Code 10x faster starting today

Hi ,

OpenAI just released Codex-Spark, a lightweight version of its agentic coding tool.

Powered by Cerebras' Wafer Scale Engine 3, a megachip with 4 trillion transistors. Designed for faster inference and real-time collaboration.

First model in OpenAI's $10 billion partnership with Cerebras. Research preview for ChatGPT Pro users now.

This is OpenAI integrating dedicated hardware for speed, not just model improvements.

First here's today's VIP loyalty framework and Sam Altman's 2035 AI prediction. Then we'll look at what dedicated AI chips mean for performance.

🔥 Prompt of the Day 🔥

VIP Membership and Loyalty Builder: Use ChatGPT or Claude

Act as a Customer Retention and Loyalty Program Architect.

I want to move away from one-off visits and create predictable, recurring revenue through a membership or loyalty program that feels exclusive without costing much to deliver.

Essential Details:

• Business Type: [YOUR INDUSTRY]

• Current Customer Behavior: [ONE-OFF VS REPEAT]

• Average Transaction Value: [TYPICAL SPEND]

• Customer Frequency: [HOW OFTEN THEY VISIT]

• Current Loyalty Approach: [WHAT YOU DO NOW]

• System/Platform: [CARD SYSTEM OR TECH YOU USE]

Design one tiered loyalty program including:

• Tier Names (memorable, aspirational names that customers want to reach)

• Requirements to Reach Each Tier (total spent, visits, tickets earned, or other measurable actions)

• Specific Perks for Each Tier (early access, bonus credits, exclusive events, priority service - things that feel valuable but cost you little)

• Progression Visibility (how customers track their status and see what's next)

• Retention Mechanics (what keeps them engaged after reaching top tier)

• Communication Strategy (how you announce tiers, celebrate upgrades, remind them of benefits)

Create recurring revenue through loyalty that doesn't break your budget.

🔮 Future Friday 🔮

Sam Altman Predicts AGI by 2027, AI Transformation by 2035

Sam Altman just outlined his vision for the next decade.

AGI by 2027. Complete AI transformation by 2035. Intelligence becomes universally accessible. Economy fundamentally changes.

Here's what he's predicting and why it matters.

The Core Prediction

2027: Artificial General Intelligence (AGI) arrives. Machines perform tasks across diverse fields with human-like proficiency.

2035: AI transforms every industry. Healthcare, education, finance, environmental management all fundamentally different.

AI agents become virtual colleagues by 2035. Function autonomously as skilled professionals. Handle tasks equivalent to software engineers, doctors, financial analysts.

The Economic Vision

AI makes intelligence widely accessible. Cost of goods and services drops dramatically.

Altman envisions tenfold decrease in AI usage costs annually. Advanced technologies become affordable. Innovation accelerates across industries.

Scientific research compressed from decades into years. Solutions to climate change and disease eradication happen faster.

Universal Basic Compute

Altman proposes Universal Basic Compute (UBC) - like Universal Basic Income, but for AI.

Everyone gets access to AI computational resources. Empowers people to leverage AI for personal and societal advancement.

Goal: democratize AI benefits. Foster creativity and productivity at unprecedented scale.

The Workforce Impact

By 2035, AI agents function as virtual colleagues. Autonomous operation. Enhanced productivity and efficiency.

This promises increased operational effectiveness. It also raises job displacement concerns.

Necessitates workforce adaptation and reskilling strategies.

The Ethical Concerns

Altman emphasizes need for robust privacy protections and international regulatory frameworks.

Prevent potential misuse like mass surveillance by authoritarian regimes.

Ensure AI benefits are equitably distributed. Avoid exacerbating social inequalities.

Gradual Deployment

Altman advocates measured deployment of advanced AI models. Allow society to adapt to rapid technological changes.

Facilitates public acceptance. Establishes appropriate regulatory measures.

Compounding impact of AI drives super-exponential growth. Creates new markets. Reshapes existing ones.

Requires careful management for sustainable and inclusive progress.

