AI thread

[Dr Strangelove]

Jodie Foster says Apple’s “F1” seemed like it “was made by AI” and written as if a computer was following “the structure that you would learn in school”:

“I don’t say this disparagingly — how could I? This movie went on to make millions of dollars. But I look at a movie like ‘F1’ and I’m like, ‘F1’ was made by AI. Wasn’t it? I mean, the structure was exactly the structure that you would learn in school. The actors say the lines exactly the way it would be written if a computer was writing exactly what would be the right thing for that time. And they were able to dominate the technology to make something big and beautiful and potentially where a lot of the information comes from other places.”

[Dr Strangelove]

Foundational Roots: Canada was the birthplace of modern deep learning and neural networks, led by researchers Geoffrey Hinton, Yoshua Bengio, and Richard Sutton (0:50 - 2:38).
The "Resource Extraction" Model: The video argues that Canada followed a pattern similar to its mining sector: it funded the foundational research, but the intellectual property (IP) and talent were frequently captured by American tech giants like Google, Microsoft, and Meta (2:52 - 7:22).
Adoption Crisis: Despite launching the world’s first national AI strategy in 2017, Canada currently has one of the lowest rates of AI adoption in the G7 (4:36 - 5:00, 9:58 - 10:14).
The $2.3 Billion Reset: The government has introduced a new $2.3 billion strategy focusing on AI adoption rather than just research. Its three main pillars are (11:25 - 13:42):
Healthcare Deployment: Using shared data platforms (like Vital) to improve diagnostics.
Sovereign Compute: Reducing reliance on foreign infrastructure by investing in domestic capacity, notably through the company Cohere.
Trust & Literacy: Training students and educators to integrate AI into their workflows.
Looking Ahead:

The video questions whether this new approach will finally break the cycle of foreign IP capture. While the recent merger of Cohere with Germany's Aleph Alpha suggests a potential shift toward a globally competitive, Canadian-anchored entity, the long-term impact remains to be seen (16:22 - 17:35).

[Dr Strangelove]

The Case for Apple Losing: The critics argue that Apple significantly lagged behind competitors like OpenAI, Google, and Meta (0:59-1:25). They note that Apple was slow to adopt the term "AI" and that Apple Intelligence has been perceived as underwhelming compared to existing generative AI tools (2:33-2:35).
The Case for Apple Winning: Proponents argue that the 'AI race' is too narrowly defined by software capabilities. They emphasize that Apple excels at hardware (0:47-0:57) and believes that the future of AI lies in on-device processing for security and performance (3:45-4:07). From this view, Apple is positioning itself to be the provider of the hardware that will eventually run these local models (4:12-4:25).
Two Different Races: The participants eventually conclude that there are two distinct competitions:
A software race where competitors are currently leading (4:48-5:02).
A long-term hardware race where Apple leverages its massive installed base of iPhone users to maintain dominance (5:10-5:24).
Current Context:
The video concludes by questioning whether an AI-focused company will eventually create hardware compelling enough to make users switch away from the iPhone, or if Apple can improve its AI offerings enough to retain its users (6:30-6:50).


Being last in with unstable tech is not a bad thing. Canada needs to make phones again.