🚨 Adobe vs. The AI Revolution: Adobe's share price is down 26% since ChatGPT launched (Nov 2022: ~$400 → Sep 2025: $349).
Instead of strengthening their 34% Photoshop market dominance, they've lost $75B in market value.
How did this happen for a company that had 3 years to prepare for the AI wave? Where is the $3.5B in R&D spend each year going? Here's the full breakdown:
1/ THE MARKET DOMINANCE
In November 2022, Adobe seemed invincible. For 40 years, they had built the ultimate creative monopoly where Design agencies built entire business models around Adobe expertise:
• 34% of the global creative software market
• 90% of professionals dependent on Photoshop
• 26 million subscribers paying $660 annually
• Revenue: $22.6 billion with 89% gross margins
Their stock traded at $400, market cap near $200 billion and CEO Shantanu Narayen called Adobe "the infrastructure for creativity itself."
2/ THE YEAR EVERYTHING CHANGED: 2025
Generative AI rewired demand and collapsed barriers to creating images/video.
Competitors from Midjourney, OpenAI to Google’s Gemini nano-banana pushed quality and speed, shifting value from tool mastery to prompt‑driven outcomes.
Adobe's leadership knew this was coming. But they completely misread what was actually happening...
3/ ADOBE'S RESPONSE
Adobe's response revealed everything wrong with their strategy. They rebranded “All Apps” to Creative Cloud Pro and raised prices:
• Creative Cloud prices jumped 17% to $70/month (+17%).
• Student plans are $29.99/mo first year, then $39.99/mo.
• New users on standard plan were limited to only 25 monthly credits (down from 500) with fewer AI features.
Meanwhile, competitors offered superior capabilities for nearly free. Google offered Nano Banana free (100 daily edits), OpenAI charged just $0.04 per image.
4/ THE $3.5 BILLION R&D SPEND
Here's where Adobe's story becomes perplexing. They spent $3.5 billion annually on R&D and in 2025 launched Firefly Video Model, Image Model 4 Ultra, enhanced mobile apps.
But inside Firefly, Adobe was using Google’s Gemini 2.5 Flash Image and OpenAI models.
The most damning evidence was yet to come...
5/ THE QUALITY GAP EXPOSED
Side-by-side tests reveal uncomfortable truth: AI tools often produce superior results.
Midjourney demonstrates artistic understanding with sophisticated lighting, compelling composition, realism and emotional resonance that Adobe's algorithm-trained models couldn't match.
6/ WALL STREET'S RESPONSE
Despite beating earnings every quarter, Adobe's stock dropped in value: $400 to $349 = 26% decline, wiping out $75 billion in shareholder wealth.
Meanwhile, Big Tech added $8 trillion since ChatGPT launched. Even with record Q3 earnings of $5.99 billion, Adobe stock traded 35% below analyst targets.
Wall Street's message was clear: we see your current profits, but we don't believe in your future.
7/ THE ENTERPRISE SPLIT
Here's the twist: Large enterprises actually increased Adobe spending 40%+ in 2025. They payed premiums for "commercially safe" AI and legal indemnification.
But mid-market companies (Adobe's growth engine) now questions ROI.
Why pay $70/month per employee when marketing teams can create campaigns using cheaper and better AI tools?
9/ BOTTOM LINE
Adobe built a $150B empire on the assumption that professional creativity required professional tools.
AI shattered that assumption overnight.
The company isn't dying, it's evolving into a high-margin enterprise niche player. But the days of creative software monopoly are over.
This isn't just Adobe's problem, it's a preview of how technological platform shifts can invalidate decades of competitive advantage.
The creative software revolution isn't coming. It's here.
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On ProofBench-Advanced—where models prove formal mathematical theorems—GPT-5 scores 20%. Gemini Deep Think IMO Gold hits 65.7%. DeepSeek Math V2 (Heavy) scores 61.9%.
That's second place—but Gemini isn't open source.
This is the best open math model in the world. And DeepSeek released the weights. Apache 2.0.
Here's what they discovered:
1/ Why Normal LLMs Break on Real Math
Most large language models are great at sounding smart, but:
- They’re rewarded for the final answer, not the reasoning.
- If they accidentally land on the right number with bad logic, they still get full credit.
