Noha Gad

When a company aspires to expand, acquire another business, or finance a major strategic project, traditional sources of funding may not provide the full amount required. Senior lenders may be unwilling to increase their exposure, particularly when the company already has significant debt or lacks sufficient collateral. At the same time, raising additional equity can reduce existing shareholders’ ownership and influence over the business.

Mezzanine financing addresses this gap, as it consists of a hybrid form of capital that combines features of debt and equity and occupies an intermediate position in a company’s capital structure. Companies often use this financing to obtain additional capital beyond the amount available from senior lenders, while limiting the need for a substantial equity raise.

What Is mezzanine financing?

Mezzanine financing represents a strategic financial tool that bridges the gap between senior debt and equity. This hybrid form of financing enables lenders to convert debt into equity, fostering flexibility and higher returns. Frequently employed in acquisitions and company expansions, mezzanine financing captures both opportunity and risk, offering companies crucial capital for growth. Although it is considered one of the highest-risk forms of debt, mezzanine financing offers some of the highest returns in debt investments.

Companies may choose mezzanine financing to fund specific growth projects or acquisitions with short- to medium-term time horizons. Often, these loans will be funded by the company’s long-term investors and existing funders of the company’s capital.

Main forms of mezzanine financing

Mezzanine financing is not a single standardized product. It can be structured in several ways, depending on the company’s financing needs, repayment capacity, capital structure, and the investor’s return requirements. The most common forms include:

• Subordinated debt. This form is a loan that ranks below senior debt in the repayment hierarchy but above equity. If the company defaults or enters liquidation, senior lenders are paid first, while subordinated lenders are repaid only after those obligations have been satisfied.
• Second-lien financing. It is secured by the same assets that support a senior lender’s first lien, but the mezzanine lender has a lower priority claim over those assets. If the borrower defaults, the first-lien lender generally has priority in enforcing its security and recovering the outstanding debt.
• Preferred equity. Preferred equity is legally a form of ownership, but it has features that make it resemble debt or other types of mezzanine capital. Preferred shareholders typically receive priority over common shareholders when dividends are paid, or assets are distributed during a liquidation.

Mezzanine financing provides companies with additional capital and more flexibility than traditional senior debt. Its characteristics make it useful for established businesses pursuing acquisitions, expansion projects, recapitalization, or ownership transitions. Key advantages are:

• Providing access to additional capital by filling the gap between the amount of senior debt a company can obtain and the total capital it needs.
• Helping existing owners retain a larger share of the business than they might retain after a conventional equity raise.
• Offering more flexible repayment structures than conventional bank debt.
• Supporting companies in completing transactions that require more funding than senior debt alone can provide.
• Offering lower cost than pure equity, particularly when the potential cost of giving up a large share of the company’s future value is considered.

Although mezzanine financing can provide flexible capital with less immediate ownership dilution than equity finance, it also exposes the company to higher costs, repayment pressure, and contractual restrictions. These disadvantages mean that it is generally suited to established businesses with predictable cash flows rather than to companies with unstable or insufficient earnings. 

Finally, mezzanine financing is a flexible hybrid capital that can help companies bridge the gap between senior debt and equity. By combining debt-like features with equity-linked characteristics, it enables established businesses to secure additional funding for acquisitions, expansion, recapitalization, and other strategic initiatives while potentially limiting immediate ownership dilution. However, it is generally more expensive than senior debt, may involve equity dilution through conversion rights or warrants, and can put significant pressure on a company’s cash flow because of interest payments and repayment obligations. 

Ghada Ismail

Artificial intelligence may be powered by chips and cloud infrastructure, but the quality of what AI produces increasingly depends on something much less visible: the data used to train it.

Data annotation, the process of labeling, classifying, transcribing, and evaluating data so that AI models can learn from it, is emerging as an important layer of the AI economy. In Saudi Arabia, the opportunity is particularly significant as the Kingdom invests heavily in sovereign AI infrastructure, Arabic-language models and the development of local digital talent.

“The AI race has moved from who has the most data to who has the best data,” said Sayed A., Chief Business Officer at Graystone Capital. “Data annotation is no longer back-office work. It is where model quality is decided.”

The shift is already visible globally. In 2025, Meta invested $14.3 billion for a 49% stake in Scale AI, the data-labeling company founded by Alexandr Wang, valuing the company at more than $29 billion. The deal underscored the growing strategic importance of high-quality training data as major technology companies compete to improve their AI models.

For Saudi Arabia, the opportunity is not simply to provide lower-cost labeling services. It is to build specialized data capabilities around Arabic, Saudi dialects, regulated industries and local cultural context.

From labeling images to training intelligence

At its simplest, annotation means adding information to raw data so an AI system can understand it. An image might be labeled to identify a vehicle or pedestrian. Audio can be transcribed and tagged according to speakers, accents or intent. Text can be classified according to sentiment, subject or meaning.

The role has expanded considerably with generative AI. Human workers can now compare AI-generated answers, identify inaccurate responses, write high-quality examples, assess safety risks, and help models understand specific professional or cultural contexts. This work is often referred to as human feedback, model evaluation, or reinforcement learning from human feedback.

