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.

[Dr Strangelove]

[al_keda]

[Dr Strangelove]

The Impact of AI Policies: The creator expresses concern over how YouTube’s automated tools—intended to flag "AI slop"—are unfairly penalizing legitimate, high-effort channels that rely on voice-overs and animation (0:47-1:10).
The "Proof of Life" Problem: To combat performance drops and AI-related skepticism, many creators, including himself, feel pressured to appear on camera to "prove" they are human, despite personal concerns about fame and mental health (2:49-3:13, 4:48-5:33).
Financial Struggles: The channel has experienced a significant decline in revenue—dropping from $30k–$40k to under $10k a month—which threatens the sustainability of expensive, high-quality animations that cost $10k–$20k per video (6:35-7:18).
Content Integrity: The creator argues that while AI is pervasive across the platform, the complex, original research and custom animations his channel produces cannot be replicated by AI, yet they are struggling to compete for visibility on a homepage increasingly dominated by AI-generated content and Shorts (9:01-11:13).
Call for Support: Due to these shifts, the creator has reluctantly decided to promote his Patreon as a necessary measure to keep the channel afloat (8:00-8:50).