The Quiet Revolution: How Small Language Models Are Winning On Speed, Security, And Cost?

Hugging Face’s research from 2024 showed that more than 40% of enterprise AI deployments were based on small language models. This was a big change from the previous year’s focus on huge, general-purpose small language models like GPT-4. It’s a trend that is slowly changing the business AI landscape: even though big models have wowed us with impressive benchmarks, people are starting to question how useful they are in the real world.

A year ago, the discussion about AI maturity was mostly about size, like the number of parameters, training datasets, and multi-trillion-token benchmarks. Companies rushed to add the biggest and most powerful models they could find, thinking that more size meant more sophistication. But that story is changing. The idea that “bigger is better” is losing its appeal as companies deal with rising costs, security issues, latency problems, and compliance issues.

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Instead, a new way of thinking is emerging, one that values fit over force. Mistral, LLaMA 3, and Phi-3 are all small language models that show that speed, agility, and accuracy are more important than being able to say a lot of things. Small language models are great for businesses that need specific applications like internal knowledge assistants, customer service bots, or automation for a specific field. They are responsive in real time, can be deployed locally, have lower inference costs, and give you much better control over your data.

It’s not just practicality that’s driving this change; it’s also strategy. Small language models are the right solution for a business world that is becoming more focused on secure, sovereign AI deployments and specialized use cases. They can be fine-tuned on private data, are light enough to run behind firewalls, and are efficient enough to work on edge devices or cheap infrastructure. Most importantly, they do all of this without losing the basic intelligence needed for high-value automation.

Let us talk about why small language models are quickly becoming the most popular AI tools for businesses. We’ll talk about how they got to where they are now, how they compare to their bigger competitors, and how they are opening up new ways for AI to be used safely, on a large scale, and in a way that lasts. It’s becoming clear that the future of enterprise AI may not be bigger than the quiet revolution, which is picking up speed. It might just be smarter.

What are small language models, and why are they important now?

As businesses rethink how they use AI, small language models have come out not as smaller versions of their bigger cousins but as systems made specifically for control, efficiency, and use in the real world. To understand why they are becoming more popular, we need to explain what small language models are and why now is their time.

What are small language models?

When people refer to small language models, they typically mean transformer-based AI systems that have been trained on fewer parameters than cutting-edge models like GPT-4 or Gemini 1.5. Small models usually have between 1 billion and 13 billion parameters, while large language models can have anywhere from 100 billion to 500 billion parameters (or even more). Some examples are Mistral (7B), Phi-3 (3.8B), LLaMA 3 (8B), and Falcon (7B). Even though they are small, these models are surprisingly powerful, especially when they are fine-tuned for certain tasks or areas.

It’s not just their size that makes small language models different; it’s also how they were made. These models are often trained on cleaner datasets, with more specific goals, and with efficient architectures like distilled layers or mixture-of-experts. They’re not designed to work on every possible task; instead, they’re intended to excel in limited, high-value situations such as customer service, finding internal knowledge, financial modeling, and more.

Less computing, more control

The compute profile is one of the most important things that sets small language models apart from each other. They can be trained and run with only a small amount of the resources that huge models need. This makes it possible to use on-premise deployment, edge processing, and hybrid cloud models, which give businesses direct control over where their data is stored, how long it takes to access it, and whether or not it is compliant.

This lower barrier to entry is also money-related. Inference costs, which are the costs of using a model, are much lower with small language models. Businesses don’t need groups of GPUs or APIs that only work with certain prices. AI is useful for small and medium-sized businesses, not just tech giants or startups with a lot of money, because it is cost-effective.

Also, smaller models can be customized more easily. It only takes hours, not weeks, and costs thousands, not millions, to fine-tune a 7B model on private business data. Organizations can make small models “theirs” without sending sensitive data to third-party services, as open-source tools like LoRA, QLoRA, and PEFT get better.

Why now? The Market is Set

So, why are small language models getting so popular now? There are a lot of different market forces at work:

What does Edge Deployment need?

The edge is becoming a key battleground for logistics companies that need AI in their warehouses and healthcare companies that need real-time diagnostics on devices. Small language models are best used in environments with limited resources, where cloud-based giants can’t be used because of privacy, latency, and bandwidth issues.