That evolution is changing the value of the industry.

“Leading AI labs have largely used up the easily available text on the internet,” Sayed A. said. “Models now improve mainly through expert human judgment: doctors, lawyers, engineers and finance professionals who can tell a model when its answer is wrong.”

This matters because AI models can produce fluent answers while still making serious mistakes. A general annotator may identify whether an answer is grammatically correct, but a doctor may recognize a dangerous medical recommendation, while a banking professional can identify an incorrect interpretation of a financial rule.

The next generation of annotation businesses is therefore moving beyond volume toward expertise.

Why Saudi Arabia has a data advantage

Saudi Arabia's AI ambitions make this shift particularly relevant.

The Kingdom is building large-scale computing and AI infrastructure through initiatives involving HUMAIN, while the wider GCC is committing billions of dollars to AI infrastructure. Yet computing power alone cannot solve the data problem.

“Compute without quality data is an empty factory,” Sayed A. said.

The challenge is especially clear in Arabic. The language is used daily by more than 400 million people, according to UNESCO, but Arabic represents only 0.6% of websites whose content language is known, according to W3Techs' October 2026 data.

More importantly, Arabic is not a single linguistic market. Models have to deal with Modern Standard Arabic as well as regional varieties, slang, code-switching, and cultural references.

This creates an opening for Saudi companies that can produce data reflecting how people actually speak and communicate in the Kingdom. Sayed A. says: "The Gulf has committed to world-class computing power, from Stargate UAE in Abu Dhabi to HUMAIN in Saudi Arabia. But compute without quality data is an empty factory. Arabic is used daily by more than 400 million people, yet it makes up less than one percent of websites, and it spans more than 30 dialects that standard web data captures poorly. The region's leading models have all had to invest heavily in native, expert-reviewed data. Jais 2 was trained on 600 billion Arabic tokens, and HUMAIN's ALLaM model was refined with more than 600 domain experts and 250 evaluators. Curated Arabic data is a strategic asset, and the GCC is better placed than anyone to build it."

That process illustrates why data quality is becoming a strategic asset rather than a technical afterthought.

A small but growing Saudi startup ecosystem

Saudi Arabia's emerging annotation market includes companies pursuing different parts of the data value chain.

Annota8, a Riyadh-based startup, is developing an annotation platform covering image, video, text, audio and speech, documents and OCR, RAG, and large language model and agent workflows. Its operating layer supports project setup, task assignment, quality review, progress monitoring and data export. The company describes itself as Arabic-first and built in Saudi Arabia. It was also selected for the 500 Global Sanabil Startup Unlocked Program.

Tawsym is a Saudi startup specializing in AI training data and data annotation. Its services cover text, audio, image and video annotation, with a focus on language and cultural characteristics relevant to the Saudi market. The company says it works in partnership with the National Technology Development Program (NTDP) and Monsha'at's AI incubation program, and offers data hosting within Saudi Arabia to meet local data-storage requirements.

Bayanat Labs, based in Riyadh, focuses on Arabic data annotation, collection, alignment and evaluation across text, audio, image, and video. The company uses vetted native speakers, linguists and licensed domain professionals, while offering in-region data hosting by default. Its approach reflects demand for Arabic data that captures local linguistic differences while meeting the data-handling requirements of regulated sectors.

The emergence of these companies comes as Saudi Arabia's broader AI startup ecosystem expands. In June 2026, Monsha'at announced the graduation of 33 AI companies from its first AI incubator cohort, supported through a strategic partnership with the National Technology Development Program.

The gap between AI adoption and AI scale

Demand could increase as Saudi businesses move from experimenting with AI to deploying it across everyday operations.

A 2025 McKinsey survey found that 84% of GCC organizations were using AI in at least one business function, up from 62% in 2023. Yet only 31% had reached a level of AI maturity where AI was being scaled or fully deployed across the organization. That gap is important for data companies.

“GCC companies have embraced AI, but scaling it is another matter,” Sayed A. said. “The gap between pilot and scale is largely a data problem.”

A model that performs well in a demonstration may struggle when exposed to real customer conversations, local terminology, unusual cases or sensitive decisions. Businesses therefore need continuously updated datasets, evaluation systems and human review rather than a one-time training exercise.

This could create recurring demand for Saudi data companies, particularly in sectors where AI is moving into operational decision-making.

Where the biggest opportunities may lie

For Saudi startups, the strongest opportunities are unlikely to come from competing solely on the price of basic labeling.

Specialized Arabic dialects are one area. Saudi speech data, regional expressions, and conversational language are difficult to reproduce through generic global datasets.

Another opportunity is domain-specific annotation. Legal, healthcare, financial, and Islamic finance datasets require people who understand the underlying subject matter, not simply workers following basic labeling instructions.

Sayed A. sees the same potential across the GCC.

“We see a real opening for GCC-based providers of specialized, compliant annotation in areas such as Arabic dialects, Islamic finance, legal and healthcare, where local expertise and in-country data handling are advantages,” he said.

The investment environment is also becoming more supportive. AI startups in MENA raised $858 million in 2025, accounting for 22% of total VC funding in the region, according to MAGNiTT. The UAE and Saudi Arabia together absorbed 87% of AI funding.

