NBA Finals Ad Cost $2,000 and Was Made With AI

Pivot 5
June 13, 2025

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Kalshi Airs $2,000 AI-Generated Spot During NBA Finals

Betting platform Kalshi aired an AI-generated commercial during Wednesday night’s NBA Finals broadcast. The ad was produced with Google’s new Veo 3 text-to-video generator for about $2,000.
AI filmmaker PJ Accetturo said he created 15 usable clips from 300–400 Veo generations and assembled the spot in CapCut or Adobe Premiere within two to three days. He reports this workflow cut costs by 95 percent versus a traditional commercial shoot.
The primetime placement shows AI-generated video jumping from social media to national television just weeks after Veo 3’s launch. Running during the NBA Finals signals that major broadcast inventory is already open to ultra-cheap, single-creator AI content.

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Professors at the New School and University of Arizona are inundated with AI-generated assignments they can recognize but cannot conclusively prove. Detection tools are so unreliable that most faculty skip formal cheating cases altogether.
Peer-reviewed studies cite false positives and biased results against non-native English writers, undercutting trust in existing software. Startups like Truely and Proctaroo promise better monitoring, yet their own founders say the solutions are nowhere near definitive.
With technology racing ahead of campus policies, educators are rewriting syllabi and shifting to in-class or iterative work to safeguard learning. The scramble exposes higher education’s broader struggle to measure genuine student output in an AI-saturated environment.

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San Francisco startup XRobotics says its countertop robot, the xPizza Cube, now makes 25,000 pizzas each month for restaurant customers. The company just closed a $2.5 million seed round led by FinSight Ventures to ramp up production and installations.
The washing-machine-sized machine leases for $1,300 a month over three years, can crank out up to 100 pies an hour, and adjusts to styles from Detroit to deep dish. Co-founder and CEO Denis Rodionov claims the system cuts staff prep time by up to 80 percent by automating sauce, cheese, and pepperoni placement.
Unlike failed pizza-bot pioneer Zume, XRobotics limits its ambition to assisting existing kitchens rather than reinventing them, letting the unit slip into standard restaurant footprints and budgets. With U.S. pizza shops exceeding 73,000 locations, management says the company will next target Mexico and Canada while staying focused solely on pizza.

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Andreessen Horowitz analyzed hundreds of AI startups and found median enterprise companies pass $2 million in ARR during their first year and secure a Series A nine months after monetization. Median consumer AI companies reach $4.2 million in ARR and land a Series A in eight months.
The pre-AI “best in class” target of $1 million ARR in 12 months now sits at the low end of the new range. Examples such as Lovable hitting $50 million in six months, Cursor reaching $100 million in its first year, and Gamma posting $50 million with under $25 million raised show how leaders are pulling away.
One-third of consumer AI startups raise large sums to train their own models and experience sharp revenue jumps after each release before plateauing. Paying users who convert then retain as well as in pre-AI businesses, underscoring that consumer AI is now a credible, revenue-generating segment.

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Altman says humanity is past the “event horizon” and digital superintelligence is close, with AI systems already outperforming humans in many ways. His blog post frames the recent advances like GPT-4 as proof that the most unlikely breakthroughs are already behind us.
He notes ChatGPT is “more powerful than any human who has ever lived” and is used daily by hundreds of millions, amplifying both positive impact and potential harm. He lays out a near-term timeline that includes cognitive agents in 2025, novel-insight systems in 2026, and capable robots in 2027.
Altman argues that abundant intelligence and energy will remove long-standing limits on progress, eventually driving the cost of intelligence toward the price of electricity. He contends that solving alignment and ensuring broad, inexpensive access to superintelligence are now the central challenges for industry and society.

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