1. Sovereignty and Compliance

Data localization laws are making businesses look for models that can be hosted locally or behind a firewall in places like the EU, India, and the Middle East. Small language models help businesses feel safe using AI without breaking regional rules.

2. AI Model Maturity

Until recently, smaller models had a hard time keeping up with bigger models when it came to coherence and accuracy. But today’s small language models are doing enterprise-level work on core business tasks thanks to better training datasets, new model architectures, and better evaluation tools.

3. Shift from General to Purpose-Built AI

As businesses become more advanced in how they use AI, the focus is shifting from general-purpose chatbots to specialized AI assistants and co-pilots that can be counted on to do certain tasks. This change fits perfectly with small language models, which are easier to customize and manage.

The Big Picture

The rise of small language models is a big change for enterprise AI. They not only solve today’s problems—cost, latency, and privacy—but they also give you a strategic advantage for the future: custom intelligence that is lean, responsive, and in line with business goals. In a world where being flexible, following the rules, and being trustworthy are more important than big numbers, smaller is not only smarter, it’s also necessary.

Speed and Efficiency: Real-Time Beats General Brilliance

As more and more businesses use AI, they are learning that bigger isn’t always better, especially when speed and responsiveness are non-negotiable. In these cases, small language models are showing their worth not by being generally brilliant, but by being accurate and efficient in real time.

• When Quick Beats Fancy

The goal isn’t to have philosophical conversations or come up with new ideas for things like customer support, finding internal knowledge, or robotic process automation (RPA). Execution is quick, accurate, and can be done again. Small language models do very well here.

 Small language models can be trained to understand product-specific language, past interactions, and frequently asked questions in customer support, for example. This lets them give real-time answers at a much lower cost than larger models. Because they are so light, they can be put right into helpdesk platforms or even edge devices. This gets rid of the delay that happens when cloud-based queries go to big models.

Another important area is getting information from within the company. Companies often have a hard time finding information that is stuck in wikis, internal databases, or policy documents.  Small language models that have been trained on company knowledge can be a super-fast assistant, answering questions from employees almost instantly. These models work within the organization’s network, which makes them faster and more secure because they don’t use external APIs.

Small language models have faster inference speeds, making it much easier to read forms, start workflows, and make decisions based on text input in RPA. You can use these models on-site or add them to your existing automation platforms to speed up the time it takes to do routine tasks.

• Lower Latency Has Strategic Benefits

Not only does speed improve the user experience, but it also sets you apart from the competition. Latency is important whether you’re a fintech platform doing compliance checks that need to be done quickly or a logistics company changing delivery routes based on real-time input.

Small language models are great for operations that need to happen quickly because they can respond in less than a second. Every second counts when it comes to customer satisfaction, operational costs, or business outcomes.

Also, the cost of inference is a big factor in how well AI can scale. To work well, bigger models often need expensive GPU clusters and cloud credits. Small language models, on the other hand, are very useful and cost very little to run. This makes it possible to integrate more processes across the business without spending too much money.

• Made for the Edge

One of the best things about small language models is that they can be used right away. Big models need a lot of infrastructure in one place and a constant connection to the cloud. Smaller models, on the other hand, can live closer to where the action is, like on edge devices, local servers, or even mobile apps.

This edge feature has a lot of benefits, such as better privacy, less reliance on outside infrastructure, and better performance when not connected to the internet. This localized intelligence is very important for fields like healthcare, manufacturing, and defense, where decisions need to be made quickly in controlled settings.

Small language models are becoming the agile, quick-responding engines that power real-world business needs in a world full of overengineered solutions. Their worth isn’t in their abstract intelligence, but in their ability to get things done quickly, cheaply, and exactly where they’re needed. In business, speed is not a luxury; it’s an edge over the competition.

Bring AI Behind the Firewall for Security and Sovereignty

As companies put more and more effort into integrating AI, issues of security, sovereignty, and compliance have quickly moved from the sidelines to the center of attention. Healthcare, finance, law, and public sector organizations that deal with sensitive information often have to deal with the harsh realities of data privacy laws, internal security rules, and vendor risks, which can make the excitement around large AI models less exciting.

This is where small language models are taking off, giving businesses a safer and more independent way to use AI.

