The more AI reasons, the more compute each answer demands and the slower it runs.
Past a point, latency caps how much reasoning you can actually use.
That's why Andy Hock of @cerebras calls speed a form of intelligence.
Catch the full session: https://t.co/sSbAuC6pYl
Missed out on the action this year? SuperAI returns 8-9 Sep 2027.
Lock in your 2027 Super Early Bird tickets at $100 off, tickets at US$199 now: https://t.co/VaOYqlsjfU
The conversations shaping the future of AI. Every SuperAI 2026 session is now on YouTube.
Watch what you missed. Rewatch what stayed with you. Share the sessions worth sharing.
https://t.co/bDuUlvMnTs
The Genesis winner is @formas_ai!
Congratulations to all five finalists who shared a $2.3M prize pool powered by @msft4startups and @OpenAI.
This is just the beginning.
What makes an AI model "frontier" in 2026? And who actually captures the value?
Hemant Mohapatra (@MohapatraHemant) took on those questions at SuperAI Singapore (@superai_conf), on a panel with Geoff Soon (Mistral) and Cherie Shi (MiniMax), moderated by Zixuan Li (https://t.co/cCpmZf49uR).
Two arguments he made on stage that builders should take note of:
1. We're still in AI's extractive phase.
• Value sits with whoever extracts intelligence best. So today's frontier is benchmarks and tokens per dollar per watt.
• But the phase will shift to distribution. Then the frontier splits:
• Problems of scale (coding, reasoning) → won on cost and usability.
• Problems of scope (cancer, materials science, robotics) → fractal. They get harder the deeper you go. Winning there takes testing null hypotheses, RL, creating de novo knowledge.
(Hemant has written about this extractive vs. distributive framework in depth — link below.)
2. Open weights exist to commoditize the intelligence layer. That's a good thing.
• If a handful of companies own intelligence, the gains pool the way oil wealth does.
• Commoditize the base layer and value moves up to applications, where everyone can pay to play.
• The trap for open-source labs: positioning as SOTA model companies locks them into selling tokens at market price, without owning the infra to serve them.
• Owning a pond doesn't make you a water business. The money is in cleaning, piping, and delivering the water.
Thanks to the SuperAI team, as well as Alex Fiskum (@AlexFiskum), for hosting.
@MistralAI@MiniMax_AI@ZixuanLi_@Zai_org
The 100x company isn't about headcount.
It's about AI as the intelligence layer connecting every workflow, decision, and customer interaction.
Hyunjin Kim (@INSEAD), Haydn Sallmann (@googlecloud), @WilliamBryk (@ExaAILabs ), Ang Li (@SimularAI), and @bernardleong (Dorje AI) on what that operating model actually looks like.
Design. Product. Engineering. AI is collapsing the distance between all three.
@randyjhunt (@NotionHQ), Nathan Xu (@PLAUDAI), Jose Florido (@magnific) and Savannah from @ideo on what it means to build when the gap between idea and execution is almost gone.
Data center demand growing 3.5x by 2030. GPU lead times at 36–52 weeks. Grid connections taking 4–7 years.
Sachin Hindupur from @AMD on the infrastructure wall AI is about to hit and what it takes to produce more with the same or less.
36 hours of building. 5 teams standing.
The NEXT Hackathon finalists have been announced.
They demo live on the Weka Stage at 5:40pm today.
$200K in prizes powered by @awscloud , @Vercel, @ExaAILabs, @stripe, and @RazerAI .
Pre-AI: best UX wins. APIs are plumbing. Post-AI: agents run through APIs, not UI.
Timothy Wong from @airwallex on why financial infrastructure needs to be borderless, real-time, and intelligent, not just fast.