Compute is the primary bottleneck for AI progress and the main determinant of success for AI companies, superseding algorithms or capital [6, 11].
Data sovereignty requirements, driven by the US CLOUD Act, are fundamentally fracturing the global cloud market and ending the unchallenged dominance of US hyperscalers [3, 5, 28].
China is competing asymmetrically in AI by focusing on full-stack systems co-design to maximize hardware performance and using adversarial distillation to copy Western model capabilities, rather than competing directly on chip manufacturing [4, 9].
The next major breakthroughs in AI will be in scientific domains like material science, which are seeing 'super-exponential' returns on compute, unlike saturated areas like coding [1, 13, 14].
The traditional venture capital model is ill-suited for funding frontier AI, as evidenced by its widespread failure to invest in Anthropic's seed round and a broader misallocation of capital [7, 18, 20].
Circa 2 years ago
Anthropic attempts to raise a $500M seed round, but after 21 rejections from 22 VCs, re-anchors to a $100M round with no participation from traditional VC firms. This event shapes Midha's view on the inadequacy of traditional venture capital for frontier AI [18, 20, 25].
Circa 1 year ago
Midha notes that major LLMs like Claude and Gemini were performing poorly on scientific analysis benchmarks [19], highlighting the data gap [1] that would inform his focus on AI for science.
Present
Midha identifies a 'GPU wastage bubble' [17] and observes that data sovereignty requirements, driven by the US CLOUD Act [3], are creating the first significant challenge to the dominance of US cloud hyperscalers in 15 years [5].
Present
Midha's company AMP has secured 1.3 gigawatts of compute infrastructure, financed with 80% debt [21, 30], and his incubation, Periodic Labs, is actively using LLMs and robotics to synthesize new materials in a 30,000 sq ft facility [8, 23].
Next 4 years
Midha posits that for Europe to achieve technological sovereignty, it must build a local compute infrastructure comparable in scale to Google's current 12-15 gigawatt capacity within this timeframe [10].
▶The Geopolitics of ComputeApr 2026
Midha frames the AI race as a geopolitical struggle for technological sovereignty, centered on compute infrastructure. He highlights China's strategy of systems co-design [9] and adversarial distillation [4] to catch up, and Europe's need to build a massive, independent compute stack to counter US dominance enforced by policies like the CLOUD Act [3, 10, 28].
For investors and analysts, this theme suggests that AI infrastructure should be viewed not just as a tech sector, but as a strategic national security asset, where data sovereignty will drive significant regional investment and market fragmentation.
▶Compute as the Ultimate ConstraintApr 2026
Midha argues that access to compute is the single most critical bottleneck in the AI industry, superseding capital or algorithms. This scarcity will determine the winners among inference companies [6], poses the greatest threat to the revenue models of major labs like OpenAI and Anthropic [11], and is currently plagued by a 'GPU wastage bubble' [17].
Companies that can secure and efficiently manage massive compute supply, like his own company AMP [21], will hold immense power and value, potentially becoming the new 'kingmakers' of the AI era.
▶AI's Next Frontier: Scientific DiscoveryApr 2026
Midha posits that while AI's gains in text-based domains are hitting diminishing returns [13], its application in science is just beginning to show 'super-exponential gains' [14]. He points to the lack of specialized physics and chemistry data as a key hurdle [1] and showcases his own venture, Periodic Labs, which uses LLMs to discover new materials like superconductors [8].
The most significant long-term value from AI may not be in chatbots or coding assistants, but in solving fundamental scientific problems, creating a new class of AI-native R&D companies.
▶The Failure of Traditional Venture Capital in Frontier AIApr 2026
Midha critiques the traditional venture capital model for its inability to grasp the scale and nature of frontier AI investment. He uses Anthropic's seed round, where 21 of 22 VCs passed [20] and no traditional firms participated [18], as a prime example of this failure. He claims there is a significant misallocation of public capital into VC managers who are missing the most important opportunities [7].
The capital structure for frontier AI may require new models beyond traditional VC, involving public benefit corporations, massive debt financing [30], and strategic partnerships, as demonstrated by his company AMP [22].