI and engineered mobility makes it clear that when first invented, both of these inventions were expensive, inefficient, sometimes unsafe and available to only a select few.

Over time, the scaling of manufacturing processes, infrastructure development, and technological improvements made them more affordable, more reliable, and widely accessible, thereby achieving better sustainability in their use and production.

Like engineered mobility, but in the context of AI, we see that it is currently highly scalable—new models can be deployed, adjusted, and integrated across various industries and applications with relative ease. 

I think this is what we can expect for the future of AI. 

To be or not to be, that is the question

“To be, or not to be, that is the question: Whether ’tis nobler in mind to suffer The slings and arrows of outrageous fortune, Or to take arms against a sea of troubles And by opposing end them.”

— William Shakespeare, Hamlet

To be

Some early adopters swear by AI’s potential to revolutionize industries, while others remain concerned about the long-term feasibility, sustainability, and ethical implications.

On the first account, the power of AI is already showing its muscle in areas like healthcare, finance, marketing, and many other sectors. With billions of dollars backing AI behind major corporations and high-profile investment entities, it is clear people are serious about AI and its promising future.

Or not to be

On the other hand ongoing issues with data privacy concerns, potential job displacement, power consumption, data shortages, and the challenge of creating explainable AI models leads many critics to believe we could all just be “duped” because we have adopted AI technologies prematurely without fully understanding their implications or long-term viability. 

All of this skepticism extends to the possibility that as soon as AI could lose momentum, leading to a mass exodus of interest or what could be referred to as AI’s “dumping point.”

In reality, the true future of AI likely lies somewhere in between. Some applications are failing to scale and live up to the hype—similar to the many failed “AI wrapper” companies associated with the financial struggles of 2023 which began with the Silicon Valley Banking Collapse—while others are evolving to become much more integral to the way we live and work.

We are all AI bullish

I want to start with a question that someone in the business community said to me, “You seem so bullish on AI.  I feel like we are in a bubble, and when we emerge, we will feel duped. But you feel so bullish – am I wrong?” I responded, “I am bullish on AI, and so are you. If you took away some of the things that make your life livable today, you’d quickly realize just how bullish you are. You’re probably using AI without even knowing it. Yes, I agree we are in a bubble because we are using AI many times when we do not know or fully understand its implications and consequences, which can lead to the overconfidence that produces market bubbles.”

How are we bullish? Well imagine having to do without some of your creature comforts today. When we start explaining to the very people who are reticent about AI the things which makes their lives comfortable and maintainable that they don’t typically think about are ran, powered and enabled by AI allows them to a paradigm shift to actually begin to realize how bullish they are on AI. 

It’s like a person who would never eat a particular food product, but the end food product contains the ingredient of the particular food product they hate, and they love the end food product. 

For example, Castoreum is used in Vanilla Flavoring, and Castoreum is a secretion from the anal glands of beavers. Something that would send most people praying to the toilet gods. Castoreum is still approved by the FDA as a “natural flavoring,” and it has been used in everything from Vanilla Ice Cream, Chewing Gum, to Alcoholic Beverages.

We can live without AI, but you wouldn’t want to

Living without AI would be a nightmare—a chaotic, life-threatening plunge into inefficiency and disaster. Every moment of your life would be slower, more stressful, and infinitely more dangerous.

Imagine scrambling through endless paperwork, wasting hours on menial tasks that AI currently handles in seconds, and facing constant delays in every transaction, whether you’re trying to get healthcare, travel, or even buy food. Businesses would spiral into inefficiency, grinding to a halt, leaving the economy in tatters and unemployment through the roof.

Healthcare systems would be crippled, with life-or-death decisions left to outdated manual processes, causing countless avoidable deaths and suffering as diagnoses are delayed, treatments are wrong, and medical errors skyrocket. Important infrastructure like power grids and supply chains would falter, causing massive blackouts, food shortages, and transportation collapses that could spark widespread panic and societal breakdown.

Security would be practically nonexistent; criminals would have a field day with weakened defenses, leading to rampant crime, data breaches, and potentially catastrophic cyber-attacks. AI has become our silent guardian, the shield that holds back chaos—and without it, the world would rapidly descend into a hellish, volatile, and dangerous state that would make survival, let alone daily living, a grueling ordeal.

