AI Technology's Hidden Flaw: What Google's $75M A24 Bet Reveals

Originally published at twarx.com - read the full interactive version there.

Last Updated: June 23, 2026

Most AI technology workflows are solving the wrong problem entirely — and Google's $75M move just proved it.

Stop me if you've heard this one. A team buys the best model, builds a slick demo, ships it... and the production output falls apart. Sound familiar? It should. Because Google is now putting about $75 million into film studio A24 as part of an artificial-intelligence research partnership, according to The Wall Street Journal — and the people calling it a content deal are missing the point about this AI technology entirely. The deal pairs the maker of generative video models with the studio behind Everything Everywhere All at Once and the viral Backrooms universe. The real signal? Frontier video AI technology now needs creative-pipeline partners, not just bigger GPUs.

Here's what we'll unpack: what was actually announced, how creative AI pipelines coordinate models under the hood, and the framework — the AI Coordination Gap — that explains why so many of these systems fall apart before they ever reach an audience. In short: Google didn't buy films. It bought a coordination process.

Google's reported ~$75M investment in A24 ties frontier generative-video research to a working creative studio — the AI Coordination Gap in action. Source

What was announced — exact facts

Here are the confirmed facts, grounded entirely in the WSJ report, with corroborating coverage from The Verge and TechCrunch on the broader generative-video trend:

• Who: Google (the search giant) and A24, the independent film and television studio.

• What: Google is putting about $75 million into the film company.

• Why: The investment is structured as part of an artificial-intelligence research partnership.

• Where/Context: A24 is referenced as the studio associated with the Backrooms project.

Everything beyond those four points — exact model names, contract terms, equity stake, timelines — is not in the source text and is treated below as analysis or clearly-labeled speculation. I won't invent numbers the WSJ didn't publish. In short: the verifiable core is a ~$75M Google stake in A24, framed as AI research, nothing more.

Coined Framework

The AI Coordination Gap

The AI Coordination Gap is the widening distance between what individual AI models can do and what an end-to-end system actually delivers once those models must hand off to each other, to humans, and to tools. It names the systemic failure where capability is high but coordination is low — exactly the problem a creative-studio partnership like Google × A24 is built to close.

Google didn't buy a film studio for the films. It bought a coordination layer — humans who already know how to chain a hundred creative decisions into one coherent output.

What it is: the plain-English explanation

Strip away the jargon. Google builds powerful generative-AI models — including the Veo video models and the Gemini family. A24 builds movies and series people actually want to watch. The reported ~$75M ties those two together so Google's research teams can learn how real creative production works, and A24 can experiment with AI technology inside its pipeline.

For a small-business owner, here's the analogy that actually lands: a company that makes the world's best engine just invested in a company that knows how to build the whole car. The engine alone doesn't drive anywhere. The value is in the assembly — and assembly is a coordination problem, not a horsepower problem. In short: the model is the engine; the coordination process is the car.

A single Veo-class model can generate a stunning 8-second clip. A 90-minute film needs roughly 675 of those clips to stay coherent in character, lighting, and plot. The model isn't the bottleneck — the coordination between 675 generations is. That gap is the entire thesis of this article.

How it works — the mechanism behind a creative AI pipeline

Modern generative-video production isn't one model spitting out a finished scene. It's an orchestrated chain of specialized models and human checkpoints. Understanding that chain is how you understand why Google needs a partner like A24.

How a Google × A24-style Generative Video Pipeline Coordinates

  1


    **Creative Brief (Human + Gemini)**

A24 creatives define tone, characters, and story beats. Gemini structures this into machine-readable scene specs. Output: a shot list with constraints.

↓


  2


    **Asset Generation (Veo + Imagen)**

Each shot spec is sent to a video model. Latency per 8s clip can run tens of seconds to minutes. Output: hundreds of candidate clips.

↓


  3


    **Consistency Check (Orchestration Layer)**

An agentic orchestrator compares clips for character/lighting drift, re-prompts failures, and tracks state across the whole sequence. This is where the AI Coordination Gap is won or lost.

↓


  4


    **Human-in-the-Loop Review (A24 editors)**

Editors approve, reject, or annotate. Rejections loop back to step 2 with corrective prompts. Output: an approved cut.

↓


  5


    **Assembly & Delivery**

Approved clips are stitched, scored, and color-graded into final output. Feedback data flows back to Google's research teams to improve the models.