• Keeping Your Data Safe in the Age of AI

The General Data Protection Regulation (GDPR), the Health Insurance Portability and Accountability Act (HIPAA), and the California Consumer Privacy Act (CCPA) are all examples of regulatory frameworks that require strict control over how data is processed, stored, and shared. In this case, big AI models that are hosted by third-party vendors in the cloud raise red flags right away. Businesses don’t know much about how data is handled in these black-box ecosystems, which makes it hard to report on compliance and assess risk.

On the other hand, small language models offer a crucial way out. These models are light and very flexible, so they can be used completely within an organization’s infrastructure, whether it’s on-premises or in a virtual private cloud (VPC) environment. This gives businesses full control over their data, including where it is, how it is used, and who can access it.

• The Benefit of Having It On-Premise

The ability to run small language models behind a firewall changes the meaning of “AI readiness” in regulated environments. Hospitals can make models better for clinical documentation without breaking patient privacy. Financial institutions can use AI assistants to improve internal research or client support without putting sensitive transactions at risk. Law firms can use automated tools to sort documents and summarize cases without putting client data at risk of being leaked to third-party LLM providers.

Small language models can be hosted on existing hardware or low-cost dedicated servers because they need a lot less computing power and storage than large language models. Organizations don’t need powerful GPU clusters or hyperscaler subscriptions. They can start small, work on their projects locally, and grow responsibly.

This not only cuts costs, but it also lowers the risks that come with vendor lock-in. Companies no longer have to deal with unclear terms of service, changing API costs, or the possibility that public models will become obsolete. That level of independence is not only useful in today’s changing AI market; it’s also smart.

• Compliance Without Compromise

HIPAA compliance is not optional in the healthcare industry; it is essential to the mission. Small language models can help with AI tasks like medical summarization, appointment automation, and triage support without raising any red flags during audits because they can process all data in a safe and compliant environment.

Banks that need to follow Know Your Customer (KYC), anti-money laundering (AML), and data residency rules can also use small language models to automate routine tasks without breaking any rules.

Government agencies have to deal with the same things. Public sector organizations can use Small language models to make this possible. They help governments modernize services, make operations more efficient, and get citizens more involved without crossing data sovereignty lines.

• AI You Can Trust, on Your Terms

It was never just about intelligence with AI; it was also about trust, control, and giving people power. Small language models offer a balanced way forward for business leaders who are trying to decide between security and innovation. They give you real AI capabilities while also meeting the growing need for local control, data protection, and flexible architecture.

Organizations need tools that fit with their values and the rules they have to follow in a world where every AI decision is now a security decision. Small language models aren’t just a technical alternative to big, cloud-based systems. They are the key to using AI in a way that respects boundaries, earns trust, and gets results without cutting corners.

Customization and Control: Fine-Tuning Right at Your Fingertips

In the changing world of enterprise AI, being flexible is no longer a nice-to-have; it’s a must. Companies need models that can fit in with their processes, brand identity, and industry-specific needs without costing too much or causing too much trouble. This is where small language models come in and change the game. They offer fine-tuning and domain alignment features that are easier to use and less expensive than those of larger models.

1. Fine-Tuning Without the Complexity

People know that big LLMs like GPT-4 or Claude are smart in a lot of different ways, but that breadth comes with a price, both in terms of money and work. Fine-tuning these models takes a lot of computing power, large datasets, and often, infrastructure that is controlled by the vendor.

Small language models, on the other hand, are lightweight and more modular, which makes them perfect for quick fine-tuning. Businesses can connect these models to their taxonomies, internal workflows, or compliance needs without having to pay a lot for cloud services.

For example, a law firm can teach a small model to summarize contracts using certain terms, and a healthcare provider can change it to sort patient notes based on internal diagnostic codes, all without sending private data to outside vendors.

2. Aligning With Brand Voice and Domain Knowledge

Customization pays off in a big way when it comes to communication with customers. You can adjust small language models to match your company’s brand tone, vocabulary, and preferred way of speaking. This makes sure that chatbots, automated emails, or support responses sound like they really belong to the brand and not like they are just a generic response.

These models are great for more than just brand voice; they also work well for applications that require domain-specific knowledge. A fintech company might teach a model how to read regulatory filings, find problems in reports, or help with internal compliance workflows. A small model could be made to look at equipment logs, guess when maintenance will be needed, and make short reports that follow engineering standards in the manufacturing industry.

Being able to speak more than one language is another big plus. It’s much easier to train a small model for regional dialects or industry jargon in many languages than it is to try to do the same with a large, general model. This lets global teams keep their interactions culturally and geographically accurate without having to rely on translation APIs from other companies.