For Saudi founders, this creates an opportunity to build an industry that sits underneath the more visible AI applications.

The human side of the AI data economy

Yet the sector faces its own challenges.

Basic labeling is increasingly being automated, putting pressure on companies whose business models depend on simple, repetitive tasks. Global providers also have enormous datasets, established customers, and large workforces.

At the same time, the treatment of annotation workers has become a major issue internationally. Low pay, inconsistent work and weak labor protections can create both ethical and reputational risks.

“The opportunity comes with hurdles,” Sayed A. said. “Automation is steadily commoditizing basic labelling, and global players are already setting up in the Gulf.”

That makes quality and trust increasingly important. Companies handling medical records, banking information or government datasets need strong controls around privacy, security, access and provenance.

Saudi Arabia’s focus on developing AI talent could provide another advantage. Under its National Strategy for Data and Artificial Intelligence, the Kingdom aims to develop 20,000 data and AI specialists and experts by 2030. Annotation could provide one route into that ecosystem, particularly as the work expands from basic labeling toward linguistics, quality assurance, model evaluation, red teaming and domain expertise.

From data labeling to strategic infrastructure

The biggest change may therefore be in how annotation itself is perceived.

What began as a labor-intensive task sitting behind the AI industry is becoming part of the model-development stack. The best providers are no longer simply asking people to label thousands of images or text samples. They are building systems for collecting, validating, evaluating, and governing the data that AI systems depend on.

For Saudi Arabia, that distinction matters.

The Kingdom has already invested heavily in computing capacity, AI models and digital infrastructure. The next layer is the human infrastructure that makes those systems useful in the local market.

“The winners will compete on expertise and trust, not on the lowest price per label,” Sayed A. said.

That may ultimately be the most important opportunity for Saudi Arabia's data annotation startups. Rather than becoming another low-cost outsourcing market, the Kingdom can build a specialized data industry around something global AI systems increasingly need but cannot easily manufacture themselves: high-quality human expertise rooted in the language, industries and culture of the region.

Ghada Ismail

In the final part of our interview, Abu Zannad turns to Saudi startups with international ambitions and closes with the one piece of advertising history he’d want every founder to know before they spend their first marketing riyal.

As Saudi startups look to expand internationally, how important is it to adapt their brand and messaging to different markets without losing their original identity?

“I think the wrong question for a Saudi startup is: “How much of our Saudi identity should we keep when we go abroad?”

The more useful question is: “What did being built in Saudi Arabia teach us that could make us more valuable somewhere else?”

Because origin by itself is not a strategy. It is raw material for a strategy.

And I think Saudi startups should be increasingly confident about this. The ecosystem has changed enormously. Saudi Arabia recorded $1.72 billion in venture-capital investment across 257 deals in 2025, the highest levels the market has seen. We are no longer only asking whether globally competitive startups can be created in Saudi Arabia. Increasingly, we are asking which of them can travel.

But travelling does not mean becoming culturally anonymous. The strongest global brands rarely erase where they came from. They understand what should travel intact and what needs to be translated.

I would ask a Saudi founder to think about four things.

First:

What is your Saudi core?

Not the flag. Not Arabic typography. Not putting a palm tree into the identity.

What capability, insight or sensibility did growing up inside this market actually give you?

Maybe you learned to design technology for Arabic-speaking consumers rather than adapting English technology afterwards. Maybe you became unusually good at operating in regulated and fragmented environments. Maybe your understanding of hospitality produced a different service standard. Maybe you grew up around a culture of family commerce and understood social selling differently. Maybe rapid transformation in the Kingdom taught your company to operate at a pace and scale that companies from more settled markets are not accustomed to. Maybe there is something in Saudi food, design, beauty, gaming, tourism, fintech or culture that the rest of the world has not encountered in this form before.

But the discipline is important:

Don’t ask what is Saudi about us. Ask 1-what is uniquely Saudi about us that is useful to somebody else.

Unifonic is an interesting example. It began in Saudi Arabia solving the difficult reality of reliable communications in fragmented, regulated markets and across Arabic language environments. Today it describes cultural fluency, trust and AI-native customer experience as part of its proposition. Something learned locally became a capability that could travel.

Foodics is another Saudi-born company that took its restaurant technology beyond the Kingdom into markets including the UAE and Egypt. The transferable asset was not “Saudi-ness” as decoration. It was a solution developed inside a sophisticated regional F&B environment that was relevant to restaurant operators elsewhere.

That brings me to the second question: 2-What are the qualifying factors in the market you are entering?

Strategists sometimes call these points of parity.

These are the things you have to get right simply to be taken seriously. If I enter Germany, Singapore, the UAE or Britain, what does the category expect? What is the regulatory standard? What does good customer service mean? What payment behaviour exists? What is the expected delivery time? What does trust look like? Which features are simply assumed? What tone belongs on the platform? What does the audience consider credible?

These are not necessarily reasons someone will choose you. They are the price of admission. A Saudi fintech company cannot enter another market saying, “We are proudly Saudi,” while failing to understand its financial regulation. A consumer startup cannot insist that its Saudi customer journey must be reproduced exactly in London.