Removing AI would disrupt productivity, critical systems, and innovation, drastically impacting how society functions and progresses. Removing AI in many ways would be like putting the entire world back several decades. We would potentially have massive blackouts, disease, crime, and very possibly large-scale conflicts. Removing AI would make it difficult, almost impossible, to revert to a world without it without facing significant consequences.

AI has now become integral in modern life, deeply cemented in daily routines, critical infrastructure, and industry workflows, making it nearly impossible to revert to a world without it. It drives efficiency, automates complex tasks, and provides data-driven insights that shape decision-making across sectors. 

What are some examples of the things that are empowered by AI that we could not live without? 

Navigation and ride-sharing services are closely connected, as they rely heavily on AI-enhanced satellite and GPS positioning systems to optimize routes, predict traffic, and provide estimated arrival times. Without the AI processing of GPS data, ride-sharing technologies like Uber and Lyft would struggle to operate efficiently.

Similarly, energy and utility management benefit from smart grid infrastructure powered by AI, which helps manage energy distribution more effectively and predict power demand spikes, ultimately preventing power outages. Without AI these things would not operate correctly. 

A real-life incident: To live without AI is not living at all, literally

In 2016, a Tesla Model S, while in Autopilot mode, was traveling on a highway in the Netherlands and the vehicle’s AI-powered system detected that the car in front was about to collide with another car and automatically applied the brakes before the human driver could react.

The AI system used radar, cameras, and sensors to not only detect the car directly ahead but also analyze the traffic situation beyond it. The Autopilot’s collision avoidance system recognized that the vehicle ahead was decelerating suddenly and predicted an imminent crash.

Before the human driver had time to react, the Tesla autonomously braked, avoiding what could have been a high-speed collision that might have caused severe injuries or even death.

In the end, the AI saved both the driver and passengers from a life-threatening accident by reacting faster than a human could have in a dangerous situation. 

Without AI the driver would have potentially died, and so in this case, to live without AI is not living at all.

Have we been duped by AI?

Many of us use AI frequently without realizing it, which can make us overconfident and careless, like bulls in a china shop… 

In a way, by charging ahead without fully understanding AI, we risk becoming uninformed and ignorant about its capabilities and limitations, which leads to hard lessons. This can leave us vulnerable to deception and making decisions without all the information, leading us to feel like we have been duped.

This is a form of Black Box AI. What is Black Box AI and how can it be avoided?

Black box AI refers to artificial intelligence systems whose processes are opaque and difficult to interpret and fail to provide clear explanations for how they arrived at those outcomes. This lack of transparency can lead us to a feeling of being “duped”.  

As an everyday user, if you are unfamiliar with AI, the best way to protect yourself is to be cautious and informed about the AI tools you use. Start by choosing reputable apps and platforms with good privacy policies and transparent explanations of how they use AI.

If you are asked to provide personal data, make sure you understand why it’s needed and how it will be used. Pay attention to any unexpected behaviors or decisions made by AI systems, and don’t hesitate to question or report issues if something seems off.

It’s also helpful to regularly review the permissions you grant to apps and to be cautious about sharing sensitive information. If possible, use platforms that allow you to adjust settings, like opting out of data collection or personalization features. Being mindful of these basics will help you stay informed about what and how you are using AI and ensure you do not become a victim of AI or a perpetrator of AI misuse. 

The illusion of deception It’s easy to think that AI is deceiving us when things go wrong. We see rogue AI systems producing unexpected or incorrect outcomes, and it feels like we’ve been led astray. However, AI isn’t inherently deceptive; it doesn’t have intent or motive. Rather, it operates within the constraints and biases of the data and instructions it’s been given.

So I would say, no we have not been duped by AI, if we have been duped it is likely entirely our fault because of our approach to adopting AI. The responsibility relies on how humans engage with and understand AI, rather than suggesting that AI itself is inherently duping us.

As AI continues to evolve and take on more complex roles in society, from autonomous vehicles to healthcare and financial markets, it’s important that we educate ourselves about how these systems work so we don’t get duped.

Will we be dumped by AI

Will we, as humans, eventually be “dumped” by AI? In other words, will AI take over our jobs, outpace us intellectually, or leave us behind, creating a future where human roles are diminished or obsolete?

Myth: I’m just a mere human, I am no match for AI

One of the most common concerns about AI is the fear of job displacement. In industries like manufacturing, repetitive tasks have been automated by machines for years, but AI is now capable of handling more complex roles that involve sophisticated, yet contained decision-making, such as data analysis, and customer interaction. 

