Funding AI infrastructure is a must because transformer architectures require massive amounts of energy.
Frontier labs such as OpenAI, Anthropic, and Google/DeepMind have developed one shared pricing language — and every AI consumer needs to learn it.
AI Implementation Costs
Author: Dr. Anastassia Lauterbach
Type: Whitepaper
Publication Date: August 2026
The economics of AI are challenging for AI makers and incumbent businesses implementing AI.
In a widely cited 2020 essay, Andreessen Horowitz partners Martin Casado and Matt Bornstein observed that AI companies often spend 25% or more of revenue on cloud compute, and up to a further 10–15% on the ongoing work of cleaning and labelling training data — one reason AI gross margins tend to sit well below those of traditional SaaS businesses. These challenging AI economics were since then confirmed in AI Edutainment interviews with technology leaders. Incumbent companies implementing AI will spend even more. AI services provided to them will only partly cover the expenditure of Big Tech to fund AI infrastructure. Hyper-scalers issued ~$121 billion in bonds in 2025 alone, and J.P. Morgan projects $5.5 trillion of AI infrastructure spend by 2030.
Usage is near-universal; understanding is not
Interactive oral examinations make cheating difficult while making structured learning (and genuine understanding) visible.
AI Literacy in Teachers
Author: Dr. Anastassia Lauterbach
Type: Whitepaper
Publication Date: August 2026
The people preparing our children for the future — teachers — are using AI without properly understanding how AI and generative AI technologies work. They are employed by institutions that mostly have no plan around AI Literacy and AI Fluency, and they live in countries whose governments have no strategic vision on how to make AI work for the economy, society and education.
There are few exceptions to learn from — and the best news is that the problem is solvable if there is genuine willingness to address it. Every single teacher can move a needle for his/her classroom, even if fixing national policies won’t happen any time soon.
Making AI more responsible requires making it more intelligent, not just more capable.
Transparent failure reporting—the systematic, public disclosure of what AI systems cannot do, where they fail, and under what conditions they become dangerous—is the minimum requirement to increase accountability around AI. It does not currently exist.
What Should Responsible AI Be About
If We Put The Noise Aside?
Author: Dr. Anastassia Lauterbach
Type: Whitepaper
Publication Date: April 2026
Responsible artificial intelligence (RAI) may be the most important concept of this decade—and it’s currently one of the least precisely defined. The term is used interchangeably to mean governance, compliance, ethics, technical safety, and a vague commitment to “doing good.” This ambiguity is a strategic liability: when the definition of responsible AI is blurred, so are the criteria for funding it, addressing it with process guidelines and policies, and defining clear principles on how to hold anyone accountable for failing at it.
AI Edutainment is proposing an RAI framework, which goes beyond the practicalities of developing and using AI tools and technologies. It does not recycle compliance checklists. It does not provide summaries of existing guardrail debates or repackage AI ethics principles that have been circulating since 2016.