💰 VCs Just Predicted Your Job Gets Replaced This Year.

Hey there,

While you were nursing your New Year's hangover, China's DeepSeek published a research paper that has AI analysts calling it a "striking breakthrough." Meanwhile, investors are predicting 2026 will be the year AI either proves its worth or bankrupts companies trying. And Apple? They're sitting on $130 billion in cash while everyone else burns money on data centers.

Here's what happened:

• DeepSeek's training breakthrough – New method trains bigger models without them breaking
• VCs predict AI labor shift – Companies will replace workers with agents in 2026
• Apple's AI gamble – Cautious approach may pay off as bubble concerns grow

Let's break it down.

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What it does:

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• Built-in library of email templates
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Why it's different:

Instead of generic "I hope this email finds you well" nonsense, MailMaestro studies your past emails and mimics your actual writing patterns.

You train it once on:

• Words to avoid
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• Common scenarios you face

Then it writes emails that sound like you, not like AI.

Real-world example:

Without MailMaestro:
"I hope this email finds you well. I wanted to reach out regarding the Q1 budget proposal. Would you be available for a quick sync to discuss next steps? Looking forward to hearing from you!"

With MailMaestro (trained on your style):
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Why people use it:

• Saves hours on email without sounding robotic
• Maintains your personal brand/voice
• Faster than training ChatGPT every time
• Pre-built templates for common scenarios

The catch:

• Requires some setup time to train properly
• $15/month isn't cheap for an email tool
• Free plan limited to 3 requests per week

Who it's for: Anyone drowning in email who wants AI help but hates how generic AI emails sound. Sales teams, executives, customer support, consultants.

Pricing:

Free: 3 requests per week
Pro: $15/seat/month + 14-day trial

Tools:MailMaestro

🇨🇳 1. China's DeepSeek Drops AI Training Breakthrough

New method could reshape how the entire industry builds models

While U.S. companies throw billions at bigger data centers, China's DeepSeek just published a research paper showing there's a smarter way to train AI models.

The paper introduces "Manifold-Constrained Hyper-Connections" (mHC) — a training method designed to scale models without them becoming unstable or crashing mid-training.

Why this matters:

Training large AI models is brutally expensive and wasteful. Models often fail mid-training, wiping out weeks of work and thousands of GPU hours.

When that happens, companies restart from scratch. More electricity wasted. More compute hours burned. More money gone.

DeepSeek’s approach makes training more stable and predictable, reducing failures and wasted resources.

The technical bit:

As language models grow, researchers try to improve performance by letting different parts of the model share more information internally. But this often makes training unstable — models break or produce garbage.

mHC fixes this by redesigning how information flows through the model during training. Tests on models with 3 billion to 27 billion parameters showed better scaling and stronger performance with only ~6.7% overhead.

What analysts are saying:

Wei Sun, principal analyst at Counterpoint Research, called it a “striking breakthrough.”

“DeepSeek combined various techniques to minimize the extra cost of training a model,” she said. “Even with a slight increase in cost, the new training method could yield much higher performance.”

Sun added that the paper signals DeepSeek can “bypass compute bottlenecks and unlock leaps in intelligence” — similar to their January 2025 “Sputnik moment” when they launched R1 at a fraction of ChatGPT’s cost.

Why the timing is suspicious:

DeepSeek’s founder Liang Wenfeng co-authored the paper. He typically only does this for major breakthroughs.

DeepSeek also has a track record of publishing foundational research right before major model launches. They did it before R1.

The company is reportedly working on R2 or V4. This paper could be the signal it’s coming soon.

The bigger picture:

While OpenAI, Google, and Meta compete on who can spend the most money on data centers and chips, Chinese AI labs are innovating around constraints.

U.S. export restrictions cut off China’s access to advanced Nvidia chips. So Chinese companies are getting creative with engineering — building better models with less compute.

If DeepSeek can train frontier models more efficiently than companies with unlimited budgets, that’s a problem for the “just throw more GPUs at it” approach dominating Silicon Valley.

Tools:Bloomberg Coverage

🗞️ 2. VCs Predict AI Is Coming for Your Job in 2026

Companies will shift budgets from labor to AI agents this year

Multiple venture capitalists told TechCrunch they expect AI to have a massive impact on the enterprise workforce in 2026 — and it wasn’t even a question about jobs.

The prediction: Companies will start pulling money from labor budgets to fund AI projects.

What investors are saying:

“2026 will be the year of agents as software expands from making humans more productive to automating work itself,” said Jason Mendel, venture investor at Battery Ventures.

Rajeev Dham, managing director at Sapphire Ventures, agreed that 2026 budgets will shift resources from labor to AI.

Marell Evans, founder of Exceptional Capital, predicted companies will pull from their hiring pools to increase AI spending.

