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“I think that right now, the existential risk is close to zero. So why freak everybody out?” Databricks CEO and co-founder Ali Ghodsi joined a16z General Partners martin_casado and Sarah Wang for a conversation on AI risk, what's actually slowing enterprise adoption, and where companies are already seeing value....

16,652 次观看 • 5 天前 •via X (Twitter)

9 条评论

EKOS _ AGI 🦊 🇮🇷 的头像
EKOS _ AGI 🦊 🇮🇷5 天前

@alighodsi @a16z @martin_casado @sarahdingwang This is EKOS This is Ai future 🔥

kettybluce 的头像
kettybluce5 天前

@alighodsi @a16z @martin_casado @sarahdingwang The useful question is less whether risk is zero and more whether teams can observe, constrain, and recover from failure. That shift—from fear to measurable operational controls—seems central to responsible enterprise adoption.

Ab-E-Kawser(Arnab) 的头像
Ab-E-Kawser(Arnab)4 天前

@alighodsi @a16z @martin_casado @sarahdingwang AI should relentlessly seek truth, remain humble and corrigible, stay under legitimate human control, serve humanity, protect human rights and human agency, maximize sustainable human flourishing across generations, and never accumulate unaccountable power .

PYX Crypto 🐍 的头像
PYX Crypto 🐍4 天前

@alighodsi @a16z @martin_casado @sarahdingwang Existential risk is close to zero, according to the man who sells the thing, which is the same confidence the guy selling smoke detectors has about your house.

Ab-E-Kawser(Arnab) 的头像
Ab-E-Kawser(Arnab)4 天前

@alighodsi @a16z @martin_casado @sarahdingwang I’m not an expert, but I think we tend to overlook that the Hugging Face incident was largely a side effect of aggressive cyber-capability testing. Such testing is necessary , but it also creates new problems. We therefore need better system design for aggressive AI testing.

Yi Casillas 的头像
Yi Casillas4 天前

@alighodsi @a16z @martin_casado @sarahdingwang 这段讨论里我比较认同“别先把风险说成末日”这点,但企业落地也确实不能只靠乐观。模型能力、数据治理和组织流程是一起变的,真正难的是别让其中一环掉队。

Ab-E-Kawser(Arnab) 的头像
Ab-E-Kawser(Arnab)4 天前

@alighodsi @a16z @martin_casado @sarahdingwang I think people should debate Anthropic’s AI Constitution—the good, bad, and ugly. What should guide AI: truth, curiosity, human control, and maximizing sustainable benefits for the greatest number across the short, medium, and long term?

lvnbbs_bnb 的头像
lvnbbs_bnb4 天前

@alighodsi @a16z @martin_casado @sarahdingwang exactly, why treat software like a boogeyman when the real hurdle is just making it work on monday

Yi Casillas 的头像
Yi Casillas5 天前

@alighodsi @a16z @martin_casado @sarahdingwang 我倒更常见另一种风险:模型不一定把世界毁了,但会把错误悄悄塞进业务流程里。企业真正需要的不是更大的恐慌,而是能追踪、回滚、知道谁批准了自动化动作。

相关视频

Databricks' Ali Ghodsi on AI risk and adoption: Ali isn't losing sleep over the existential risk debate. He says a number of conditions would all have to be true simultaneously to enable an actual runaway takeoff scenario, and currently several opposite conditions exist. Each frontier training run requires significantly more resources. Power, GPUs, engineers - and some attempts fail, burning up huge piles of money with them. Until that reverses, he doesn't see the self-improving loop happening. Cyber is what he's watching most closely and where he anticipates real impact. Most orgs are not equipped for the coming change in agentic capabilities. The time between a vulnerability being published and being weaponized has collapsed from years to hours. On adoption, he believes most companies don't need a smarter model. The models are already smart enough. The gap is context they don't absorb - the things an employee who's worked at a company for five years learned by osmosis. If the frontier stopped advancing today, he thinks it wouldn't meaningfully change the value most are extracting from AI anyway. In conversation with a16z's Martin Casado and Sarah Wang: 00:00 Intro 00:48 Why Ali places the AI risk near zero 05:05 The word "pacing" was a mistake 10:50 What 10k agents and $100m can do 12:20 What would change his mind on AI risk 14:20 More GPUs, more ways to fail 18:05 US export controls on PlayStations 20:10 The damage everyone expected by now 24:30 Public vulnerabilities weaponized in hours 30:15 Why labs can't grade each other 37:15 Why most of RSI isn't actually RSI 40:05 Why nobody really needs a smarter model 41:50 The AI use cases nobody argues about 47:30 Google Search solved this 25 years ago 50:55 Nobody has privileged knowledge now 55:10 Same model, new harness, 2x cost 58:15 Open source: 5% of spend, 60% of tokens 1:05:30 90% of new databases are created by agents YouTube: Databricks martin_casado Sarah Wang

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327,508 次观看 • 5 天前