Identity is not an excuse for irrelevance. You earn the right to be different only after you have demonstrated that you belong in the category.

Then comes the third question: 3-What are your winning factors?

These are your points of difference. Once I believe you can perform the basic job as well as the alternatives, why should I choose you? This is where Saudi origin can become strategically interesting. What can you offer that the incumbent cannot easily copy? A product insight? A technology? A cultural understanding? A design sensibility? A service model? A community? A particular form of hospitality? Access to a new cultural world? A way of solving complexity that your home market forced you to learn?

So I would separate very clearly:

Qualifying factors get you into the consideration set.
Winning factors give people a reason to choose you.

And both have to be understood at several levels: the market, the category, the audience, and increasingly, the platform.

Something that makes you distinctive on TikTok may be irrelevant in enterprise sales. Something that wins in Saudi food culture may need a different cultural translation in Paris. Something that works in B2C may signal the wrong things entirely in B2B.

So the identity stays coherent. The expression adapts.

This is where I think the national Saudi Made brand offers a very useful lesson. The program was deliberately created as a unified identity for Saudi products and services in domestic and international markets, and from the beginning it has been associated with quality, competitiveness, credibility and excellence. More recently that architecture has expanded: there is a Saudi Tech label supporting technology companies abroad, while the Saudi Crafts identity has been explicitly built around creativity, authenticity and quality presented in a contemporary way.

I find that combination interesting.

Quality and innovation on one side.
Authenticity and cultural confidence on the other.

Saudi Arabia does not have to choose between heritage and modernity. In many ways, the interesting story of Saudi Arabia today is precisely the tension between the two. That is something Saudi startups can borrow from; not necessarily the Saudi Made logo itself, because that has eligibility requirements, but the larger idea of what Saudi provenance can begin to mean.

A Saudi startup going abroad should ask:

4-What does “from Saudi Arabia” add to this particular proposition?

Sometimes the answer may be heritage. Sometimes technology. Sometimes design. Sometimes hospitality. Sometimes ambition. Sometimes the credibility of having solved a difficult problem in one of the world’s fastest-transforming economies. And sometimes, frankly, Saudi origin may add nothing relevant to the customer’s decision. Then don’t force it.

Because the objective is not to make every international customer admire where you come from. The objective is to give them a compelling reason to choose what you built. That is why I would never tell Saudi founders simply to “localize.” Localization can become another superficial exercise: change the language, hire a local influencer, swap some images and call the job finished.

I prefer the word trans-creation.

Trans-creation asks a much harder question:

5-How can the same meaning survive in a different cultural grammar?

This is actually one of the larger ideas behind AdEntity. Cultures have always survived contact not by remaining untouched, but by absorbing and translating what comes from outside while retaining enough coherence to remain recognizable.

Brands are not very different. The identity should have a centre. The expression should have flexibility. So if I had to give Saudi startups one framework for international expansion, it would be:

Know what is non-negotiably yours.
Learn what is non-negotiably theirs.
Meet the qualifying factors.
Protect the winning factors.
Then translate the expression for the market, category, audience and platform.

Don’t export the Saudi advertisement. Export the Saudi advantage. And perhaps the strongest global Saudi brands of the future will not be the ones that become less Saudi as they travel. They will be the ones that discover which part of being Saudi the world finds valuable.”

If you were advising a founder launching a startup today, what is one lesson from the history of advertising that you would want them to understand before spending their first marketing budget?

“My first advice? Please refer to my previous six answers. But if I had to reduce 100 years of advertising history to one lesson, it would be this:

Media amplifies.

It does not create meaning.

If you haven’t understood the human, the culture, the category, the product truth, and why anybody should care, spending more money will not solve the problem. Today, AI can simply help you waste that money faster.

So before buying attention, build something worth paying attention to. Then amplify the hell out of it.”

Noha Gad

Riyadh is rapidly emerging as a global hub for artificial intelligence (AI), driven by Saudi Vision 2030, massive investments, and a coordinated national strategy led by the Saudi Data and AI Authority (SDAIA). Designating 2026 as the “Year of AI” and committing over $14.9 billion in AI-related investments, the Kingdom is moving from strategy to execution, building one of the region’s most advanced digital infrastructures. With over 60 data centers, the world’s largest government data facility (Hexagon), and strategic partnerships with global tech giants, the Saudi capital now offers the infrastructure, capital, and talent pipeline needed to develop, deploy, and scale AI at pace. Additionally, initiatives such as the Riyadh Digital Innovation District, the national AI management standard (ISO 42001), and large-scale training programs signal a broader ambition: to make Riyadh a top-10 global technology district and a responsible, innovation-driven AI ecosystem by 2030.

Future-ready AI infrastructure

Riyadh’s emergence as an AI hub is underpinned by a rapidly expanding digital infrastructure designed to support the intensive computing, storage, and connectivity requirements of advanced AI systems. This infrastructure combines high-performance computing, large-scale data centers, cloud services, and specialized AI development zones, creating an integrated foundation for research, government applications, and private-sector innovation.

