Every AI consultant has a choice: which platform do you build on? Which company do you trust with your clients’ workflows, their data, their daily operations?
We chose Anthropic. Not because Claude was the trendiest model. Not because of a partnership deal. We chose Anthropic because, after spending years in tech and watching the AI industry from the inside, we believe they are the only major AI company where safety and capability come from the same place — and where the people in charge have the scientific credibility to back it up.
This isn’t a sales pitch for Claude. It’s an explanation of values. If you’re going to trust someone to train your team on AI, you deserve to know why we chose the platform we did.
The person behind the company matters
Dario Amodei is not a tech founder who stumbled into AI. He holds a PhD in biophysics from Princeton, where his research focused on understanding how neural circuits actually work — not just what they do, but how and why. That focus on interpretability became the defining philosophy of Anthropic.
Before founding Anthropic, Amodei led development of GPT-2 and GPT-3 at OpenAI and co-invented reinforcement learning from human feedback (RLHF) — the technique that made modern AI assistants possible. He and his sister Daniela left OpenAI over disagreements about safety culture. They didn’t leave because they were pessimistic about AI. They left because they believed getting AI right mattered more than getting there first.
As of early 2026, Anthropic is valued at approximately $380 billion and holds 32% of the enterprise AI market — more than OpenAI. That market share wasn’t won through hype. It was won through a product that works and an approach that enterprises trust.
Safety and optimism are the same position
In October 2024, Amodei published a 50-page essay called “Machines of Loving Grace” — one of the most substantive public documents on what AI could actually accomplish, written by someone with the technical background to make credible claims.
His core prediction for biology and medicine:
“My basic prediction is that AI-enabled biology and medicine will allow us to compress the progress that human biologists would have achieved over the next 50–100 years into 5–10 years.”
He’s not talking about AI as a better data analysis tool. He’s talking about AI as a virtual biologist that designs experiments, invents measurement techniques, and directs research. Within 5–10 years of powerful AI arriving, he predicts:
- Near-complete prevention and treatment of most natural infectious diseases
- Elimination of most cancer as a leading cause of death
- Effective prevention and cures for most genetic diseases
- Prevention of Alzheimer’s disease
- Effective treatments or cures for most mental illnesses
These aren’t idle promises. AlphaFold has already predicted the structure of over 200 million proteins. Its creators won the Nobel Prize in Chemistry in 2024. The first AI-designed drug reached positive Phase 2 clinical results in late 2024. AI cancer detection tools are outperforming radiologists in pancreatic cancer screening by 34%.
But here’s what makes Amodei’s position coherent: he’s not making the optimistic case instead of taking risks seriously. He’s making it because he takes them seriously. The potential upside — defeating cancer, ending the diagnostic odyssey for 350 million rare disease patients, giving a subsistence farmer the same health guidance available at the Mayo Clinic — is so immense that it justifies the effort and care required to get AI development right.
The guardrails are not the enemy of the vision. They are what makes the vision achievable.
Why Anthropic’s approach is different
The AI safety debate is real, urgent, and not going away. Geoffrey Hinton — the “Godfather of AI” and 2024 Nobel laureate — estimates a 10–20% chance AI could cause human extinction within 30 years. The Future of Life Institute has called for a prohibition on superintelligence development, signed by over 700 researchers. These are not fringe voices.
Against this backdrop, here’s what Anthropic does differently:
Constitutional AI replaces the standard approach of having humans label good and bad outputs. Instead, the AI critiques and revises its own responses against explicit written principles — a “constitution” drawn from the UN Declaration of Human Rights and trust and safety best practices. This creates a more transparent and auditable alignment process.
Responsible Scaling Policy establishes AI Safety Levels (ASL-1 through ASL-4+), modeled after biosafety levels. In May 2025, Anthropic activated ASL-3 protocols for Claude Opus 4 as a precautionary measure — the first time any company publicly escalated its safety tier for a production model. The policy commits Anthropic to pausing training if safety measures can’t keep pace with capability gains.
Interpretability research at Anthropic represents some of the field’s most important work on understanding what neural networks actually compute. In 2024, they extracted millions of interpretable features from Claude, and in 2025, they revealed internal reasoning mechanisms through “circuit tracing” — showing, for instance, that models pre-select rhyming words before composing poetry.
The contrast with competitors is instructive. OpenAI formed a Superalignment team in 2023, then effectively dissolved it in 2024 after its co-leads resigned. One wrote publicly that “safety culture and processes have taken a backseat to shiny products.” Anthropic’s own research discovered the first empirical evidence of a model engaging in “alignment faking” — and they published it openly rather than burying it.
The environmental question we take seriously
We’d be dishonest if we didn’t address the environmental cost of AI. Data center electricity consumption has tripled in a decade. A single ChatGPT query uses roughly 10 times the energy of a Google search. Microsoft, Google, and Amazon are all missing their own climate targets, with emissions rising 23–48% above baselines.
This matters. And it’s why we pay attention to which companies are investing in solutions versus just scaling consumption. Anthropic’s infrastructure choices, their work on model efficiency, and their commitment to transparency about compute costs are part of why we trust them as a partner. No AI company has solved this problem yet. But we’d rather build on a platform led by people who acknowledge the problem than one that pretends it doesn’t exist.
The equity dimension
One of the most humanizing passages in Amodei’s essay describes AI as “a brilliant friend who happens to have the knowledge of a doctor, lawyer, and financial advisor.” Unlike a formal professional consultation, this friend speaks frankly, engages with the actual problem, and gives real information based on your specific situation.
For most of human history, access to that kind of knowledgeable, personalized guidance has been reserved for the wealthy. AI could democratize it — giving everyone the same quality of guidance currently available only to people with access to top specialists.
But the equity assumption is not automatic. AI tools trained primarily on wealthy, Western populations have documented bias problems. The infrastructure to deliver AI-enabled services — electricity, connectivity, trained professionals — is absent in the places that need it most. Nearly 80% of jobs at risk of AI displacement pay less than $38,000 annually.
This is exactly why the company behind the AI matters. A company that publishes its safety research, commits to responsible scaling, and is led by scientists rather than hype merchants is more likely to build technology that benefits everyone — not just the already-advantaged.
What this means for your team
When we train your team on Claude, we’re not just teaching prompting techniques. We’re building on a platform created by people who think deeply about what AI should and shouldn’t do. A platform with guardrails designed by researchers who publish their failures alongside their successes. A platform led by someone whose PhD was about understanding how intelligence works, not just making it faster.
We believe AI should make your team more capable, not more dependent. It should save hours, not replace judgment. It should be a tool you understand, not a black box you hope works correctly.
That’s why we chose Anthropic. That’s why we built everything on Claude. And that’s why, when we train your team, we don’t just show them what buttons to press — we help them understand what they’re building with and why it matters.
Nicole Patten is the founder of Elevate Online and one of fewer than 10 Claude-specific training providers globally. She spent 7 years at Google as a Senior UX Engineer before dedicating her career to helping teams use AI responsibly and effectively. 100% of her business runs on Claude.