The panel discussion “VC’s Unfiltered: The Toughest Founder Questions, Answered Live” at Global AI Show Riyadh 2026 brought the kind of conversation founders usually wish they could hear before walking into an investor meeting. For anyone interested in AI startup funding, this was not the polished version. Not the soft encouragement. The real one.
The session looked at what venture capital actually wants from AI startups now that foundation models, AI agents, and compute-heavy products are changing the rules. Everyone talks about opportunity. This panel focused more on the uncomfortable part: what is still worth funding, what makes investors hesitate, and why strong traction does not always save a company from deeper technical doubts.
The discussion was moderated by Megha Merani, Senior Editor and Chief Correspondent at Arabian Gulf Business Insight. The panel featured Mohammed Alzubi, Founder and Managing Partner at Nama Ventures; Ali Abussaud, Founder and Chief Executive Officer at HALA Capital; Michael Lints, Founding Partner MENA at Golden Gate Ventures; Amal Dokhan, Managing Partner at 500 Global; and Halah Al-Jubeir, Managing Director at Antler Saudi Arabia.
What Is Actually Venture-Backable in AI?
One of the sharper questions from the session was simple on the surface: in the era of foundation models and AI agents, what is truly venture-backable? Models, data, or distribution?
It is a harder question than it sounds. Building a model is no longer automatically impressive. Many startups can now build on top of existing foundation models and move quickly. That lowers the barrier to entry, but it also makes differentiation more fragile. If another team can copy the same workflow, connect to the same APIs, and target the same users, investors will ask what really protects the business.
Data still matters, especially when it is proprietary, hard to collect, or deeply tied to a specific industry. But data alone is not always enough either. A startup can have interesting datasets and still fail to build a product customers truly need. Distribution may be the least glamorous answer, but it often becomes the most important one. Can the company reach customers faster than others? Can it convert demand? Can it stay close enough to the market to keep improving?
That is where many AI startups face the real test. The product may be technically strong, but if the company has no path to users, no wedge into the market, and no reason customers should stay, the story weakens fast.
Strong Traction Does Not Hide Technical Red Flags
The panel also tackled a question many founders probably do not love: which technical red flags make investors walk away from an AI startup despite strong traction?
Traction is powerful, but it does not erase everything. In AI, investors are paying closer attention to what sits underneath the growth. Is the startup too dependent on a single model provider? Are the unit economics broken because every user action costs too much in compute? Is the product just a thin layer over someone else’s platform? Is there a real technical moat, or only a nice interface with good timing?
These questions matter because AI startups can scale quickly and look impressive early. A product can gain users fast if it solves an obvious pain point. But once usage grows, the hidden costs appear. Inference costs. Infrastructure limits. Data quality problems. Model reliability issues. Security concerns. Compliance exposure. Suddenly the startup is not just growing. It is becoming expensive to operate.
That is the part founders cannot ignore. Investors may like growth, but they also want to know whether the company becomes stronger as it scales or weaker under its own usage.
The Thin Wrapper Problem Is Still Real
There was an unspoken tension running through the discussion: a lot of AI startups look similar right now.
Some are useful. Some are clever. Some may even grow fast. But many are still built too close to the foundation model layer, with limited defensibility beyond branding, user experience, or early market access. That does not mean they are worthless. It does mean the fundraising story needs to be sharper.
Founders need to explain why their company survives when models get cheaper, when competitors appear, when platforms release similar features, or when customers realize they can build part of the workflow internally. That is not easy. But it is the conversation serious AI founders now have to prepare for.
A startup does not need to own every layer. It does need to know which layer it owns.
Capital Is Starting to Meet Compute
Another major theme was the relationship between venture capital, sponsors, and infrastructure. The panel raised the question of how VCs and sponsors can collaborate to deliver infrastructure-as-a-service for early-stage deeptech startups.
This is becoming more important because deeptech founders do not only need funding. They need access. Access to compute, cloud credits, data environments, testing infrastructure, enterprise networks, and technical support. Money helps, but money alone does not solve the infrastructure burden.
For early-stage AI and deeptech companies, compute can become a survival issue. A startup may have the right idea and the right team, but without infrastructure support, it can burn through capital before proving the product. That creates a different role for investors. They are no longer only writing checks. The better ones may need to help founders unlock the infrastructure needed to build and test serious technology.
This is especially relevant in markets where national AI ambitions are rising. If countries want more AI startups to succeed locally, then funding, compute, cloud access, and sponsor-backed infrastructure cannot be treated as separate conversations.
Execution Speed Is Becoming Hard to Ignore
The panel also asked whether VCs are now backing execution speed over vision as AI compresses time-to-scale.
Founders love vision. Investors do too, at least in pitch decks. But AI has changed the tempo. A company can prototype faster, launch faster, test faster, and sometimes scale faster than before. That means execution is becoming a louder signal.
A big vision still matters. Without it, the company can become a feature, not a business. But vision without speed feels weaker now. Investors want to see whether founders can move through uncertainty quickly. Can they ship? Can they learn from customers? Can they control costs? Can they change direction without losing the plot?
The old startup rhythm is not gone, but it is compressed. Months can feel like years in AI. A founder who waits too long to validate a product may find that the market has already moved, the model layer has changed, or a platform has absorbed the use case.
What Founders Should Take From This
The most useful part of the discussion was not that it gave founders one perfect answer. It did not. That would have been too neat anyway.
The real message was that AI founders need to be more honest about what they are building. If the moat is data, prove why the data is hard to get. If the advantage is distribution, show how the company reaches and keeps customers. If the edge is technical, explain why it will still matter when models improve. If the business depends on heavy compute, show that the economics can survive real usage.
Founders also need to understand that investors are looking past the AI label. The market is crowded. Everyone has a demo. Everyone says agentic. Everyone says proprietary. The companies that stand out are the ones that can explain, without hiding behind buzzwords, why they can build something durable.
AI Startups Are Entering a Tougher Funding Phase
The “VC’s Unfiltered” panel at Global AI Show Riyadh 2026 made one thing clear: AI startup funding is not slowing down because investors lost interest. It is becoming more selective because the category is maturing.
There is still capital for strong founders. There is still excitement around AI agents, deeptech infrastructure, vertical AI, automation, and new enterprise workflows. But the easy story is fading. Calling something an AI startup is not enough. Building a thin layer over a foundation model is not enough. Early traction is helpful, but it is not always proof of long-term value.
The next wave of AI startups will need more than speed, more than a polished demo, and more than a convincing pitch. They will need infrastructure awareness, technical clarity, a real customer path, and a sharper answer to the question every investor is quietly asking: why this company, and why now?