A key component is Shaheen III, the most powerful supercomputer in the GCC, operated by King Abdullah University of Science and Technology (KAUST). Consisting of two partitions: a CPU partition and a GPU-accelerated partition, Shaheen III is the fastest supercomputer in the Middle East and ranked among the world’s leading high-performance computers. Alongside it, the Hexagon Data Center in Riyadh is classified as the world’s largest government data center with a Tier IV facility and a planned capacity of 480 megawatts. Spanning over 30 million square feet in the Saudi Capital, Hexagon Data Center meets the highest international standards, aiming to ensure maximum levels of availability, security, and operational readiness for government data centers.

The Kingdom has also established nine cloud regions, in partnership with global tech leaders:

1. Google Cloud’s region in Dammam. Launched in November 2023, this region is Google’s official cloud hub in Saudi Arabia, obtaining a Class C License from the Communications, Space and Technology Commission (CST) and supported by $1 billion in strategic cloud and AI infrastructure investments by Google. 
2. Oracle (three cloud regions in Riyadh, Jeddah, and NEOM). Oracle launched its first Oracle Cloud Region in Jeddah in 2020 to provide over 100 core cloud and AI services, including Oracle Autonomous Database, OCI Compute, and enterprise SaaS apps. In October 2021, the company announced its partnership with NEOM Tech and Digital Hold Company to establish a hyperscale data center in NEOM to serve the ambitions of the public and private sector across the region and beyond. To further strengthen its commitment to the Kingdom, the tech giant launched a new cloud region in Riyadh in August 2024 to help businesses increase performance, protect data, and access Oracle's full array of cloud services. 
3. Huawei. Launched in 2023, the Huawei Cloud Riyadh Region is the company’s first region in the Middle East, offering three availability zones to promote digital-led economic growth in the Kingdom.
4. Alibaba. Launched in 2022, Alibaba launched its Cloud Region in Riyadh with two availability zones operated by the Saudi Cloud Computing Company (SCCC).
5. Tencent. Tencent Cloud launched its first Middle East Cloud Region in Riyadh in 2025, featuring two availability zones with full redundancy, advanced cloud services, and AI capabilities.
6. AWS. In 2024, Amazon Web Services (AWS) announced its plans to build three cloud regions in Saudi Arabia, including an AI Zone in collaboration with HUMAIN.
7. Microsoft Azure. Set to be launched in the fourth quarter (Q4) of 2026, Microsoft's Saudi Arabia East Azure datacenter region will enable government and private sector organizations to access supported Microsoft cloud and AI services and host eligible workloads and data locally in the Kingdom.

Together, these projects demonstrate Riyadh’s ambition to move beyond adopting AI technologies and become a major location for developing, hosting, and scaling them.

Key events powering Riyadh’s AI ecosystem

Riyadh’s AI ambitions are reinforced by a growing network of partnerships with global technology companies, the establishment of regional headquarters (RHQs), and the organization of major international technology and entrepreneurship events. These efforts are helping the city attract investment, expand access to cloud and computing infrastructure, develop local talent, and connect Saudi startups with global investors and technology leaders.

Major tech companies moved their regional headquarters to Riyadh to better serve the broader Middle East region. For instance, Lenovo opened its Middle East, Turkey, and Africa (META) RHQ in Riyadh this year, placing Saudi Arabia at the center of its regional leadership and supporting customers and partners across more than 60 countries. Similarly, other tech giants, including Google, Microsoft, Salesforce, Groq, and Tencent Cloud, announced strategic investments and initiatives to enhance AI, cloud computing, data centers, and skills development. 

The Kingdom is also strengthening its AI ecosystem by hosting and organizing flagship events and exhibitions that provide spaces for companies, policymakers, researchers, investors, and startups to exchange knowledge, announce partnerships, showcase emerging solutions, and reinforce Riyadh’s position as a meeting point for the global AI industry. Some of these leading events are:

• LEAP, the massive annual global technology event held in Riyadh, focusing on major tracks, notably AI, fintech, and cybersecurity.
• Black Hat, the leading cybersecurity conference and exhibition that gathers cybersecurity professionals, cutting-edge technologies, solution providers, and decision-makers from around the world. Black Hat MEA 2026 is scheduled to take place in Riyadh from 1 to 3 December.
•  Middle East Entrepreneurship AI & Analytics Summit, a global gathering for senior government, enterprise, and technology leaders to explore the technologies, strategies, and use cases shaping Saudi Arabia’s AI-powered future. The 15^th edition of the ME Entrepreneurship AI & Analytics Summit will convene in the Saudi capital on 28 October.
• NextGen2030 2.0 Youth & AI Summit Riyadh 2026, the international youth summit bringing together young leaders, innovators, entrepreneurs, and changemakers from around the world to explore the future of AI, youth leadership, innovation, and entrepreneurship.
• Global AI Show, Saudi Arabia’s biggest AI conference where the next generation of AI innovation meets real business opportunity.

