En 30 segundos
Desai argues that AI software vendors need to show concrete use cases and a clear path to value.
- 1ModelsDesai expects models to remain important.
- 2DataHe identifies a continuing need for data storage.
- 3ValueHe says vendors must demonstrate value quickly.
Conclusiones
- Models and data remain important.
- Other layers will evolve.
- Customers need a clear reason to switch.
Lee el texto editado por capítulos
The software stack
CJ Desai discusses which parts of the software stack may remain important as AI applications evolve.
CJ Desai discusses which parts of the software stack may remain important as AI applications evolve.
He expects language models to remain part of many AI applications and identifies data storage as another continuing need.
A case for switching
Desai argues that the surrounding software will evolve and that vendors must show customers clear value.
Desai argues that the surrounding software will evolve and that vendors must show customers clear value.
He says a case for switching from older software should explain how quickly the new product delivers value and which formerly difficult use cases it enables.
Más de este canal
38:06EN
No Priors: AI, Machine Learning, Tech, & Startups· 29 oct 2026Pódcast · 3 vocesServal’s Jake Stauch on coding agents for enterprise ITInvitado: Jake Stauch · Presenta: Sarah Guo, Elad GilStauch explains why Serval is challenging ServiceNow, how it approaches permissions and approvals, and why background agents remain difficult to deploy safely.
1:07ENNo Priors: AI, Machine Learning, Tech, & Startups· 27 oct 2026PódcastGeorge Kurtz on How AI May Change CyberattacksInvitado: George KurtzThe CrowdStrike CEO describes his assessment of evolving threats.
39:23ENNo Priors: AI, Machine Learning, Tech, & Startups· 15 oct 2026Pódcast · 2 vocesThe Defenders' Window: Greg Brockman on AI-Generated Knowledge, Drug Discovery, and the New Physics of CybersecurityPresenta: Sarah Guo, Elad GilOpenAI's president explains why solving open math problems is a proof point for science, how proactive agents change product form, and why security spending must exceed software spending.
1:04ENNo Priors: AI, Machine Learning, Tech, & Startups· 14 oct 2026PódcastWalter Goodwin on the Slow Steps in Chip DesignInvitado: Walter GoodwinA chip founder explains why automation does not remove every computational bottleneck.
1:09:44ENNo Priors: AI, Machine Learning, Tech, & Startups· 9 oct 2026Pódcast · 2 voces10,000 GB300s for Four Weeks: Reflection’s Open-Model LedgerPresenta: Sarah Guo, Elad GilMisha Laskin tells No Priors how Reflection trained Beam, why reinforcement learning outspent pre-training, and why the West needs its own open weights.
0:39ENNo Priors: AI, Machine Learning, Tech, & Startups· 8 oct 2026PódcastMichael Lee on Rethinking Organizations with AIInvitado: Michael LeeA business leader argues that the opportunity goes beyond faster tasks.