Four signals today that video, retail, and hardware are turning AI from demos into durable businesses

If you are building in AI right now, today’s headlines read like a progress report on a bigger shift. Consumer traction is accelerating, enterprise adoption is operational not experimental, and the biggest lab in the room is quietly assembling a supply chain. Meanwhile, a top VC firm is basically waving a flag that says your old SaaS instincts might get you hurt.

Here’s what mattered today, and what founders and investors should be watching next.

Higgsfield hits a 1.3B valuation and makes AI video feel inevitable

A Snap veteran’s new company just proved that generative video can scale like a consumer app and monetize like a real business.

Higgsfield, founded by former head of Generative AI at Snap Alex Mashrabov, extended its Series A with an additional $80 million, bringing the round to $130 million total and putting the company at a reported $1.3 billion valuation. The product is a consumer and creator focused tool for creating and editing AI generated video, and it is moving fast from novelty to workflow.

The traction is the story. Higgsfield says it crossed 15 million users nine months after launch and is already at a $200 million annual revenue run rate, up from $100 million just two months prior. That kind of revenue velocity is rare even in the frothiest consumer cycles, and it suggests two things operators should internalize. First, AI video is becoming a core marketing surface, not just a creative toy. Second, time to monetization for breakout AI consumer apps is compressing because the output is directly tied to revenue generating activities like ads, social, and commerce.

Strategically, Higgsfield also signaled a shift in who uses the product, from casual creators toward professional social media marketers. That is where budgets live and where retention gets sticky, because content calendars do not take days off. With Accel, Menlo Ventures, AI Capital Partners, and GFT Ventures participating, the market is effectively voting that the category will produce large outcomes. What to watch next is whether Higgsfield can defend on distribution and workflow integration, because in video, models commoditize but channels and habits compound.

OpenAI starts shopping for US hardware suppliers  

OpenAI is acting less like an API company and more like a future device and robotics platform with a domestic supply chain.

Bloomberg reports that OpenAI is soliciting proposals from US based hardware makers as it plans an expansion into consumer devices, robotics, and data center technology. There is not much subtlety here. This is a signal of intent to move down the stack and into physical product development, which changes the competitive map for everyone who assumed the frontier labs would stay purely software.

For founders, this matters because vertical integration creates new choke points and new partner opportunities at the same time. If OpenAI builds or heavily influences the reference architecture for devices and robotics, it can define the default interfaces, the safety and control layers, and the distribution relationships. That can be great if you are building components, sensors, actuation, testing infrastructure, or specialized manufacturing capability. It can be existential if your “device startup” is mostly a wrapper around someone else’s model without a differentiated hardware advantage.

For investors, the US supplier angle is equally strategic. It hints at a supply chain posture shaped by geopolitics and export controls, plus a desire for tighter iteration loops between model teams and hardware teams. What to watch is whether this becomes a quiet ecosystem play, similar to how smartphone platforms cultivated suppliers, or a more direct push into branded consumer hardware. Either way, OpenAI is broadening the battlefield.

Menlo says there are no AI markets, only proto markets 

If you are applying the SaaS playbook to AI, Menlo’s argument is that you are optimizing for a world that no longer exists.

Menlo Ventures put out a clear thesis that “AI markets” today are proto markets, meaning they can generate meaningful revenue but lack stable boundaries, durable moats, and settled unit economics. The practical implication is blunt. Category definitions are still melting, product expectations mutate fast, and foundation model releases routinely force application companies to rebuild architecture and workflows.

The examples are telling. Claude Sonnet 4.5 landing reportedly pushed Cognition to rebuild Devin. Manus has reconstructed its agent framework multiple times. On the enterprise side, offerings like Salesforce Agentforce and ServiceNow NowAssist blur lines between categories like sales, IT, support, and search. The point is not that the products are bad. The point is that the ground under them is moving, so “ship once then defend” is not a real strategy right now.

Menlo also flags a more uncomfortable truth. In areas like code and voice AI, pricing is sometimes at or below cost, which means classic SaaS margin assumptions are not yet reliable. Their prescription is to compete on product plasticity and learning velocity. Build systems that absorb user behavior, evolve quickly, and become “hackable by design,” borrowing language from Anthropic’s Claude Code creator Boris Cherny. What to watch is which companies turn this into a defensible loop, where usage teaches the product faster than competitors can copy, and where distribution and workflow lock in replaces feature lock in.

Walmart’s Sparky shows AI at scale 

Walmart is done tinkering and is using agents in customer journeys, store operations, and fulfillment where impact is measurable.

Walmart is leaning hard into AI transformation, including a partnership with Google’s Gemini platform and continued work with OpenAI. Its in house agent, Sparky, already fields customer service questions and nudges repeat purchasing with reminders for appointments and prescriptions. Walmart also enabled purchases inside ChatGPT in Fall 2025, and the Gemini integration lets users access membership perks and sync purchases into a live Walmart cart.

This is what operationalized AI looks like in a physical first business. Walmart associates are using a backend agent to prioritize tasks like restocking and responding to in store issues like spills. Fulfillment centers are using AI to predict which products to store. None of that is flashy, and that is the point. It is the unglamorous automation of thousands of micro decisions that produces real margin and customer experience gains.

The strategic layer is distribution. Walmart is positioning itself to show up inside AI driven inquiry results that may not start as commerce questions but can convert into sales. Daniel Danker, Walmart’s EVP of AI acceleration, product and design, summed it up with unusual clarity.

This is the year where tinkering becomes transformation, and the bigger risk is not being out front.

What to watch is the retail arms race this triggers. Once a retailer the size of Walmart treats AI agents as a core interface, every competitor is forced to match on speed, personalization, and presence inside the new AI shopping funnels.