Upskilling national AI talent

A sustainable AI hub needs more than investment; it needs people. That is why Saudi Arabia is focusing on developing local AI talent, improving digital skills, and preparing the workforce for AI-driven business environments. SDAIA is leading these efforts by launching major initiatives, notably the SDAIA Academy, which provides professional programs and practical bootcamps in areas such as machine learning (ML), large language models (LLMs), data engineering, computer vision, generative AI, AI-agent development, and responsible AI.

Global tech companies entering the Saudi market are also contributing to upskilling Saudi national talent in AI and other emerging technologies by transferring technical knowledge, delivering specialized training, and creating practical pathways for Saudi professionals to gain experience with widely used industry tools. For instance, Salesforce pledged to provide upskilling opportunities to 30,000 Saudi citizens by 2030 through its AI Center of Excellence (CoE) in Riyadh. Similarly, AWS partnered with the Saudi Ministry of Communication and Information Technology (MCIT) on a national program designed to qualify more than 20,000 Saudi citizens in artificial intelligence, machine learning, and cloud computing.

AWS also launched the AWS Builder Accelerator to provide Saudi graduates and early-career technology professionals with intensive training in cloud computing and AWS technologies. Additionally, Google’s Gemini unveiled an initiative to give one million students at Saudi universities access to advanced AI technologies, helping them develop digital skills and prepare for future jobs.

Meanwhile, Microsoft’s joint training programs have benefited more than one million beneficiaries over the past years. This included training over two-thirds of a million participants in SDAIA’s SAMAI initiative, empowering over 5,000 women through specialized programs, and training thousands of students and over 100,000 teachers.

Finally, Riyadh’s emergence as a global AI hub is being built on more than ambitious targets. Through large-scale computing and cloud infrastructure, partnerships with global technology companies, international events, and broad-based skills development, the Saudi capital is creating an ecosystem capable of turning AI investment into practical economic and social value. As the city continues to attract tech giants, develop local expertise, and expand its capacity to host and deploy advanced AI systems, its role in the global technology landscape is likely to become increasingly significant. 

Kholoud Hussein 

For years, the artificial intelligence race was largely conducted in English. The world’s most powerful foundation models could write, code, summarize, and reason across a growing range of tasks, but their performance often weakened when they encountered the realities of Arabic: its grammatical complexity, regional dialects, cultural references, code-switching, and the enormous gap between formal written Arabic and the language people actually speak.

Saudi Arabia increasingly sees that gap not simply as a linguistic problem, but as a technology and investment opportunity.

The Kingdom is now emerging as one of the most ambitious markets for Arabic artificial intelligence, with government-backed companies, startups, global technology groups and large enterprises building different layers of an Arabic AI ecosystem. At the center of that effort is the development of proprietary language models that can understand Arabic on its own terms rather than treating it as a translation layer on top of English-centric systems.

The scale of the opportunity is reflected in the capital flowing into the wider Saudi AI ecosystem. Saudi Arabia’s National Strategy for Data and AI targets $20 billion in local and foreign investment and at least 300 active AI startups by 2030. Meanwhile, the country’s broader AI market is estimated at $2.14 billion in 2025 and projected to reach $16.9 billion by 2032, according to MarketsandMarkets.

Yet the more important question is not how much Saudi Arabia spends on AI.

It is whether the Kingdom can turn that capital into proprietary intellectual property, globally competitive companies and models that become infrastructure for the Arabic-speaking digital economy.

The Arabic gap is bigger than translation

Arabic presents a distinctive challenge for AI developers.

The language exists simultaneously in Modern Standard Arabic, classical forms, and dozens of spoken dialects. A Saudi user, for example, may switch between formal Arabic, Saudi dialect, English terminology, and industry-specific language within the same conversation.

For AI models trained predominantly on high-quality English data, this creates a structural disadvantage.

Research cited by Arab News estimates that only around 15% of Arabic text available online is clean enough for large-language-model training, compared with more than 50% for English. Developers therefore face not only a shortage of data but also a shortage of high-quality, correctly labelled and culturally representative data.

That creates an opening for companies willing to build the data layer themselves.

This is where Saudi startups and emerging technology companies are becoming important. Their advantage is not necessarily the ability to spend hundreds of millions of dollars training a general-purpose model. It is their proximity to Arabic users, enterprise data, dialects and specific commercial problems.

The resulting market is therefore developing on several levels.

At one end are foundation models such as HUMAIN’s ALLAM, developed in Saudi Arabia for Arabic-first use cases. At another are companies such as Riyadh-headquartered Intella, which has built proprietary speech technologies and small language models designed around Arabic dialects and enterprise applications. Between them sits a growing ecosystem of startups developing voice agents, vertical models, translation systems, enterprise copilots and domain-specific AI applications.

Together, they are attacking the Arabic AI problem from different directions.

From consuming AI to owning the model

The launch of HUMAIN in May 2025 marked a major change in Saudi Arabia’s approach.

The PIF-owned company was established to operate across the entire AI value chain, from data centers and cloud infrastructure to advanced models and applications. Its flagship ALLAM model is positioned as one of the world’s most powerful multimodal Arabic large language models.

HUMAIN later launched HUMAIN Chat, powered by ALLAM 34B. The company said the model was trained on more than 500 billion Arabic tokens and refined using hundreds of domain experts and evaluators. The system was designed to support Arabic and English while incorporating regional and cultural context.