- Over time they become “confident liars”: fluent, persuasive, and sometimes wrong.
That’s fatal for real math, where the proof is the product.
To fix this, DeepSeek Math V2 changes what the model gets rewarded for: not just being right, but being rigorously right.
2/ The Core Idea: Generator + Verifier
Instead of one model doing everything, DeepSeek splits the job: 1. Generator – the “mathematician”
- Produces a full, step-by-step proof.
2. Verifier – the “internal auditor”
- Checks the proof for logical soundness.
- Ignores the final answer. It only cares about the reasoning.
This creates an internal feedback loop:
One model proposes, the other critiques.
Battery storage is already scaling—159 GW deployed globally, 926 GW projected by 2033.
Renewables needed it first. Now AI needs it too.
Tesla is deploying Megapacks at data centers. China is deploying 30 GW this year, integrating storage directly into AI buildout.
Why? Data centers can’t scale without solving three problems:
- 7-year interconnection queues
- power quality GPUs demand
- backup without diesel permits
Batteries solve all three ↓
Why AI Data Centers Need Batteries
Interconnection is broken. Utility connection takes 7+ years. Batteries bypass it. Skip the queue.
GPUs break traditional power. Training loads swing 90% at 30 Hz. Batteries smooth it in 30 milliseconds.
Diesel doesn’t scale. Permitting is hard. For 20-hour backup, batteries are cost-competitive.
The math: ~1% of data center capex.
The Scale
Global capacity: 159 GW by end-2024. Up 85% from 86 GW in 2023. Projected: 926 GW by 2033.
Cost curve: $115/kWh in 2024, down 84% from $723/kWh in 2013. Still falling.
Economics flipped. Solar plus 4-hour storage runs ~$76/MWh. New gas peakers cost $80-120/MWh.
The universe isn’t just expanding — it’s speeding up
13.8 billion years after the Big Bang, astronomers expected gravity to slowly slow cosmic expansion. Instead, when they looked deep into space, they found the opposite: the universe is accelerating.
Whatever drives that acceleration makes up ~70% of the cosmos.
We call it dark energy.
We can measure it. We can see its effects. So what is it, really?
How we figured this out
Cepheid stars: the distance trick
Henrietta Leavitt discovered that certain stars (Cepheid variables) get brighter and dimmer with a regular period — and that period tells you their true brightness → lets us measure distance to faraway galaxies.
Redshift: galaxies on the move
Vesto Slipher used spectra of galaxies to show many had their light stretched to longer, redder wavelengths.
Redder → moving away faster.
Hubble & the expanding universe
Edwin Hubble and Milton Humason combined Cepheid distances with redshift and found a pattern:
>The farther a galaxy is, the faster it’s receding.
That’s the Hubble–Lemaître law: clear evidence that the universe is expanding.
The shock: expansion is accelerating
In the 1990s, two teams studied Type Ia supernovae, stellar explosions so consistent in brightness that they act like “standard candles.”
By comparing how bright they should be to how bright they look, you can get distance.
By measuring redshift, you get how fast they’re moving away.
The surprise:
• The supernovae were dimmer and farther away than expected.
• That only made sense if, over billions of years, the universe’s expansion had sped up instead of slowing down.
This cosmic acceleration is what we now attribute to dark energy.
🚨The White House just launched the Genesis Mission — a Manhattan Project for AI
The Department of Energy will build a national AI platform on top of U.S. supercomputers and federal science data, train scientific foundation models, and run AI agents + robotic labs to automate experiments in biotech, critical materials, nuclear fission/fusion, space, quantum, and semiconductors.
Let’s unpack what this order actually builds, and how it could rewire the AI, energy, and science landscape over the next decade:
1/ At the core is a new American Science and Security Platform.
DOE is ordered to turn the national lab system into an integrated stack that provides:
• HPC for large-scale model training, simulation, inference
• Domain foundation models across physics, materials, bio, energy
• AI agents to explore design spaces, evaluate experiments, automate workflows
• Robotic/automated labs + production tools for AI-directed experiments and manufacturing
National-scale AI scientist + AI lab tech as infrastructure.
2/ The targets are very explicit and very strategic.
Within 60 days, DOE has to propose at least 20 “national challenges” in: