What You'll Learn

1. How to design and implement a secure IoT architecture.
2. The role of encryption, authentication, and access control in IoT systems.
3. How to implement secure communication between devices and cloud services.
4. Real‑world strategies for firmware updates, monitoring, and intrusion detection.
5. How to avoid common IoT security pitfalls and build scalable, maintainable systems.

What You'll Learn

• The core principles behind major programming paradigms.
• How procedural, object-oriented, and functional programming differ in structure and philosophy.
• When to use each paradigm — and when to avoid it.
• How paradigms impact performance, scalability, and testing.
• Real-world case studies showing how major tech companies apply these paradigms.

What You’ll Learn

• The core principles and history of SRE.
• How to define and measure Service Level Indicators (SLIs) and Service Level Objectives (SLOs).
• How to use error budgets to balance reliability with innovation.
• How to set up monitoring, alerting, and incident response workflows.
• How to automate operations with code and reduce toil.
• How to build a culture that supports continuous reliability.

What You'll Learn

• The foundational building blocks of AI and how they interconnect.
• The difference between AI, Machine Learning, and Deep Learning.
• Key algorithms and architectures used in modern AI systems.
• How to train and evaluate a simple AI model in Python.
• When to use AI—and when it’s not the right tool.
• Common pitfalls, scalability, and security considerations.
• How major companies apply AI in production.

Why $700 Billion? The Forces Driving the Spend

The spending surge is driven by a single reality: demand for AI compute is outstripping supply across every major cloud provider. Inference workloads — running trained AI models to serve predictions, generate text, and produce images — now account for an estimated 60 to 70 percent of total AI compute demand across major hyperscalers, up from roughly 40 percent in 2024.2 As enterprises adopt AI agents, copilots, and multimodal applications, the compute requirements are scaling faster than anyone anticipated.

Every major cloud provider reported in their most recent earnings calls that they are "capacity-constrained" — they have more customer demand than they can serve. This is the core justification for the spending: it is not speculative building, but building to meet contractual backlogs worth hundreds of billions of dollars.

The Timeline: Six Months from Launch to Shutdown

OpenAI first previewed Sora as a research project in February 2024, generating enormous excitement with its ability to produce photorealistic video from text prompts. The first public version launched for ChatGPT Plus and Pro users in the United States and Canada in December 2024.1

Sora 2, a major upgrade with an iOS app and standalone consumer experience, launched on September 30, 2025.2 An Android app followed two months later. By November 2025, downloads had peaked — and then began a steep decline.

On March 24, 2026, OpenAI announced the shutdown.3 The web and app version will go dark on April 26, 2026, with the API following on September 24, 2026. Users have been urged to download their content before the cutoff dates.

The entire consumer lifecycle of Sora 2 lasted roughly six months.

What Is Google Lyria 3 Pro?

Google Lyria 3 Pro is an AI music generation model developed by Google DeepMind. It is the successor to Lyria 3, which launched just one month earlier in February 2026 with a 30-second generation limit. The "Pro" upgrade extends that cap to three full minutes and introduces structure-aware composition — the model understands musical elements like intros, verses, choruses, and bridges, and users can specify these in their prompts.1

The model sits within Google's broader generative AI ecosystem. Rather than offering it as a standalone product, Google has distributed Lyria 3 Pro across six different surfaces: the Gemini app for consumers, Google Vids for video editors, ProducerAI for music producers, and Vertex AI, the Gemini API, and AI Studio for developers and enterprises.12

This multi-platform distribution strategy is significant. While Suno offers enterprise API access through its Enterprise tier, Google's deep integration across consumer apps, developer tools, and enterprise infrastructure gives it a structural advantage in the B2B segment of AI music generation. The Vertex AI integration is powered by Google's custom TPU infrastructure — for context on how Google and other tech giants are building custom silicon to run models like Lyria 3 Pro, see our breakdown of the custom AI chip race in 2026.

What You'll Learn

1. What Apple's Gemini deal actually includes — the technical scope, financial terms, and privacy architecture.
2. How model distillation works and why it matters for on-device AI.
3. What the redesigned Siri will be capable of, based on confirmed reports and leaks.
4. When these changes ship and what developers should watch for.
5. Why this partnership reshapes the competitive dynamics of the AI assistant market.

What the New Siri Will Actually Do

The redesigned Siri, developed under the internal codename "Project Campos," represents Apple's most ambitious assistant overhaul since Siri's original 2011 launch.6

Based on confirmed reports and Apple's own statements, the new Siri will include:

• On-screen awareness: Siri will be able to see and understand what is currently displayed on your screen — a flight confirmation in an email, a photo in Messages, a product on a webpage — and take contextual actions based on that content.7
• Cross-app reasoning: Rather than being limited to single-app commands, Siri will chain actions across multiple apps. For example, extracting a date from an email, creating a calendar event, and sending a confirmation message — all from a single request.7
• Conversational memory: Siri will remember previous conversations and use that context to provide more personalized, coherent responses over time.2
• Proactive suggestions: The assistant will anticipate needs — suggesting you leave early based on traffic data before an airport pickup, or surfacing relevant documents before a meeting.2
• Ask Siri toggle: A new UI element appearing across the app ecosystem where users can highlight content and ask Siri for contextual actions — summarizing, translating, searching, or acting on the selected content.7

Apple is also developing a standalone chatbot version of Siri that will compete directly with ChatGPT, Gemini, and Claude. This mode will handle extended conversations, document analysis, creative writing, emotional support queries, and complex multi-step tasks like booking travel.67

A preview of the chatbot Siri could be shown at WWDC 2026 on June 8.7

Apple has delayed the "more personalized Siri" multiple times over the past year, leading to declining consumer perception of Apple Intelligence.11 The Gemini partnership is widely seen as Apple's strategy to close the gap rapidly rather than building frontier-scale models entirely in-house.

Confirmed CapabilitiesThe Chatbot ModeTimeline and What to Watch ForMilestoneExpected DateStatusApple-Google partnership announcedJanuary 12, 2026Confirmed1Gemini distillation capability confirmedMarch 25, 2026Confirmed3WWDC 2026 keynote (Project Campos reveal)June 8, 2026Announced7iOS 27 beta with new SiriSummer 2026ExpectediOS 27 public releaseFall 2026Expected

What You'll Learn

1. Why the largest tech companies are investing billions in custom AI chips instead of relying solely on Nvidia.
2. What each major chip offers — with verified specs and real deployment data.
3. How these chips compare on memory, compute, energy efficiency, and scale.
4. What this means for developers, cloud costs, and the AI ecosystem.
5. Where Nvidia stands — and whether its dominance is genuinely threatened.

What You'll Learn

• How AI customer service bots actually work — behind the scenes.
• The pricing models and cost structures of leading platforms.
• Real-world case studies from Sephora, HDFC Bank, and Intercom.
• When to deploy bots vs. human agents.
• Step-by-step guide to building your own AI assistant using the OpenAI Assistants API.
• Common pitfalls, troubleshooting, and monitoring best practices.