The significance extends beyond having another chatbot.

A proprietary foundation model gives Saudi Arabia control over an important part of the technology stack: the data, model weights, training processes, deployment environment, and intellectual property.

That matters particularly for government, financial services, healthcare, energy and other regulated industries where data residency, security and customization can be as important as raw model performance.

It also changes the economics of the market.

Instead of paying indefinitely for access to foreign foundation models, Saudi companies can increasingly build products on locally developed models or adapt them to specific requirements.

Startups are attacking the problem from the bottom up

The startup opportunity is not necessarily to compete head-on with the largest global AI laboratories.

It is to solve the problems those laboratories have historically struggled to solve.

Intella is one example.

The Saudi-headquartered company, founded in 2021, focuses on Arabic speech intelligence rather than trying to become another general-purpose ChatGPT competitor. Its technology covers speech-to-text, text-to-speech, analytics and industry-specific small language models, with support for more than 25 Arabic dialects. The company says its proprietary speech-to-text technology has reached 95.73% accuracy.

Its commercial proposition illustrates where startups can create value.

A bank does not necessarily need the world’s largest LLM. It needs an AI system that understands how its customers actually speak, recognizes local expressions, complies with data requirements, and can connect those conversations to a banking workflow.

The same logic applies to telecom operators, government agencies, insurers and retailers.

In September 2025, Intella raised $12.5 million in a Series A led by Prosus, taking its total funding to $16.9 million. The round included Saudi investors such as Wa’ed Ventures and Hala Ventures and was intended to support R&D, product development and regional expansion.

The figure is modest compared with infrastructure investments in Saudi AI, but strategically important.

It demonstrates that private capital is beginning to finance the specialist layers that make Arabic AI commercially useful.

The corporate-startup model is becoming more important

Saudi Arabia’s Arabic AI ecosystem is also developing through an unusual combination of startups, sovereign capital and global technology companies.

Google Cloud and PIF announced a $10 billion partnership to advance an AI hub in Saudi Arabia, with the initiative involving HUMAIN. The partnership includes research into Arabic-language models and Saudi-specific AI applications, including work to enhance the Arabic capabilities of Google’s Gemini models using additional Arabic datasets.

The model is significant because it illustrates how international technology companies can provide capabilities that startups and local companies may struggle to build independently: compute, cloud infrastructure, specialized chips, model-development platforms, and global distribution.

In return, Saudi Arabia offers something equally valuable: access to a rapidly digitizing market, large enterprise customers, government use cases, capital and a concentrated pool of Arabic data and talent.

The relationship is increasingly moving beyond conventional technology procurement toward co-development.

In August 2026, Microsoft and HUMAIN announced a long-term strategic collaboration under which ALLAM models are planned to become available through Microsoft Foundry and the Microsoft 365 Copilot ecosystem. The partnership also brings HUMAIN AI specialists together with Microsoft’s Forward Deployed Engineers to develop and deploy Arabic-language AI solutions for organizations.

This is strategically important for Saudi startups as well.

A local model becomes significantly more valuable when it can be distributed through a global enterprise platform.

The same principle is visible in AWS’s expanding relationship with HUMAIN. At LEAP 2026, AWS announced plans to make ALLAM available through Amazon Bedrock, while expanding infrastructure capacity for AI workloads in the Kingdom.

The emerging architecture is therefore not simply “Saudi versus Silicon Valley.”

It is increasingly a partnership model in which Saudi companies own local intelligence and context while international technology companies provide global infrastructure, platforms and distribution.

Aramco adds another dimension

Saudi Arabia’s AI ambitions are also being accelerated by its largest corporate institution: Aramco.

The company has developed its own industrial large language model, trained on decades of proprietary Aramco data. The model is designed for applications ranging from analyzing drilling and geological information to forecasting refined-product markets. Aramco has said its decision to develop its own generative AI capability reflects the need to capture the benefits of AI while managing technology and data risks.

That approach could become particularly influential in the next phase of Arabic AI.

The most valuable models may not be the largest models.

They may be the models that understand a particular industry better than a general-purpose system does.

Energy, banking, government, healthcare and legal services all contain large amounts of proprietary information that cannot simply be uploaded to a public AI platform.

This creates a market for smaller, highly specialized models trained or fine-tuned on proprietary datasets.

For startups, that is a much more realistic opportunity than attempting to reproduce the enormous capital expenditure of frontier-model developers.

The investment story is much larger than LLM funding

One of the biggest challenges in measuring the Arabic LLM opportunity is the lack of a separate investment category.

Saudi Arabia does not publish a single figure showing how much has been invested specifically in Arabic foundation models. Much of the disclosed capital is instead bundled into broader AI infrastructure, cloud computing, data centers, chips, venture capital, and AI applications.

The numbers nevertheless show the scale of the ecosystem being constructed.

Saudi Arabia announced nearly $15 billion of investments and agreements at LEAP 2026 across AI infrastructure, data centers, cloud computing, technology manufacturing and venture capital. AWS alone announced a planned investment of more than $5.3 billion in its Saudi cloud infrastructure region, while other agreements covered major data-center and AI projects.