The "doom loop" for workers:

Eric Bahn, co-founder at Hustle Fund, isn’t sure exactly what happens next:

“Is it going to lead to more layoffs? Is there going to be higher productivity? Or will AI just be an augmentation for the existing labor market to be even more productive in the future? All of this seems pretty unanswered, but it seems like something big is going to happen in 2026.”

Translation: Nobody actually knows if AI will replace jobs or just make people more productive, but either way, budgets are moving.

The cynical take:

Antonia Dean, partner at Black Operator Ventures, said companies will blame AI for layoffs whether or not they’re actually using AI successfully:

“Many enterprises, despite how ready or not they are to successfully use AI solutions, will say that they are increasing their investments in AI to explain why they are cutting back spending in other areas or trimming workforces.”

The counter-argument:

Not everyone buys the doom narrative. Some predict “2026 will be the year of the humans” because AI hasn’t worked as autonomously as promised.

Expected outcome: New jobs in AI governance, transparency, safety, and data management. Unemployment predicted to average under 4%.

Why this matters:

Whether AI actually replaces workers or not, the narrative is shifting budgets. CFOs are being told to cut labor costs and redirect to AI.

Even if the AI doesn’t work as well as promised, the damage is done. Jobs get cut. Budgets get reallocated. Companies hope AI fills the gap.

2026 is when we find out if that bet pays off.

Tools:TechCrunch Article

🗞️ 3. Apple's Cautious AI Strategy May Finally Pay Off

Sitting on $130B while competitors burn cash on data centers

While OpenAI, Google, and Meta invest hundreds of billions in AI infrastructure, Apple took a different approach: do almost nothing and wait.

That strategy — which got Apple criticized for "falling behind" — might look smart in 2026 as AI bubble concerns grow.

The numbers:

• OpenAI, Google, Meta: Spending hundreds of billions on data centers, chips, model training
• Apple: Sitting on $130 billion+ in cash and marketable securities

Market sentiment is shifting. Investors are starting to question whether massive AI spending can be justified by near-term revenue.

If the AI bubble pops, Apple has cash. Competitors have data centers.

Apple's 2026 plan:

The company’s biggest AI move is an overhauled Siri launching in spring 2026. The updated assistant will be more conversational and handle multi-step tasks.

To power it, Apple is adopting Google’s Gemini — reflecting an internal view that large language models are becoming commoditized and not worth building in-house.

The iPhone advantage:

Unlike AI companies that need standalone apps or web services, Apple can distribute AI features through software updates to its existing device ecosystem.

Competing hardware from AI companies faces major challenges: manufacturing, distribution, ecosystem development. Apple already owns all of that.

Why this could work:

The Information argues Apple’s cautious approach positions them well if:

1. AI spending enthusiasm cools (already happening)
2. The overhauled Siri actually delivers (big if)
3. LLMs become commoditized (Apple’s bet with Gemini)

If all three happen, Apple avoided burning billions while competitors built expensive infrastructure that may not pay off.

The risk:

If AI doesn’t plateau and keeps improving exponentially, Apple will be years behind competitors who invested early.

But Apple has done this before — let others pioneer, then perfect the experience later. Worked with smartphones, tablets, watches.

What to watch:

Spring 2026 Siri launch. If it’s good, Apple’s strategy looks genius. If it’s the same old broken Siri with a ChatGPT wrapper, the criticism was justified.

Tools:MacRumors Coverage

Tip 1: "Negative Space Prompting"

Most people tell AI what to include. Power users tell AI what to EXCLUDE.

Why it works:

Constraints force creativity. When you remove obvious options, AI has to think differently.

The template:

"Give me [what you want]

But do NOT use:
- [Common approach 1]
- [Common approach 2]
- [Common approach 3]

Force yourself to be creative."
  

Example:

Bad: "Give me marketing ideas for our app."

Good: "Give me 10 marketing ideas for our productivity app.

Do NOT use:

• Social media ads
• Paid search
• Email campaigns
• Influencer marketing
• Content marketing

Force creative, unconventional approaches.

Real result difference:

Without constraints: Predictable answers (run Facebook ads, do SEO, send newsletters)

With constraints: Creative answers (partner with competing apps for co-marketing, sponsor productivity podcasts' "tool of the week," create viral productivity challenges on TikTok)

When to use:

Brainstorming, breaking out of obvious solutions, finding differentiated approaches.

Tip 2: "Persona Switching"

Ask AI to answer the same question from multiple expert perspectives. Reveals blind spots and trade-offs you’d never see from a single viewpoint.

Why it works:

Every expert has biases based on their role, incentives, and experience. A CFO cares about different things than a product manager. A customer success manager sees risks a founder ignores.

Single-perspective advice is incomplete. Multi-perspective advice shows you the full picture — including conflicts between what’s "right" for different stakeholders.

