Most conversations about AI assume that the user writes in English, or at least in a language that looks a lot like English on the internet. For a large part of the world, that assumption is simply wrong.
I work across Europe, Africa and the Middle East, and I keep running into the same gap. Arabic is one of the world's major languages, yet the AI tools most people use still treat it as a secondary concern. I think this is one of the clearest blind spots in the market today, and also one of the clearest opportunities.
A huge language with a small digital footprint
Start with the numbers. UNESCO, which has celebrated World Arabic Language Day every 18 December since 2012, describes Arabic as a language spoken daily by more than 400 million people. It is one of the six official languages of the United Nations.
Now look at the web. According to W3Techs, which tracks the content language of websites, Arabic is used by about 0.6% of the websites whose language it can identify (data as of November 2026).
That gap matters for AI. Large language models learn from text, and much of that text comes from the public web. When a language is massively spoken but thinly represented online, models see less of it, and what they see is skewed toward certain registers and topics. The result is predictable: answers that are grammatically acceptable but culturally flat, translations that sound like a textbook, and a noticeable drop in quality compared with English.
Arabic is not one language, and the market underestimates it
The second problem is less visible from the outside, and it is the one I find most interesting.
When people say "Arabic", they often mean Modern Standard Arabic, the formal language of news, official documents and much of written content. But that is not how most people talk. In daily life, people speak their dialect: Moroccan Darija, Egyptian, Levantine, Gulf, Iraqi and many others. The ISO 639-3 standard actually treats Arabic as a macrolanguage, and Ethnologue lists 28 individual language varieties under it.
These are not small accent differences. Vocabulary, grammar and expressions can vary enough that speakers from distant regions sometimes switch to a more standard register to understand each other. On top of that, many people write their dialect online in Latin characters mixed with numbers (often called Arabizi), and switch between Arabic, French and English in the same sentence.
For an AI system, this is hard. A model that handles formal Arabic reasonably well can still misunderstand a voice note in Darija or a WhatsApp message in Egyptian slang. For a business, that is the difference between a tool customers use and a tool they abandon after the second try.
Why the market keeps underinvesting
I see a few reasons why this gap persists.
Benchmarks favour formal language. Evaluations of Arabic models usually focus on standard Arabic. A model can score well and still struggle with how real customers write.
Data is fragmented. Dialect data is spread across countries, platforms and scripts. Collecting and cleaning it requires local knowledge, not just compute.
Global products localise last. For many large AI companies, Arabic is one language on a long list. Localisation often means translating the interface, not rethinking how the product understands people.
The buyers are underestimated. Businesses and public institutions across the region are actively looking for AI that works in their context. In our projects, we often see strong demand for automation in customer service, sales and internal operations, held back mostly by tools that do not speak the way customers speak.
What building for Arabic actually requires
This is not a theoretical concern for me. Several companies in our group exist precisely because of this gap. Thaler Group is an AI-powered growth agency for the Arab world. ArabClaw is a community building an Arabic reference for OpenClaw, the open-source personal AI agent platform. And Fares AI is an assistant designed to speak Arabic dialects.
From that work, a few principles stand out.
Design for dialects from day one
If your product will be used by real people in the region, assume they will write in dialect, mix languages and use Latin script. Treat that as the normal case, not an edge case. Test with real messages from your target market, not only with formal text.
Meet people where they already talk
In much of the Arab world, daily life runs through messaging apps. An assistant that lives inside WhatsApp or Telegram, and understands voice notes, is far more natural than one more app to download. That is also why we built OpenClaw-based agents to live in messaging apps, a choice I explain further in Building Nino: lessons from launching a consumer AI assistant.
Respect cultural context, not just language
Tone, politeness, religious and social references, and local business habits all matter. A correct translation can still feel foreign. Products that work best are built with people from the region, not only for them.
Keep the use cases concrete
As with any AI project, the winners are rarely the flashiest demos. They are specific, repetitive tasks done well: answering customers, qualifying leads, organising information. I wrote about this more generally in AI agents in business: what actually works in 2026. The same rules apply in Arabic, with one extra requirement: the system must truly understand the customer.
Why I think this is an opportunity, not a charity case
It would be easy to frame Arabic AI as a social cause, a matter of digital inclusion. That framing is not wrong, but it undersells the reality. This is a large population, with fast-growing digital economies, ambitious public sectors and businesses eager to adopt AI.
The companies that take Arabic seriously, dialects included, will not be serving a niche. They will be serving one of the largest language communities in the world, at a time when most competitors are still treating it as an afterthought.
Entrepreneurs come from everywhere, and so do customers. Building AI that understands them is not a favour. It is good business.
Key takeaways
- Arabic is spoken daily by more than 400 million people (UNESCO), yet represents about 0.6% of websites with known content language (W3Techs).
- Real usage is dialectal, multilingual and often written in Latin script, which general models handle poorly.
- Benchmarks, fragmented data and late localisation keep the market underinvested.
- Build for dialects from day one, inside messaging apps, with local cultural context.
- Arabic AI is a major business opportunity, not a niche.