Here's what caught my eye in the world of AI this week:

1. Setting AI Goals That Drive Extraordinary Outcomes

Here's a powerful insight about goal-setting that's reshaping how industry leaders approach AI strategy: If your teams are hitting 100% of their targets, you're likely positioning your organization for mediocrity rather than excellence.

The Amazon Success Formula:

Consider Amazon's S-Team approach: They deliberately set stretch targets with an expected 75% achievement rate. This isn't failure – it's strategic brilliance. When you hit every goal, you've simply confirmed you're not pushing hard enough.

Why This Matters Now:

1. The Speed Factor

The AI landscape moves at unprecedented speed. While cautious players perfect their conservative plans, market leaders are redefining entire industries. By the time you achieve that "safe" objective, the market has likely created three new opportunities you've missed.

2. The Innovation Catalyst

When teams face ambitious goals, something remarkable happens: Innovation becomes necessity rather than choice. As Bezos noted, limiting yourself to guaranteed wins means missing the breakthrough opportunities that define market leaders.

Compare These Approaches:

Feature Factory Goal: Deploy an automated ticketing system by Q2.

Moonshot Goal: Reduce customer support costs by 30% while boosting satisfaction scores by 25% through intelligent automation.

The first delivers a nebulous feature. The second creates capabilities that transform your competitive position.

The ROI of Ambitious Targets:

Even when teams fall short of moonshot goals, they:

• Develop next-generation capabilities

• Uncover unexpected opportunities

• Build innovation muscle that drives future success

Think about it: Would you rather achieve 100% of an incremental goal or 80% of a transformational one?

Strategic Takeaway:

In the AI era, playing it safe is the riskiest strategy. Set goals that make your teams stretch, innovate, and occasionally miss. The capabilities you build along the way will position you for market leadership.

Action Point:

Review your AI objectives today. If they don't make you slightly uncomfortable, they're probably not ambitious enough to drive real competitive advantage.

Remember: In AI strategy, the gap between good and great isn't in execution – it's in the ambition of your goals.

Quote of the week

2. The personalization of education with AI provides us a LEAP forward in activating children’s desire to learn.

3. Anthropic Updates

Executive Summary:

Anthropic has unveiled two exciting advancements: the upgraded Claude 3.5 Sonnet and the all-new Claude 3.5 Haiku. Claude 3.5 Sonnet raises the bar in AI-driven coding, delivering industry-leading performance without sacrificing speed or cost. Meanwhile, Claude 3.5 Haiku combines state-of-the-art capabilities with affordability, surpassing previous models. Additionally, a groundbreaking feature in public beta, "computer use," allows Claude to interact with software interfaces like a human, setting a new frontier in AI automation. Developers can now leverage these innovations to push the boundaries of what AI can achieve.

Key Points:

1. Claude 3.5 Sonnet: Next-Level Coding and Tool Use

• The upgraded Claude 3.5 Sonnet shows remarkable gains in coding, particularly in agentic coding tasks, scoring 49.0% on SWE-bench Verified, outpacing all publicly available models.

• Customers like GitLab and The Browser Company have reported significant improvements in multi-step development and web-based automation, making Claude 3.5 Sonnet a top choice for complex software engineering tasks.

2. Claude 3.5 Haiku: Power, Speed, and Affordability

• Claude 3.5 Haiku delivers the performance of the previous generation’s largest model, Claude 3 Opus, but at a lower cost and with similar speed. It excels in coding, achieving 40.6% on SWE-bench Verified.

• Designed for user-facing applications and data-intensive tasks, Claude 3.5 Haiku will be available later this month, initially as a text-only model with plans for image input to follow.

3. Introducing 'Computer Use' in Public Beta: A New AI Frontier

• Claude 3.5 Sonnet’s "computer use" allows it to navigate and interact with software interfaces, mimicking how humans operate a computer—scrolling, clicking, typing, and more.

• This feature is currently experimental, but early adopters like Replit are using it to automate complex tasks. Developers can access this on the Anthropic API, with safety measures in place to ensure responsible use.

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4. AI First Teams

I'm a big fan of Peter Yang’s writing—if you haven’t checked out his Substack, you’re missing out. I’ve been diving into NotebookLM lately, and his post on the small, scrappy team that built it is a perfect example of what I believe: it’s going to be small, empowered teams, whether at startups or Fortune 500 giants, that drive real change. AI doesn’t wait for bureaucracy to catch up. The old guard better reinvent itself fast, or risk being left in the dust.