The template:

"Answer this question from 3 perspectives:

1. As a [Role 1] who prioritizes [their main concern]
2. As a [Role 2] who prioritizes [their main concern]
3. As a [Role 3] who prioritizes [their main concern]

For each perspective:
- What would they recommend?
- Why would they recommend it?
- What are they worried about?
- What are they ignoring?

Question: [Your question]"
  

Example:

Question: "Should we raise prices from $50 to $75/month?"

Answer from 3 perspectives:

1. As a startup founder focused on rapid growth and market share
2. As a CFO focused on profitability and burn rate
3. As a customer success manager worried about churn and retention

For each: What would they recommend? Why? What are they worried about? What are they ignoring?

What you get:

• Recommendation: Test price increase with new customers first
• Why: Protects existing growth metrics while testing willingness to pay
• Worried about: Losing momentum, competitors undercutting, slowing customer acquisition
• Ignoring: Existing customer sentiment, support burden, margin improvement

• Recommendation: Raise prices immediately for all customers
• Why: Revenue per customer increases 50%, even with 20% churn we come out ahead
• Worried about: Burn rate, runway, unit economics
• Ignoring: Brand damage, customer goodwill, competitive response, sales team morale

• Recommendation: Don’t raise prices; add premium tier instead
• Why: Avoids forcing price increase on happy customers who’ll churn
• Worried about: Support ticket volume, churn spike, angry customers, bad reviews
• Ignoring: Revenue pressure, investor expectations, competitive pricing

The insight:

Now you see the full trade-off space. The founder and CFO both want price increases but for different reasons and with different strategies. Customer Success opposes it entirely.

The best decision might be a hybrid: Premium tier for new customers (CSM’s idea), gradual increase for existing (Founder’s caution), with clear ROI targets (CFO’s math).

You couldn’t see that hybrid solution from a single perspective.

Advanced move:

Add a fourth perspective that’s antagonistic or contrarian:

“4. As a competitor watching this decision, what would you hope we do — and why?”

This reveals strategic blindspots. Your competitor wants you to raise prices so they can undercut you. Knowing that changes the calculus.

When to use:

• Major strategic decisions (pricing, hiring, product direction)
• Evaluating investments or partnerships
• Understanding stakeholder conflicts
• Reframing analysis paralysis by seeing all angles at once
• Preparing for tough conversations (you already know what others will say)

Tip 3: "Assumption Testing"

Make AI state its assumptions before answering. Catches when AI is working from wrong premises and prevents wasted advice.

Why it works:

AI makes assumptions based on patterns in training data. It assumes "normal" situations that might not match your reality. Without surfacing assumptions, you get advice that sounds smart but doesn’t work.

The template:

"Before answering my question, first list the 3–5 main assumptions you’re making about [topic/situation].

Be specific about what you’re assuming is true.

Then, after I confirm or correct your assumptions, answer the question.

Context: [relevant details]
Question: [Your question]"
  

Example:

Bad: "How should I scale my SaaS business?"

Good: "Before telling me how to scale, list the 5 main assumptions you’re making about my SaaS business."

Context: B2B productivity tool, 500 customers, $50/month, 5% monthly churn, acquired via Meta ads at $100 CAC.

Then tell me how to scale."

What AI might reveal:

"I’m assuming:

1. Your unit economics work (CAC < 3x LTV)
2. You’re adding net new customers after churn
3. You have product-market fit
4. Meta ads consistently deliver at $100 CAC
5. You can support 10x volume without infrastructure changes"

Now you can respond:

"Actually, assumptions #1 and #2 are wrong:

• My LTV is only $200, so CAC is half of LTV (losing money per customer)
• We’re flat on growth — churn equals new signups

Given these corrections, what should I do?"

AI’s corrected response:

"You don’t have a scaling problem, you have a unit economics and retention problem. Scaling now would just lose money faster.

Instead:

1. Fix churn first (5% monthly is 46% annual)
2. Test pricing (you may be underpriced)
3. Improve LTV through annual plans
4. Only scale once CAC < 3x LTV"

The insight:

Without assumption testing, AI would’ve given you a scaling playbook (hire salespeople, expand ads) that would’ve killed your company.

By surfacing assumptions first, AI caught the real problem: fix fundamentals before scaling.

When to use:

• Strategic advice
• Business decisions
• Technical recommendations
• Career choices
• Anything where context matters and generic advice could be dangerous

📚 More Resources

Worth your time:

• Model Context Protocol (MCP) – The "USB-C for AI" that's becoming the standard for agentic workflows
• IBM's 2026 AI Predictions – 18 expert predictions on what's coming
• Anthropic's Agentic AI Foundation – Linux Foundation's new initiative for open-source AI agents

A quick note before you go

Thanks for reading this week’s Brain Bytes — I hope something here helped you move faster or think better.

How’d this one land?

• A ✨ Super Helpful!
• B 🤔 Some Good Stuff!
• C 😕 Could be better.

See you next week, — Oliver