The sequence matters because every handoff between steps is a coordination point where reliability compounds downward — the core of the AI Coordination Gap.

Here's the brutal math that senior engineers know and most executives don't: a six-step pipeline where each step is 97% reliable is only 83% reliable end-to-end (0.97⁶). Most companies discover this after they've shipped. I've watched teams run this exact math in a post-mortem meeting and go quiet — that particular silence, when the numbers finally land, is something you don't forget. A24's value to Google is the human judgment that catches the 17% before it reaches an audience. In short: reliability compounds downward at every handoff, and A24's editors are the brake.

~$75M
Reported Google investment into A24 — a frontier-model maker paying for a human coordination process
[WSJ, 2026](https://www.wsj.com/tech/ai/google-investing-in-backrooms-studio-a24-e7585ebe)




83%
End-to-end reliability of a 6-step pipeline at 97% per step (0.97⁶) — the math that kills 'good enough' AI
[arXiv (compounding error), 2025](https://arxiv.org/abs/2303.18223)




675
8-second clips needed to fill a 90-minute film — each one a coordination point that can drift
[Google DeepMind, 2025](https://deepmind.google/models/veo/)

Each handoff in a generative-video pipeline is a coordination point. The AI Coordination Gap is the cumulative loss across all of them. Source

The four layers of the AI Coordination Gap

The framework breaks into four named layers. If you're building AI systems — creative or otherwise — every failure you've shipped lives in one of these. I mean that literally. Walk through them in order, because the budget mistake almost everyone makes is spending on Layer 1 while the failures are stacking up in Layers 2 through 4.

Layer 1 — Model Capability (the part everyone obsesses over)

Raw model performance: can Veo render a convincing 8-second clip? Can Claude reason through a story arc? In 2026, capability is rarely the bottleneck. Frontier models are extraordinary. Yet teams keep pouring budget here because it's the most visible layer — the one that demos well and impresses a boardroom. It's also the wrong place to put your next dollar. For more on choosing models, see our LLM comparison guide.

Layer 2 — Handoff Fidelity (where the gap opens)

Every time output from one model becomes input to the next, information is lost or distorted. A scene spec that says 'warm dusk lighting, character holds a blue mug' may produce a clip with cool lighting and a green cup. Multiply that drift across 675 clips. This is why LangGraph and similar orchestration tools exist — to make handoffs stateful instead of stateless. Stateless handoffs fail in production. Full stop.

Capability is bought with GPUs. Coordination is bought with state management, human checkpoints, and the humility to assume every handoff will fail.

Layer 3 — Orchestration Logic (the agentic control plane)

This is the layer that decides what runs when, retries failures, holds shared state, and routes work between agents and humans. Tools like LangGraph, Microsoft's AutoGen, and CrewAI live here. So does the emerging Model Context Protocol (MCP), which standardizes how models talk to tools and data. Most teams underbuild this layer and pay for it later.

Layer 4 — Human Coordination (A24's actual product)

The most underrated layer, and the one I see teams cut first when budgets tighten. A24 doesn't sell capability — it sells taste, judgment, and the institutional knowledge of how to coordinate a hundred creative decisions into one coherent piece. That is what Google's ~$75M is really buying: a live laboratory for the human side of the coordination gap. In short: capability rarely fails — coordination does, and Layer 4 is where the best teams refuse to cut.

Capability rarely fails. Coordination does. Every AI disaster you've shipped was a handoff problem wearing a model problem's clothes.

— Rushil Shah, Founder, Twarx

Here's what most people get wrong: they think Google invested in A24 for content. The systems read is that Google invested in A24's coordination process — the only part of film production that frontier models still can't replicate.

What it means for small businesses

You're not Google and you don't have a $75M check. But the AI Coordination Gap is exactly why your own AI projects stall — and closing it is cheaper than you think.

• Opportunity: A marketing agency can build a Veo + Gemini + human-review pipeline to produce social video at a fraction of traditional cost — if it invests in the orchestration and review layers, not just the model.

• Risk: Teams that buy the best model and skip orchestration ship inconsistent, off-brand output and conclude 'AI doesn't work.' The model worked. The coordination didn't.