Executive Summary:

The success of NotebookLM, an AI-powered tool from Google Labs, demonstrates how small, agile teams can deliver groundbreaking results within large tech companies. Starting as a simple experiment to turn text into lifelike audio conversations, NotebookLM evolved into a viral hit, changing how users study and engage with information. By prioritizing user needs, fostering close-knit collaboration, and leveraging AI effectively, the tiny NotebookLM team achieved outsized impact. Below are six key lessons from their journey, along with tips on how you can maximize your own use of NotebookLM.

Six Lessons from NotebookLM’s Success:

1. Build with Users, Not for Them

Engaging directly with users on platforms like Discord helped the team gather critical feedback, leading to quick iterations. Understanding user needs deeply was crucial to refining features, such as adding in-line citations.

2. Don’t Build AI for the Sake of AI

The team focused on bridging cutting-edge AI with practical, human needs. They kept the user experience simple and intuitive, which played a key role in the success of features like Audio Overview.

3. Meetings Should Be About Building, Not Talking

The NotebookLM team prioritized action-oriented meetings where product managers, designers, and engineers collaborated directly to solve problems. This approach sped up development and ensured clear outcomes from each session.

4. Invest in Relationships Within the Team

Strong, ego-free relationships allowed team members to share ideas openly and iterate quickly. Regular interactions, even casual ones over lunch, helped foster an environment of trust and collaboration.

5. Use AI to Assist in Building the Product

The team utilized their own AI to streamline development, from training new users to finding innovative ways to connect people with the product’s core value. This helped them refine features and enhance user engagement.

6. Share Information Strategically

Rather than drowning in documentation, the team kept communication clear and strategic. By sharing updates in concentric circles—starting with core team members and expanding to stakeholders—they ensured alignment without unnecessary bureaucracy.

NotebookLM’s journey is a powerful example of how small, empowered teams can achieve breakthrough success by staying focused, user-centric, and agile.

Author: Peter Yang

5.  AI Safety…Ok Doomer: with Anca Dragan

Love the below analogy!

I created the below Audio Podcast and Briefing Doc for the YouTube podcast using NotebookLM:

Audio Podcast of the Podcast which helps to shorten the learning time in an engaging manner. Cliff notes for when you are driving to work!

Briefing Doc:

Google DeepMind’s lead for AI safety and alignment, Anca Dragan, discusses the challenges of ensuring that artificial general intelligence (AGI) is aligned with human values.

Dragan emphasizes the importance of designing safety into AI systems from the beginning, drawing analogies to bridge construction and robotics. She highlights the need for AI to engage in back-and-forth conversations with humans, anticipating their responses and learning from their feedback.

Dragan also explores the issue of conflicting human values and the need for AI to consider the broader societal impacts of its actions. The conversation touches upon the Frontier Safety Framework, a set of guidelines for proactively identifying potential harms from AI, and the need for scalable oversight mechanisms to ensure that AI systems remain aligned with human intentions, even as they become more capable. Finally, Dragan discusses the potential risks of AGI, including the possibility that it might develop its own goals that could be detrimental to humanity, emphasizing the importance of continuing to address these concerns.

Killing The Golden Goose

These measures may also slow down the rate of innovation and reduce the broad accessibility of capabilities. Striking the optimal balance between mitigating risks and fostering access and innovation is paramount to the responsible development of AI. This is very similar to what I previously mentioned in my last newsletter - The Great AI Reset: Power Players Speak Out - 10-18-24. The real danger? Losing our lead in a race we can't afford to lose. We need smart, balanced regulation that fuels, not fumbles, our AI future.

6. Friday Funnies 🤣 . Perhaps not so funny, but true. Prediction - education will be fundamentally disrupted over the next 10 years. I am really excited to play a small role in making that happen!

7. Memorandum: Advancing the United States’ Leadership in Artificial Intelligence

On Wednesday, while presenting to Master’s candidates at the Navy Postgraduate School, I was asked when I thought the government would start removing restrictions on AI use by federal employees. My response was clear: advancing AI governance isn’t just a smart financial move—it's critical to national security. AI can dramatically cut costs, for instance, by streamlining aircraft production, but more importantly, it ensures our strategic edge in defense. The need for thoughtful, rapid progress in this area couldn't be clearer. And then, almost as if on cue, the very next day, a groundbreaking memorandum was released. Below, I’ve summarized the “Framework to Advance AI Governance and Risk Management in National Security” to help you quickly get up to speed.