AI makes the honor roll

Another concern is with the development of increasingly sophisticated machine learning models, the fear is that AI will eventually outthink us, leaving humans intellectually inferior and eliminating your kids from making the honor roll.

The fear that AI will render humans “intellectually inferior” and eliminate opportunities for jobs and achievements like making the honor roll is exaggerated. AI can process information quickly and make complex decisions, but human qualities like creativity, emotional intelligence, critical thinking, and adaptability are currently beyond AI’s reach.

The job market and educational systems are going to adapt, focusing on skills that complement AI rather than compete with it, therefore enhancing human intelligence and capability.

This is a big opportunity to drill down on the creativity side and team up with AI’s intellectual prowess for those who feel AI is a displacement. 

AI operates as sophisticated yet contained intelligence. What does that mean? It means that AI operates within parameters, not footloose and voyaging as humans operate, but rather as an aid to humans and our creativity.

This leaves humans to evolve and expand into new realms where creativity can take us and show us why, but we can then bring AI into the picture, and it can show us how.

The beauty of this is that it allows for very accelerated learning and innovation to happen and for the global collective of humans to put their energy towards creativity and all of the productive ways that AI can be used and deployed. Imagine any idea you’ve ever had—what if you had access to all the resources needed to make it happen? 

Think about those questions or ideas you’ve searched everywhere to find answers for; this is where large language models like ChatGPT come in. They take your questions or ideas and draw upon a large range of relevant resources available, sometimes all resources available, to show you the way.

Now, imagine leveraging this capability for every resource on Earth. In the future, AI may enable us to tap into all imaginable resources to tackle even the most ambitious challenges, such as traveling to distant places in the universe light years away, far beyond the physical traveling capability of any human right now. This is the future of AI. This is the reality.

I don’t know if this is working; it just feels like we are two separate people

There is a fear that a type of “AI divide” could lead to economic and social disparities, with only those who have the resources to leverage AI succeeding, while others are left in less economically viable positions.

While the history of economics and human behavior shows that this outcome is somewhat likely, it is more probable that the AI divide will begin to evaporate as humans and machines work together to improve infrastructure, education, and cultural adoption.

Over the coming years and decades, as AI becomes increasingly consumerized, shared access and AI infrastructure will expand at the individual level, creating opportunities for everyone who desires access. Another reason the AI divide may lose its grip is that division often arises from a lack of opportunity, objectivity, and understanding.

AI has the potential to enable humans to operate at their best capacity—creativity and innovation—where opportunity thrives. As an objective technology, AI can help reduce the subjectivity in human decision-making that has plagued and divided societies since our existence.

On the other hand, AI could inadvertently “dump” portions of humanity by reinforcing biases, displacing workers, or concentrating power in the hands of a few. To support this, governments, organizations, and industries must collaborate to establish frameworks and policies that promote inclusive access to AI technologies. This means investing in education and retraining programs that equip workers with the skills needed for an AI-driven economy.

Take control of the relationship

The key to avoiding being “dumped” by AI lies in how we, as individuals and societies, adapt to the changes AI brings. It’s important to approach AI not as a threat but as a tool that can be leveraged for human advancement. 

In the end, the most important question isn’t whether AI will “dump” us, but rather, will we allow it to?

AI is here to stay, but its future lies in a balance between scalability and sustainability. As disruptive as it is, AI’s rapid growth has created both opportunities and challenges, leaving us to question if we’ve been prematurely optimistic or overly cautious about its potential.

Much like engineered mobility, AI is not yet sustainable, but it is scalable—and scalability is the path to sustainability. From revolutionizing industries to empowering daily life, AI’s current scalability drives efficiency and innovation, setting the stage for eventual sustainability as technology, regulation, and ethical considerations catch up.

While skepticism and challenges remain, the world is inevitably moving forward with AI, shaping a future where its integration is not just advantageous but very important for growth and survival. 

Human agency is central to the development and application of AI technologies. As creators and users of AI, we have the ability to steer its development in ways that benefit society rather than displace or harm it.

Through responsible innovation, thoughtful policy, and ongoing education, we can ensure that AI continues to serve as a powerful tool for human progress rather than a force that leaves us behind. Whether you believe we are being duped or on the brink of a revolutionary, humanity-changing evolution, one thing is certain: AI is an unstoppable force that will continue to reshape the world for generations to come.

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