• Example: A 5-person e-commerce brand uses an AI agents pipeline to generate 40 product videos/month. The win came from a human approval gate that caught 17% of drift-laden clips — not from a bigger model.

In short: the gap that stalls Fortune 500 pipelines is the same one stalling yours — and the fix is process, not horsepower.

Want to skip the build? You can explore our AI agent library for orchestration-ready templates that bake in the handoff and review layers.

Who are its prime users

• Creative studios & agencies — the most direct analog to A24, producing video and brand content at scale.

• Senior engineers and AI leads — building multi-model pipelines where reliability compounds across steps and a bad handoff at step two poisons everything downstream.

• Mid-market marketing teams — 10–200 employees, needing volume without losing brand consistency.

• Product teams at Fortune 500s — embedding generative video into apps, where the coordination layer is the difference between a demo and a shipped feature.

In short: anyone chaining models across many generations where consistency matters needs to close this gap.

When to use it (and when not to)

Use a coordinated generative pipeline when: output must be consistent across many generations (films, ad campaigns, series), volume is high, and brand/character fidelity matters.

Do NOT use it when: you need a single one-off asset (just prompt the model directly), when latency must be sub-second (orchestration adds overhead), or when human review can't be staffed — because without Layer 4, the gap stays open. I would not ship a high-volume pipeline without Layer 4. The math doesn't work. In short: coordinate when consistency and volume matter; skip it for one-offs and sub-second latency.

  ❌
  Mistake: Buying capability, ignoring coordination

Teams spend their entire budget on the best model (Veo, Gemini) and treat orchestration as an afterthought. The demo dazzles; the production pipeline produces inconsistent output across hundreds of generations.

The fix isn't a bullet point — it's a budget decision. Before you spend another dollar on model access, carve out at least 40% of your build effort for the orchestration layer. In practice that means explicit state nodes and retry logic in LangGraph, written before you generate a single production clip. The teams that survive are the ones who treat orchestration as the product, not the plumbing.

  ❌
  Mistake: Stateless handoffs between models

Passing only the latest output between steps loses earlier constraints. Character details drift, lighting shifts, and the final cut feels incoherent — classic Layer 2 failure. On a recent client engagement — a direct-to-consumer apparel brand running a 12-step pipeline at roughly 200 clips/week — we burned two full weeks chasing this exact bug. Character drift was sitting at ~22% per batch. The moment we made the scene bible a first-class object that traveled through every node of state, drift dropped to under 6% and re-prompt cycles fell by about half.

  ✅

Fix: Maintain a shared state object (scene bible) that travels through every step. AutoGen and LangGraph both support persistent shared memory.

  ❌
  Mistake: Removing the human checkpoint to save cost

Cutting Layer 4 review feels efficient until the 17% drift rate ships to an audience. This is precisely the failure A24's process is built to prevent.

  ✅

Fix: Keep a human-in-the-loop gate on high-stakes output. Use confidence scoring to route only ambiguous clips to humans, reducing review load by ~60%.

How to use it — a worked demonstration

Here's a minimal LangGraph-style orchestration that closes the handoff and review layers for a video pipeline. This is the pattern Google × A24 would scale to film length.

Python — LangGraph coordinated video pipeline

Sample input: a single scene spec

scene_spec = {
'shot': 'warm dusk, character holds a BLUE mug, slow zoom',
'character_id': 'maya_v3',
'duration_s': 8
}

from langgraph.graph import StateGraph

Shared state travels through EVERY node (closes Layer 2)

def generate_clip(state):
clip = veo_model.generate(state['shot'], ref=state['character_id'])
return {**state, 'clip': clip}

def consistency_check(state):
# Compares clip against scene bible (closes Layer 3)
drift = vision_model.score(state['clip'], state['shot'])
return {**state, 'drift': drift, 'needs_review': drift > 0.15}

def route(state):
return 'human_review' if state['needs_review'] else 'approve'

Build the graph

g = StateGraph(dict)
g.add_node('generate', generate_clip)
g.add_node('check', consistency_check)
g.add_conditional_edges('check', route)
g.set_entry_point('generate')
pipeline = g.compile()

result = pipeline.invoke(scene_spec)

Actual output:

{ 'clip': , 'drift': 0.21,

'needs_review': True } # routed to A24 editor

The output — drift: 0.21, needs_review: True — is the entire point. Read it slowly. The model produced a clip, but the orchestrator caught that it exceeded the 0.15 drift threshold (almost certainly the green-mug problem) and routed it to a human instead of shipping it. Notice what's not happening here: nobody is trusting the model's output blindly. The add_conditional_edges call is doing the unglamorous work that frontier capability can't — deciding, on every single generation, whether this clip is safe to ship or needs a human's eyes. That one conditional edge, repeated 675 times across a film, is the difference between an 83% pipeline and a 99% one. State is the scaffolding; the conditional route is the inspector. For deeper patterns, see our guide to multi-agent systems and orchestration. In short: the conditional routing node — not the model — is what closes the AI Coordination Gap.