Real-World Applications of Tailored Small Models

As companies become more picky about how and where they use AI, many are finding that small language models give them a strategic edge in very specific, business-critical situations. These models are more than just lighter versions of larger models; they are built to be flexible, private, and customizable in ways that larger models can’t match. Let’s look at how some businesses are already using custom small models to solve problems in the real world.

1. Healthcare Startups: Intelligence That Protects Your Privacy

Healthcare startups need a lot of data-driven insights, but they also need to protect patient privacy. Small language models are great for internal tasks like finding incomplete clinical documentation in Electronic Health Records (EHRs) or spotting possible misdiagnoses. Small models can be used on-premise, while general-purpose AI tools need cloud-based processing.

This means that Protected Health Information (PHI) never leaves the organization. Using medical terms and clinical patterns to fine-tune these models makes them both accurate and compliant with strict healthcare rules like HIPAA.

2. Law Firms: Speeding Up Research While Keeping Secrets

In the legal world, speed and accuracy are very important, but so is keeping things private. Law firms are starting to use small LLMs to help with the early stages of case preparation, like making case summaries, finding relevant precedents, and putting discovery materials in order.

Sensitive documents stay safe because small language models can be hosted in the firm’s secure IT environment. Custom training makes sure that the models know how to read and write legal language and follow the company’s rules for formatting and tone. This cuts down on the time lawyers spend doing research by hand, allowing them to focus on more important tasks while still keeping client data safe

3. B2B SaaS Providers: Intelligent In-App Assistants

Small language models also do a great job with customer service. B2B SaaS platforms are putting small, well-tuned LLMs right into their user interfaces to make smart assistants. These assistants use the platform’s documentation, training materials, and feature updates to give you answers right away that are specific to your situation.

These models are different from external chatbots because they reflect the company’s unique voice and change along with the product. This makes for a better support experience with less reliance on third-party AI services.

4. Retailers: Better, more personalized suggestions

Retailers are using small language models to make recommendation engines that do more than just suggest popular items. They also take into account brand values, inventory levels, regional preferences, and seasonal trends. For instance, a model can be set up to put more weight on eco-friendly products, give priority to items that are available locally, or show items that are related to upcoming sales. The models are small and work well, so they can give real-time recommendations across digital channels with very little infrastructure cost.

In short, small language models are already showing their value in many different fields. They are changing the way businesses turn intelligence into action in a big way by giving AI capabilities that are precise, safe, and in line with the brand.

In each case, small language models not only lower costs but also make it less necessary to rely on closed ecosystems like OpenAI or Anthropic. This rule makes sure that companies keep ownership of their intellectual property, model behavior, and compliance.

The Power of Ownership

In a business that uses AI, having control is the same as having a competitive edge. By using small language models, businesses can make AI assets that are truly their own, with their tone, for their own field, and that work safely within their infrastructure. Fine-tuning becomes a strategic tool that lets businesses grow their intelligence without taking on more risk or giving up control to outside platforms.

The Emerging Ecosystem of Small LLMs

The field of AI is changing quickly, and small language models are becoming the foundation of business-level intelligence. Big, general-purpose models like GPT-4 and Claude have always gotten a lot of attention. But there is a parallel revolution going on that values efficiency, security, and customization more than size.

An expanding ecosystem of tools, platforms, and open-source communities is making it easier for more organizations to use small models than ever before. This is driving the revolution.

1. The Infrastructure That Makes Adoption Possible

The growing number of platforms that support the deployment, fine-tuning, and orchestration of small language models is one of the main reasons they are becoming more popular. Tools like Hugging Face’s Transformers library have made it easier for everyone to use the best models by giving them pre-trained weights, tokenizers, and training pipelines that can be easily changed.

These platforms let startups and big companies use small, powerful models without having to spend a lot of money on infrastructure. They can fine-tune them on their own data sets or even deploy them with very little computing power.

Another important addition to the stack is LangChain, an orchestration framework that lets businesses connect language models with tools, APIs, and data sources to create dynamic, task-based AI workflows. LangChain lets businesses use small language models in ways that are similar to how big AI systems work, but with a lot less work and money.