Aramco has also committed approximately SAR9.9 billion ($2.6 billion) in cash contributions to HUMAIN in 2026, alongside the transfer of AI assets, according to Aramco.

Earlier commitments included the $10 billion PIF-Google Cloud AI hub and Saudi Arabia’s $1.5 billion commitment linked to AI-chip company Groq.

These figures should not be added mechanically: some represent partnerships or multi-year commitments, while others span infrastructure and AI rather than Arabic models specifically.

But collectively they illustrate the capital intensity of the market Saudi Arabia is attempting to build.

At the startup level, the numbers are smaller but equally revealing.

Wa’ed Ventures has a $500 million technology-focused fund and had deployed approximately $270 million across more than 75 companies by 2024. Its mandate includes AI and other deep technologies, while requiring certain international technology investments to localize operations in Saudi Arabia.

The Kingdom is therefore developing both sides of the capital equation: large strategic investment for infrastructure and smaller venture capital for experimentation and commercialization.

How much more money could follow?

The next investment cycle is likely to move from infrastructure toward monetization.

Saudi Arabia’s National Strategy for Data and AI targets $20 billion in local and foreign investment and at least 300 active AI startups by 2030. Meanwhile, the country’s AI market is forecast to grow from $2.14 billion in 2025 to $16.9 billion by 2032.

The enterprise AI segment alone is forecast to rise from $810.6 million in 2024 to more than $5.3 billion by 2030, representing a 37.8% compound annual growth rate.

These forecasts suggest that future capital will increasingly follow commercially proven applications.

That could benefit Arabic-model startups because the value of an LLM is ultimately determined by what sits on top of it.

A model that understands Saudi dialects becomes more valuable when it powers a bank’s customer service. A government-specific model becomes more valuable when it automates document processing. An industrial model becomes valuable when it improves maintenance, engineering or energy efficiency.

The transition is therefore likely to be from model building to model commercialization.

The next frontier: Arabic AI agents

The most important development over the next few years may not be larger Arabic LLMs, but more specialized AI agents.

Global AI development is already moving from systems that generate answers to systems that can perform tasks. In Saudi Arabia, Arabic-first agents could combine language models with enterprise databases, government systems, CRM platforms, and workflow tools.

That creates a much larger commercial opportunity.

An Arabic AI agent for a bank could understand a customer’s dialect, verify information, retrieve account data, and complete a transaction.

A government agent could interpret Arabic documents, identify regulatory requirements, and route applications.

An industrial agent could combine technical manuals, sensor data and historical operational information to assist engineers.

The underlying foundation model is only one component.

Data, security, retrieval systems, workflow integration, and domain expertise increasingly determine whether the technology produces economic value.

This is precisely where startups can complement the large capital providers.

The real competition will be over data and talent

The biggest constraint on Saudi Arabic AI may eventually cease to be funding.

It could be data and people.

Developing an Arabic model requires enormous quantities of high-quality training data, but collecting that data raises questions about copyright, privacy, ownership, dialect representation and governance.

Saudi Arabia has an advantage in that it can combine government datasets, corporate information, Arabic digital content and local linguistic expertise. But access to data does not automatically make it usable for training.

The country therefore needs a broader data economy alongside its AI economy.

Talent will be equally critical.

HUMAIN said its ALLaM team included more than 120 AI specialists, including 35 PhD holders, while Aramco has committed to training more than 6,000 AI developers through collaborations involving institutions such as Imperial College, Caltech and KAUST.

For startups, the competition for this talent could become intense.

The next generation of Arabic AI companies will require machine-learning researchers, computational linguists, data engineers, Arabic-language experts, cybersecurity specialists and enterprise software developers.

From linguistic gap to economic infrastructure

Saudi Arabia’s Arabic AI push is ultimately about more than language.

It is an attempt to establish ownership over a layer of digital infrastructure that could sit underneath the region’s future economy.

The opportunity is significant because Arabic is spoken by hundreds of millions of people, while businesses and governments across the region are accelerating digital transformation.

But building a competitive Arabic LLM does not automatically create a successful technology business.

The coming years will test whether Saudi companies can turn models into recurring revenue, whether startups can scale beyond government contracts, whether proprietary data can become a defensible advantage, and whether international partnerships create technology transfer rather than simple dependence on foreign infrastructure.

The strongest companies are likely to occupy the space between these worlds.

They will understand Arabic deeply enough to solve problems global models struggle with, but build products sophisticated enough to compete internationally.

That is where Saudi startups have a potentially decisive role.

The Kingdom does not need to build every component of the global AI stack itself. It needs to identify the layers where local knowledge creates an enduring advantage — Arabic language, regional data, industry expertise, sovereign deployment and culturally relevant applications — and build globally competitive businesses around them.

The first phase of Saudi Arabia’s AI strategy was about attracting infrastructure and capital.

The next phase is about turning that infrastructure into intellectual property.

And the ultimate test will be whether Arabic AI becomes something Saudi Arabia merely helped develop — or an industry in which Saudi companies own the models, data, applications, and businesses that serve the next generation of the Arabic-speaking digital economy.