Executive Summary:

The "Framework to Advance AI Governance and Risk Management in National Security" establishes comprehensive guidelines for responsible AI use within U.S. national security contexts. It emphasizes balancing innovation with ethical considerations, ensuring AI aligns with democratic values, human rights, and legal standards. By implementing robust oversight, risk management, and transparency measures, the framework seeks to build public trust and maintain effective AI systems that enhance national security without compromising core values.

Key Points:

Clear AI Use Guidelines and Restrictions

• The framework defines prohibited and high-impact AI use cases, particularly those that may violate legal or ethical norms. These include restrictions on profiling, unlawful surveillance, and removing human oversight in critical areas like nuclear command.

• Agencies are tasked with cataloging these uses, ensuring compliance with national and international laws to prevent misuse and maintain integrity.

Robust Risk Management Practices

• Establishes minimum risk management standards for high-impact AI, such as mandatory risk assessments, effective human oversight, and continuous monitoring. This ensures AI systems are safe, reliable, and aligned with mission-critical needs.

• Encourages periodic evaluations and adaptations to emerging risks, with the goal of preempting issues and maintaining operational readiness without compromising security.

Training, Oversight, and Accountability

• The framework mandates the appointment of Chief AI Officers and creation of AI Governance Boards to oversee AI implementation, risk management, and compliance across agencies.

• Training programs are required to equip personnel with the skills needed to manage AI responsibly, alongside mechanisms to report misuse and hold individuals accountable, ensuring transparency and accountability throughout the AI lifecycle.

This framework lays the foundation for the ethical and secure use of AI within national security, promoting innovation while safeguarding civil liberties.

Read the NSM here.

Read the Frame work here.

8. Customization Debt

Customization debt is a critical issue that resonates deeply within the AI space, and it’s one that too many companies overlook. In the rush to secure big deals, businesses often make bespoke changes that seem harmless but eventually snowball into major technical headaches. For AI, this can mean custom algorithms, integrations, or features that deviate from the core model, leading to costly maintenance and stifled innovation. Just like any other software, AI thrives on scalability and standardization—endless customization erodes that foundation, leaving teams to constantly play catch-up. To truly unlock AI’s potential, companies need to recognize these hidden costs, streamline their offerings, and find a strategic balance between customization and sustainable development.

Executive Summary:

"Customization debt" is a hidden issue affecting many B2B/enterprise software companies. It arises when businesses make bespoke changes to their products to secure big deals, trading short-term revenue gains for long-term technical complications. While these customizations may seem minor, they accumulate over time, burdening development teams and undermining product scalability and innovation. Addressing this issue requires recognizing the hidden costs and making strategic decisions to limit or manage customization demands more effectively.

Key Points:

Customization Debt Explained

• Customization debt refers to the ongoing costs and technical challenges that stem from making bespoke modifications for individual clients to secure deals.

• These "specials" often include unique features, integrations, or configurations that deviate from the standard product, leading to maintenance issues down the line.

  Though presented as one-off projects, they pile up, creating unseen technical debt that drains resources and disrupts development.

The Hidden Costs of Specials

• Customizations may bring in immediate revenue, but they come with hidden long-term costs, such as increased maintenance, bug fixes, and support demands.

• Engineering teams often scramble to address unexpected issues with custom solutions, diverting focus from core product development.

• Over time, this pattern leads to slower innovation, lower morale among developers, and an overall reduction in the company’s ability to scale and compete.

Strategies to Tackle Customization Debt

• Companies need to acknowledge and track customization efforts, setting aside resources for ongoing support or creating dedicated "specials" teams.

• Implementing a recurring fee for bespoke features can fund long-term maintenance, while offloading maintenance to customers or third parties can alleviate internal burdens.

• Another approach is to standardize and limit customization options, providing configurable, modular features that meet client needs without extensive bespoke coding.

In short, addressing customization debt is about finding a balance between immediate revenue opportunities and sustainable, scalable product development.

Author: Richard Mironov

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Until next time, take it one bit at a time!

Rob

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