@aial.ie's @hellinanigatu.bsky.social reflects on African AI sovereignty, the current AI landscape, as well as the conference itself following her participation in the 2026 Deep Learning Indaba. #DLI2026
Read about it here: aial.ie/blog/worksho...
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An Introspective Look at the African AI Landscape: An organizer’s reflection
Key takeaways from “An Introspective Look at the African AI Landscape” workshop at the 9th inaugural Deep Learning Indaba
![Lessons from adjacent movements. Many of the mechanisms of capture used by Big AI, that we have
discussed in this paper, mirror strategies that have historically been applied by similar industries such as
Big Tobacco, Big Pharma, and Big Oil. Civil society’s efforts to hold big corporations accountable in these
sectors is ongoing and has been met with significant challenges and has often fallen short of meaningfully
countering corporate power [45, 50, 118]. Yet, there are remain lesson to be learned from these efforts and the
braoder scholarship on corporate capture. For example, the OECD report on preventing policy capture in public
decision-making [85] recommends to (i) level the playing field by engaging diverse stakeholders, (ii) ensure
transparency and access to information, (iii) promote accountability via external control, effective competition,
and regulatory policies, and (iv) define clear institutional codes of conduct, promote cultures of integrity, and
establish appropriate frameworks for risk-management. Similarly, in the context of Big Tobacco, Lee [63] calls for
appropriate separation between public and private interests, binding rules for government-industry interactions
to manage conflicts-of-interests, enforcement of transparency and accountability practices, and safeguarding
academic knowledge production from undue industry influence. A 2024 report [5] further calls for applying
transferable lessons from the US Food and Drug Administration on how to regulate and hold Big AI accountable](https://cdn.bsky.app/img/feed_thumbnail/plain/did:plc:26hkfmtr7jrchexamh7hk6rz/bafkreic73dnga3yin6bovkbh2s6geovsezduoqulseis4zewvhm7rfmoum)
![Prior work has typically adopted a dichotomous theoretical lens – distinguishing information capture from
influence on policymaking [108], whereas the most recurring mechanisms in our evidence also include the Elusion
of law. These recurring violations and contentious interpretations of antitrust, privacy, copyright and labour
laws call into question the effectiveness of enforcement. Such pressures, along with weakening the mandates
of regulatory agencies, risk the normalisation of a de facto law in which Big AI operates outside the bounds of
regulatory scrutiny. Furthermore, the stochastic nature of the underlying technology–where the technology
cannot be reliably tested–coupled with the economic power of Big AI corporations enables a normalisation of
“algorithmic states of exception” at scale. Defined by McQuillan [71], this indicates the application of algorithms
as the de facto authority in contexts where their behaviour has not been or cannot be tested, creating conditions
akin to martial law. The conjunction of law-flouting practice and technological affordances thus raises urgent
concerns regarding the contemporary integrity of lawmaking institutions over their respective jurisdictions.
Our annotation of narratives employed provide another qualitative avenue for insight into capture strategies,
which can be further studied to understand the causal or system impact of different narratives on discourse,
public conceptions of AI, and knowledge production. Our finding that there is substantial growth over time in the
Discourse & Epistemic Influence (see Figure 2b) indicates that how AI is framed is becoming increasingly important.
The consistent co-occurrence of each D&EI mechanism with approximately one corresponding mechanism from
other categories indicates the significant role that public-facing campaigns play.](https://cdn.bsky.app/img/feed_thumbnail/plain/did:plc:26hkfmtr7jrchexamh7hk6rz/bafkreie4dmfcomofk4degqch4teyxoocdbpszetfysy5hmeyh5id5dvivq)
![We use the term ‘Big AI’ to refer to the handful of companies that develop and mass deploy large-scale AI technologies – such as large
pre-trained models – built on massive datasets collected through vast, centralised infrastructures, which, with increased integration into
societal infrastructure, continue to exert outsized epistemic, economic, political, and societal influence. The term ‘Big AI ’ also encapsulates
the structural consolidation of AI technologies by Big Tech as their core value proposition, central to their infrastructure, resources, and
strategic investments [121]. While new entities such as OpenAI, Anthropic, DeepSeek and xAI are included in our definition due to their
significant geo-political influence, ‘Big AI’ also marks the shift of existing Big Tech companies – Alphabet, Meta, Amazon, Microsoft, Apple,
NVIDIA – towards becoming “AI-first” companies, further expanding their unprecedented power and influence across all economic sectors
and aspects of public life.](https://cdn.bsky.app/img/feed_thumbnail/plain/did:plc:26hkfmtr7jrchexamh7hk6rz/bafkreib2yn6sn77re456gpt54bg67exarfi5u7rjykn6eot4uri7ia67aa)