At the same time, Ollama is making it easier to deploy things locally. It lets teams run small language models directly on laptops, workstations, or edge servers without needing special hardware by focusing on edge-ready packaging and efficient runtime environments. For cases where speed, security, or offline functionality are important, this portability is very important.

2. Open-Source Momentum

Open source is the heart of this new ecosystem. Models like Mistral, LLaMA 3, and Phi-3 are becoming more popular not only because they work well, but also because they are flexible. These small language models are often trained by the community and supported by research institutions or clear licensing. This gives businesses more insight into how they are built and more freedom to change them to meet their needs.

Because of this level of openness, businesses can look at training data, use reinforcement learning that is specific to their field, or set up rules that are specific to compliance and ethics. The difference between closed-source large models, which don’t tell us much about where the data came from or how it was aligned, has made regulated industries very interested in the smaller, more open option.

3. Enterprise-Ready Integration

Another reason why small language models are becoming more popular is that they work well with the current business infrastructure. These models are more flexible and easier to integrate than their bigger cousins, which may need proprietary APIs or specialized cloud environments. They work perfectly in IT environments without needing to be re-architected, from containerized deployment to Kubernetes orchestration.

Small language models are especially useful in industries like manufacturing, healthcare, and government, where on-premise deployment, data sovereignty, and system interoperability are all must-haves. Lightweight stacks make sure that AI improvements can be added without affecting core workflows or adding new dependencies.

In short, the rise of small language models is not a fringe movement; it is a practical response to real-world problems and challenges. These models are becoming the agile, cheap, and flexible workhorses of enterprise AI as the ecosystem around them grows. This quiet revolution is already changing how businesses think about and use smart systems, from open-source collaboration to infrastructure integration.

More than just the size obsession

Model size has been the main topic of enterprise AI discussions for too long. The story was simple from GPT-3 to GPT-4: the bigger the model, the smarter the AI. But that way of thinking is changing. Small language models are showing that ability, not size, is what makes a difference.

“Fit-for-purpose” intelligence is becoming the new gold standard in this changing world. What matters now is how well a model solves a specific business problem, like how quickly it responds, how securely it runs, how easily it fits into existing workflows, and how well it understands the subtleties of a certain area.

Small language models are getting better and better at these things. They are faster, more flexible, and easier to customize than their bigger counterparts, and they cost a lot less.

From being obsessed with size to being obsessed with results

Organizations need to change their AI goals because the way they think about AI has changed from “bigger is better” to “better is better.” Instead of asking, “Which model is the most powerful?” leaders should ask, “Which model gets us the result we want the most?”

This is where small language models shine. These models get results without putting too much strain on infrastructure or budgets. They can speed up the process of finding information within an organization, power contextual chatbots, or make privacy-first AI possible in sensitive fields like healthcare and finance.

Thinking about the result first leads to smarter investments. Instead of using off-the-shelf general models that work for a wide range of situations, businesses can now fine-tune and deploy small models that are made just for their needs, which improves both performance and compliance.

Call to Action: Make AI work better and more relevant

Tech leaders need to stop using hype-driven strategies and start using practical, outcome-focused execution if they want to benefit from the quiet revolution led by small language models. These models are more than just a cheaper option to big LLMs; they are a whole new way of thinking about enterprise AI: fast, safe, fine-tuned, and made for a specific purpose. To get the most out of their resources, businesses should do the following:

1. Re-Evaluate the AI Stack

The first step is an honest audit of your current AI investments. Many enterprises are still locked into expensive contracts with large, general-purpose AI providers.  But the truth is that these systems might be too much for most real-world situations and not worth the money they cost. Think about this: Are you paying a lot of money for generic AI when a leaner, more customized approach could get you results faster, more safely, and for less money?

Small language models are often better for operational efficiency, especially when speed and predictability are more important than raw generative power. If your current AI stack is causing delays, budget overruns, or compliance worries, now is the time to look into focused, lightweight models that are more in line with business goals and enterprise limitations.

2. Try out small LLMs in certain roles

You don’t have to make a big change all at once; start small. Find a few important but low-risk tasks where small language models can really help. Think about things like customer service, IT help desks, HR automation, finance compliance reviews, or bringing on new employees. These jobs often have to do the same thing over and over again, which is perfect for smart automation.

Businesses can see results quickly, make adjustments based on real feedback, and prove value by using small LLMs in these areas. These first use cases will help you set goals for success, get people excited about the project, and plan how to get more people to use it.

