En-Do Foresight: Who Leads AI Over the Next 24 Months? Anton Vibe Art.
Not investment advice.
Likely leader: OpenAI (≈45%), with Google DeepMind (≈30%) running neck-and-neck. Anthropic (≈10–12%) and Meta (≈8–10%) shape key niches (enterprise reliability and open-weights, respectively). The field remains multipolar, but frontier breakthroughs plus distribution will determine the podium.
The Frame: What “Leadership” Actually Means
“AI leadership” isn’t a single trophy. Over the next 24 months it means a bundle of capabilities:
- Frontier model quality (reasoning, multimodal grounding, long-context coherence).
- Scale of deployment (compute access, data pipelines, reliability at production).
- Distribution (how fast models reach users through platforms, OSes, enterprise suites).
- Learning flywheel (telemetry, alignment, post-training, eval culture).
This article applies a simple foresight loop: priors → 10ish hard signals → scenarios → what would change my mind.
Where We Start (Priors)
- OpenAI is pacing the shift to reasoning-first frontier models and has strengthened access to at-scale compute — an essential ingredient for the next jumps in capability.
- Google DeepMind is pushing Gemini 2.x/2.5 with unusually deep distribution (Android, Chrome, Search, Workspace) and its own TPU stack — an advantage in data + delivery.
- Anthropic has momentum in reliability, tool use, and enterprise controls — winning trust where safety and predictability matter most.
- Meta drives the open-weights frontier (Llama family), accelerating research diffusion and ecosystem breadth, including edge and public-sector pilots.
- NVIDIA’s platform remains the metronome for the entire industry; the pace of Blackwell-class systems and memory supply is a lever on everyone’s roadmap.
The Short List: Signals That Actually Move the Needle
- Compute Access, Not Hype. Real contracts and delivery windows (GPUs/TPUs/ASICs, memory, interconnect) beat press releases.
- Frontier Releases With External Wins. Reasoning evals, tool-use success, long-context reliability — verified by independent benchmarks, not cherry-picked demos.
- Distribution Gravity. OS-level integration and enterprise suites amplify model advantages faster than greenfield apps do.
- Unit Economics. Are AI features lifting gross margin/ARPU or just raising inference bills?
- Safety/Policy Posture. Safer agents and auditability unlock regulated verticals; regulatory shocks can reshuffle compute and data access.
- E2E Latency/Cost Curves. If inference cost/latency falls in lockstep with capability, adoption accelerates; if it doesn’t, budgets balk.
Scenarios (24 Months)
1) Base — OpenAI Extends the Lead (≈45%)
Why this path:
- Access to very large-scale compute + cadence of reasoning-centric releases.
- Tight product loop through a major ecosystem (developer tools, productivity suites, real-time experiences).
What you’d see: SOTA reasoning and robust multimodal, consistent “wins” in independent evals, strong developer retention, and credible cost/latency curves for production use.
2) Alt-1 — Google DeepMind Pulls Even or Ahead (≈30%)
Why this path:
- Gemini 2.5 lands repeated frontier wins (planning, tool-use, long context) and rides Google’s distribution rails (Search, Android, Workspace).
- TPU platform closes gaps on cost/latency; policy/compliance strength expands enterprise footprint.
What you’d see: External eval leadership in multi-step tasks, rapid OS/service embedding, and visible enterprise lift tied to Gemini features.
3) Alt-2 — Anthropic / Meta Lead by Domain (≈15–20% combined)
What you’d see: Sector-specific leadership (compliance-heavy industries for Anthropic; open platforms/edge for Meta) while frontier crown remains shared.
Tail Risk — Policy / Energy / Supply Shocks (≈10%)
Export controls, antitrust remedies, or power-siting constraints for data centers slow one giant while others surge. Leadership becomes temporarily polycentric.
“What Would Change My Mind” (Forecast Stop-Loss)
I’d upgrade Google’s odds if, across two consecutive quarters, Gemini 2.5/3.0:
- Tops independent reasoning evals and
- Demonstrates margin-accretive wins in flagship products, and
- TPU cloud shows durable cost/latency advantage at scale.
I’d reinforce OpenAI’s base case if:
- Massive compute contracts deliver on time;
- Next-gen models sustain SOTA across reasoning/multimodal;
- Inference cost/latency drops enough to widen enterprise rollout.
I’d downgrade any leader on:
- Dual shock of regulation + chip/energy bottlenecks, or
- Two quarters of weak ROI from “AI features” in core products.
Operator/Investor Checklist (Pin This)
- Compute reality: Delivery vs. promise (GPUs/TPUs, memory, networking).
- External evals: Factual, chain-of-thought-free scores; tool-use success rates.
- Distribution: OS hooks, default surfaces, enterprise suites; time-to-adoption.
- Cost curves: Inference $/token, latency at target quality, caching/streaming wins.
- Safety & audit: Policy readiness; agent oversight; eval transparency.
- Margins: Do AI features expand gross margin/ARPU in public reports?
Bottom Line
The next 24 months likely belong to OpenAI, with Google DeepMind so close that quarterly execution could flip the board. Anthropic and Meta won’t “win everything,” but they’ll lead where it counts — enterprise reliability and open-weights ecosystems — shaping developer choice and policy trajectories.
Leadership won’t be a single crown. It will be frontier capability + production economics + distribution — executed quarter after quarter.