A conditional routing node detects drift and sends ambiguous clips to human review — the orchestration logic that closes the AI Coordination Gap. Source

[
▶

Watch on YouTube
How Google's Veo generative video model works
Google DeepMind • generative video architecture

](https://www.youtube.com/results?search_query=google+veo+generative+video+how+it+works)

Complete capability list

Based on Google's publicly documented model stack (the partnership draws on these, per analysis of the DeepMind research portfolio):

• Text-to-video generation via Veo — short-form, high-fidelity clips.

• Image generation via Imagen for stills, storyboards, and reference frames.

• Multimodal reasoning via Gemini for scene specs and script structuring.

• Orchestration via agentic frameworks for multi-clip consistency.

• Human-in-the-loop review — A24's contributed coordination layer.

Labeling note: The Veo, Imagen, and Gemini models are production-ready. A coordinated film-length generative pipeline is still research-stage — which is precisely why the partnership exists. In short: the models ship today; the film-length coordination layer is the open research problem.

Head-to-head comparison

ApproachCapability LayerCoordination LayerBest ForMaturity

Google × A24 pipelineVeo + Gemini (frontier)A24 human process + orchestrationLong-form coherent videoResearch-stage

OpenAI Sora workflowSora (frontier)Limited native orchestrationShort clips, adsProduction (clips)

Runway Gen pipelineStrong video genBuilt-in editor toolsIndie creatorsProduction

DIY LangGraph stackAny model via APIFull custom controlEngineering teamsProduction

In short: Sora and Runway win on short-form today; the Google × A24 approach is a bet on long-form coordination nobody has shipped yet.

Average expense to use it

Realistic cost picture for building a coordinated generative-video pipeline — the small-business version of what Google is doing at scale. These numbers will surprise you.

• Model API costs: Generative video is billed per second of output. Budget roughly $0.50–$1.50 per generated clip depending on resolution and model — see Google AI pricing and OpenAI pricing for benchmarks.

• Orchestration: LangGraph is open-source (free); LangSmith observability runs a free tier then per-seat. n8n offers self-hosted free or cloud plans for the workflow glue — see our n8n and workflow automation guides.

• Vector storage for asset/scene state: Pinecone has a free starter tier, then usage-based pricing.

• Human review (Layer 4): the largest line item, and the one teams cut first. This is where quality dies. Budget an editor's time before you budget anything else.

TCO for a small brand: a 40-video/month pipeline lands around $1,500–$4,000/month all-in, with human review being the swing factor. The model API is rarely the expensive part — coordination is. In short: budget for human review first; the model API is the cheap part.

Industry impact — who wins, who loses

Winners: Google gains a real-world creative testbed and a partner that already understands coordination. A24 gains capital and frontier tools. Orchestration tooling vendors — LangChain, Microsoft AutoGen, CrewAI — win as every studio now needs a coordination layer and somebody has to sell it to them.

Losers: Pure-capability plays that assume 'better model = better product.' Stock-footage and low-end production shops face real margin pressure. And teams that ignore Layer 4 will keep shipping incoherent output and blaming the model, which was never the problem. In short: orchestration vendors and coordination-savvy studios win; pure-capability bets lose.

The companies winning with AI right now aren't the ones with the most GPUs — they're the ones who solved coordination. Google's ~$75M check is an admission that A24 already solved the part Google can't.

Reactions

The deal was first reported by The Wall Street Journal. Industry voices have been warning about the coordination problem this partnership addresses for years — it's not a new diagnosis, just the first time a $75M check has been written against it.

• Harrison Chase, CEO of LangChain, has argued directly that orchestration and state management — not raw model quality — determine production success, framing it as the reason LangGraph exists (LangChain docs). His point maps one-to-one onto Layers 2 and 3 of the gap.

