In assessing the future of life with advanced AI, integrity demands separating verifiable truth from corporate hype and speculation. One key truth is that artificial intelligence is not an overnight phenomenon of the 2020s. The field's roots stretch back over half a century – the term itself was coined in the 1950s – and it has weathered cycles of optimism and "AI winters" of disillusionment. Only in the last decade have deep-learning breakthroughs (e.g. neural networks mastering vision and language tasks) vaulted AI from research labs into everyday life. Remembering this long arc helps us avoid exaggerating the novelty of current breakthroughs. AI has been decades in the making; what's new is how rapidly and pervasively it is now being deployed, and how concentrated its control has become.
Today's advanced AI infrastructure is dominated by a handful of private actors wielding enormous resources. Flagship "AGI" labs like OpenAI, Google DeepMind, and Anthropic are funded and owned by tech giants and elite investors, often behind closed doors. OpenAI – founded in 2015 as a nonprofit promising to "benefit all humanity" – pivoted to a for-profit model by 2019 to attract billions in capital from partners like Microsoft. DeepMind has been wholly owned by Alphabet (Google) since its acquisition in 2014, operating largely as an internal research wing of Google. Anthropic, founded by former OpenAI researchers, has secured massive backing from Amazon and Google among others. The scale of investment is staggering: in late 2025 OpenAI was reportedly valued around $500 billion (after a secondary share sale), and Anthropic at $138 billion following a major funding round. Industry observers describe a "multi-trillion-dollar race" between these firms to dominate AI, with corporate titans picking sides (Microsoft and NVIDIA aligning with OpenAI, while Google and Amazon bolster Anthropic). These valuations and alliances are rarely advertised to the public, yet they reveal who is truly powering and steering the evolution of AI.
Much of this AI revolution's infrastructure indeed operates beyond public view, housed in private data centers and proprietary codebases. Cutting-edge models demand immense computing power and data, which only a few organizations command. For example, Anthropic's partnership with Google grants it access to one million of Google's Tensor Processing Unit (TPU) chips across Google Cloud's servers. Amazon has likewise invested $4 billion in Anthropic and built one of the world's largest AI training clusters – nearly half a million custom chips – to fuel Anthropic's models. OpenAI's exclusivity deal with Microsoft's Azure ensures it can tap virtually unlimited cloud compute, in exchange for Microsoft's priority access to OpenAI's technology. The upshot: the keys to advanced AI (compute infrastructure, cutting-edge research talent, and troves of training data) are held by a narrow few. The public sees the outputs (like ChatGPT's answers or DeepMind's AlphaFold discoveries), but the process – the training of models, the tuning of algorithms, the content of datasets – happens in siloed lab environments, far from public scrutiny or independent audit.
Who audits or oversees this fast-evolving ecosystem? At present, meaningful external oversight is scant. There is no comprehensive public regulator examining the code or ethics of GPT-4 or DeepMind's latest systems. For the most part, we rely on the word of the companies themselves. OpenAI's charter and mission statements espouse safety and transparency, but in practice the organization has become more secretive as it gained power. Notably, OpenAI quietly abandoned its pledge of publishing key governance documents, such as board meeting minutes and financial details, despite earlier promises that these would be open to the public. WIRED reporters found that after OpenAI's much-publicized boardroom crisis in late 2023 (when CEO Sam Altman was briefly fired and then reinstated amid investor pressure), the company refused to disclose its updated bylaws or conflict-of-interest policies. In other words, even the governance rules of perhaps the most influential AI lab in the world are now opaque, making it hard to verify who truly holds power and how decisions are made. This opacity isn't unique to OpenAI – most frontier AI models are developed under proprietary regimes with minimal transparency.
Internal ethics and safety teams, meanwhile, lack independent authority and have struggled to check the corporate drive for AI dominance. In one prominent example, Google's Ethical AI team leaders Dr. Timnit Gebru and Meg Mitchell were pushed out after their research highlighted biases and risks in large language models – work that clashed with Google's business priorities. "Companies are not just going to self-regulate" in this climate, Gebru later warned, emphasizing that external pressure and public accountability are needed because a pure profit motive is driving AI development. Her experience underscored the integrity issue: Big Tech may publicly affirm "AI ethics," but when ethical concerns threaten profits or timelines, the tendency is to silence them rather than address them. Integrity as a principle requires that we recognize this pattern and not take corporate AI narratives at face value.
Encouragingly, there are early signs of oversight mechanisms (finally) being erected around these unchecked AI engines. Global institutions have begun to realize that independent audit and governance must catch up. In late 2023, the UK convened a global AI Safety Summit at Bletchley Park, where leading countries and companies agreed on the Bletchley Declaration – including a commitment for major AI firms to allow governments to assess and test their most advanced models for extreme risks before and after deployment. The White House issued an executive order around the same time requiring that U.S. companies share the results of safety tests for any "frontier" AI system (the most powerful models) with the federal government before releasing them to the public. The United Nations is discussing an advisory panel for AI, akin to the IPCC for climate, to provide ongoing expert oversight. These measures, while voluntary or nascent, acknowledge that some transparency and accountability must exist when private AI systems have broad societal impact. In the European Union, the draft AI Act proposes requiring external conformity assessments (audits) for high-risk AI systems, and policymakers are debating how to enforce "AI audit" regimes in practice. Academic proposals have outlined frameworks for third-party AI audits – suggesting independent audit bodies, funded perhaps by a public pool rather than tech companies themselves, to avoid conflicts of interest. If implemented, such mechanisms could shine light into the black boxes of AI development and verify claims about safety and bias for the public.
For now, however, the reality is that advanced AI's development largely answers to boards and investors, not to the public interest. Integrity in our understanding of this landscape means refusing to be lulled by polished PR statements and instead looking at the hard evidence of who controls AI and how. It means asking: Who funds and owns the AI systems shaping our future? – and following the money to find the answer. It means demanding proof for any safety or fairness assurances, rather than trusting a handful of CEOs to be benevolent caretakers of super-powerful technology. In summary, maintaining integrity is about seeing the truth beneath the hype: today's AI revolution is real and potent, but it is neither evenly distributed nor openly governed. The concentration of power in this domain is a fact we must confront. Only by clearly recognizing these truths – the opaque ownership, the minimal oversight, the conflicts of interest – can we begin to assert human agency and insist on structures that serve humanity's adaptive survival, not just corporate agendas. The first step in any honest appraisal of advanced AI's future is to strip away illusions and face these realities head-on.
(Integrity means we start with a clear lens: no illusions, no convenient half-truths. In the next section, we turn to Proof – grounding each claim in evidence and data, to reinforce a truthful narrative.)