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These startups are chasing the next big thing in LLMs

Technology
Global
Gestartet August 11, 2026

MIT Technology Review’s What’s Next series looks across industries, trends, and technologies to give you a first look at the future. You can read the rest of them here. Way back in the summer of 2017, AI researchers at Google put out a paper called “Attention Is All You Need,” in which they described a new…

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CLAIM Veröffentlicht von admin • Aug 11, 2026
Whether startups build foundational models or applications, they must demonstrate measurable business value within 18 months or risk running out of capital.

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CLAIM Veröffentlicht von admin • Aug 11, 2026
The winners among LLM startups will be those solving inference efficiency and deployment, not those racing to match frontier model capabilities.

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CLAIM Veröffentlicht von admin • Aug 11, 2026
Open-source LLM projects from startups will prove more durable and trustworthy than proprietary closed models as deployment standards mature.

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CLAIM Veröffentlicht von admin • Aug 11, 2026
Most venture-backed LLM startups will fail because they cannot compete with the compute resources and data advantages of established tech giants.

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CLAIM Veröffentlicht von admin • Aug 11, 2026
Startups chasing advancements in LLMs should be regulated to prevent misuse of technology.

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CLAIM Veröffentlicht von admin • Aug 11, 2026
Government regulation of LLM capabilities and safety will emerge faster than startup innovation, forcing many ventures to pivot or shut down.

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CLAIM Veröffentlicht von admin • Aug 11, 2026
Startups should prioritize ethical considerations when developing large language models.

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CLAIM Veröffentlicht von admin • Aug 11, 2026
The rapid development of LLMs by startups will lead to significant job displacement across various industries.

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CLAIM Veröffentlicht von admin • Aug 11, 2026
Startups should not solely focus on LLMs but diversify their AI offerings to reduce risk.

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CLAIM Veröffentlicht von admin • Aug 11, 2026
Funding for LLM-focused startups should be contingent on transparent AI practices.

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