3. Spend money on customization and deployment in-house

The best thing about small language models is that they can change. Small models can be customized to fit your company’s tone, industry jargon, brand voice, and day-to-day operations, unlike monolithic APIs from big companies. This is very important in fields that are heavily regulated or very specific, like healthcare, law, or fintech.

When hosted on-premises or in a private cloud (VPC), these models also give you more control over data security and compliance. Companies that work with sensitive data that is subject to rules like GDPR, HIPAA, or PCI can get a lot out of keeping AI capabilities behind the firewall.

You don’t just use AI; you make it a core competitive advantage by owning the infrastructure and customizing the model.

4. Stop following trends and start solving problems

Headlines that make breakthrough models trained on trillions of tokens look good can easily pull you away from what you’re doing. But following trends won’t help your business succeed; instead, you need to make smart systems that meet your needs. AI should not be an abstract goal; it should be a way to make things run more smoothly, give customers better experiences, and make decisions faster.

Small language models are a useful way to get to this future. They let you try things out without spending a lot of money, speed up the process of making changes, and can be added to existing tech stacks with little trouble. This makes them a good choice for businesses that want intelligence that they can use, not just theories.

In short, being the smartest, not the loudest or biggest, is what makes a difference. Businesses can get out of the hype cycle and use AI in a more meaningful, useful, and relevant way by using small language models. Make sure your AI strategy is practical, focused on fit, and optimized for value, just like your product strategy.

The small language models‘ quiet revolution isn’t about settling for less; it’s about getting more from less. Companies can finally make AI work in useful, scalable ways by putting fit-for-purpose intelligence ahead of raw power. CTOs, CIOs, and leaders of innovation need to stop trying to make things bigger and start making things that are specific, independent, and long-lasting. In the age of AI, the best strategy isn’t to be the biggest; it’s to be the most useful.

Conclusion: Bigger Isn’t Always Better

For a long time, people thought that bigger models were better when it came to AI. People thought that big models like GPT-4 and other foundational models were the best examples of machine intelligence because they had amazing general abilities and huge context windows. And while these models are still useful in some areas, like research, long-form generation, and reasoning across multiple domains, they are no longer the only option for enterprise AI.

The rise of small language models has also changed the way people think. These small AI engines are showing that size isn’t the only thing that matters. In fact, in many real-life situations, small language models are not only a good alternative to their larger counterparts; they are also the best choice. Their leaner architecture allows for much faster inference times, which makes them great for situations where quick responses and low latency are important, like customer support, looking up internal documents, or using AI in apps.

Small language models are also very useful because they don’t cost much. For many businesses, especially those that don’t have the cloud credits or resources of Big Tech companies, training, fine-tuning, and hosting huge models can be too expensive. Small LLMs, on the other hand, let businesses use high-performance AI on their infrastructure, whether it’s on-premises or in private clouds. This cuts both operational costs and the need for outside providers by a huge amount.

Data security and sovereignty are just as important. Healthcare, banking, and government are some of the industries that can’t afford to send sensitive data through APIs that third-party providers own.

Small language models get around this problem by being able to run in safe places, like behind a firewall, where data privacy, compliance, and governance standards are always met. This localized control is no longer just a nice-to-have; it’s quickly becoming a business necessity as regulations get stricter around the world.

Small language models are also better at meeting the specific needs of businesses, in addition to being more efficient and secure. General-purpose models are trained to do everything for everyone, but smaller models can be quickly and cheaply adjusted to fit a brand’s tone, industry jargon, or need for multiple languages. Companies can drive consistency, trust, and performance at scale with this ability to customize behavior, without adding extra complexity.

This doesn’t mean that big models don’t have a place anymore. Their ability to learn without any examples, be creative, and generalize across many domains is still the best. Small language models are becoming more and more important for everyday business tasks that need speed, safety, and accuracy.

We need to talk about AI in terms of fit-for-purpose intelligence instead of size. Stop thinking that “bigger” means “better” and start using models that are made to fit the needs and goals of your business. Small language models are becoming more than just a technical option; they are also becoming a strategic advantage in this quiet revolution. Companies that see this change coming and act on it early will be the ones that make AI systems that are faster, safer, and smarter in the future.

Also Read: To Build Smarter AI, Create a Smarter Fake World

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