Long-Term Societal Changes

By 2035, AI's influence extends beyond economics and technology to reshape societal values and norms.

As intelligence becomes accessible and affordable, traditional concepts of work, education, social structures may evolve.

Reimagined human experience where creativity and personal growth prioritized over routine tasks.

Why This Matters

These are official predictions from OpenAI's CEO. Not speculation. This is their roadmap.

AGI by 2027 is less than two years away. That timeline determines everything OpenAI builds and how fast they move.

If Altman's right, your industry fundamentally changes in the next decade. Your job changes. How value gets created changes.

If he's wrong, OpenAI's strategy is based on unrealistic timelines. Raises questions about their decision-making and capital allocation.

Either way, these predictions drive behavior. OpenAI races toward 2027 AGI deadline. Competitors try to keep pace. Governments respond to perceived urgency.

The predictions themselves create the pressure that shapes AI development speed and direction.

Did You Know?

The global AI market is projected to surpass a trillion dollars by the end of the decade, with enterprise spending on generative AI alone multiplying several times over in a single year making it one of the fastest-growing technology sectors ever recorded.

🗞️ Breaking AI News 🗞️

OpenAI Launches Codex-Spark Powered by Cerebras Chip

OpenAI announced Codex-Spark, a lightweight version of its agentic coding tool powered by dedicated hardware from Cerebras.

Research preview for ChatGPT Pro users now.

What Changed

OpenAI's Codex previously ran on standard infrastructure. Fast, but not optimized for real-time collaboration.

Codex-Spark integrates Cerebras' Wafer Scale Engine 3, a megachip with 4 trillion transistors specifically designed for AI inference speed.

This is the first model resulting from OpenAI's $10 billion, multi-year partnership with Cerebras announced last month.

How It Works

Codex-Spark is designed for swift, real-time collaboration and rapid iteration.

Handles rapid prototyping and daily productivity tasks. Not the longer, heavier tasks that GPT-5.3-Codex handles.

"Lowest possible latency" is the priority. Cerebras' chips excel at workflows demanding extremely low latency.

OpenAI describes two complementary modes: real-time collaboration when you want rapid iteration, and long-running tasks when you need deeper reasoning and execution.

The Partnership

OpenAI and Cerebras announced multi-year agreement worth over $10 billion last month.

"Integrating Cerebras into our mix of compute solutions is all about making our AI respond much faster," OpenAI said at the time.

Codex-Spark is the "first milestone" in that relationship.

Cerebras Context

Cerebras has been around for over a decade. In the AI era, it's gained prominence.

Last week: Raised $1 billion at $23 billion valuation. Previously announced IPO intentions.

Wafer Scale Engine 3 is Cerebras' third-generation megachip. 4 trillion transistors designed specifically for AI inference.

Sam Altman's Hint

CEO Sam Altman tweeted before announcement: "We have a special thing launching to Codex users on the Pro plan later today. It sparks joy for me."

The "spark" was Codex-Spark.

Why This Matters

This marks a shift from pure software optimization to hardware-software integration.

Previous AI improvements came from better models, better training, better algorithms. Same underlying compute infrastructure.

Now OpenAI is integrating purpose-built hardware. Chips designed specifically for AI inference speed, not general computing.

If Codex-Spark performs significantly better than standard Codex, expect other AI companies to pursue similar hardware partnerships.

Cerebras becomes a critical strategic partner, not just another compute provider. $10 billion commitment reflects how important dedicated hardware is becoming.

What This Means For Developers

Real-time AI collaboration becomes faster. Rapid prototyping gets even more rapid.

Latency-sensitive applications become more viable. AI that needs to respond instantly gets the infrastructure to support it.

Two-mode workflow: Use Spark for quick iteration, use standard Codex for deep reasoning. Choose based on task requirements

Over to You...

Does having two modes (Spark for speed, Codex for depth) complicate things or make sense?

Let me know if you'd use both or pick one.

To workflow complexity,

Jeff J. Hunter 
Founder, AI Persona Method | TheTip.ai

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