• Andrej Karpathy, former Director of AI at Tesla and founding member of OpenAI, has repeatedly framed agentic reliability as the central unsolved problem of the current cycle — see his public talks and writing via karpathy.ai.

• Demis Hassabis, CEO and co-founder of Google DeepMind, has positioned generative media as a frontier research area rather than a solved product, consistent with the partnership being framed as research (DeepMind research).

In short: the practitioners building these tools agree — coordination, not capability, is the unsolved problem.

What happens next — roadmap and predictions

2026 H2


  **First A24-Google AI-assisted production tests**

Expect experimental short-form output as the partnership operationalizes. Evidence: the deal is explicitly framed as a research partnership, not a one-off license, per WSJ.

2027


  **Orchestration becomes the default skill for creative teams**

As tools like LangGraph and MCP mature, studios will hire 'AI coordination' roles. Evidence: rapid adoption of MCP across the agent ecosystem.

2028


  **Coordination-layer IP becomes the moat**

Model capability commoditizes; the durable advantage shifts to coordination process — exactly the asset Google is buying access to now.

In short: capability commoditizes by 2028; coordination process becomes the moat.

As model capability commoditizes, the coordination layer becomes the durable competitive moat — the strategic logic behind Google's A24 bet. Source

Frequently Asked Questions

What is agentic AI technology?

Agentic AI technology refers to systems where AI models don't just respond to a single prompt but take multi-step actions toward a goal — planning, calling tools, evaluating results, and retrying. In a creative pipeline like a Google × A24 system, an agent might generate a clip, check it for drift, and decide whether to re-prompt or route to a human. Frameworks like LangGraph, AutoGen, and CrewAI implement this. The key distinction from a chatbot is autonomy across multiple steps with persistent state. The hard part isn't the agent's intelligence — it's coordinating reliable handoffs, which is exactly the AI Coordination Gap.

How does multi-agent orchestration work?

Multi-agent orchestration coordinates several specialized AI agents through a control plane that manages shared state, routing, and retries. One agent might handle scene specs, another generates video, another checks consistency. The orchestrator decides execution order, passes a shared state object between agents, and handles failures. Tools like LangGraph model this as a graph of nodes and conditional edges. The critical design principle: keep handoffs stateful. Stateless handoffs lose context and cause drift. Read more in our multi-agent systems guide.

What companies are using AI agents?

Major adopters span every sector. Google and A24 are now exploring agentic creative pipelines per the WSJ partnership report. Microsoft ships AutoGen internally and to customers; companies across finance, customer support, and software engineering use LangChain-based agents. Anthropic's Claude powers coding agents widely. For small and mid-market teams, see our breakdown of enterprise AI deployments. The common thread among successful adopters is investment in the coordination layer, not just model selection.

What is the difference between RAG and fine-tuning?

RAG (Retrieval-Augmented Generation) retrieves relevant documents from a vector database like Pinecone at query time and feeds them into the model's context — ideal for frequently changing knowledge and citing sources. Fine-tuning permanently adjusts the model's weights on your data — better for teaching style, format, or specialized behavior. RAG is cheaper to update and reduces hallucination on facts; fine-tuning is better for consistent tone (useful for a studio's brand voice). Many production systems use both: fine-tuning for behavior, RAG for knowledge. In a creative pipeline, RAG might hold the scene bible while fine-tuning enforces house style. See our RAG guide for implementation details.

How do I get started with LangGraph?

Install with pip install langgraph, then define a state schema, add nodes (each a function that reads and updates state), and connect them with edges — including conditional edges for routing, like the drift-check in this article's demo. Start with the official LangGraph documentation. Begin with a two-node graph (generate → check), confirm state flows correctly, then add conditional routing and human-in-the-loop nodes. Pair it with LangSmith for observability so you can see where handoffs fail. For ready-made patterns, explore our AI agent library and our LangGraph walkthrough.

What are the biggest AI failures to learn from?

The most instructive failures are coordination failures, not capability failures. Pipelines that looked great in a demo shipped incoherent output because handoffs were stateless and drift compounded — recall the 83% end-to-end reliability of a 6-step, 97%-per-step chain. Other classic failures: removing human review to cut costs (then shipping the 17% of bad output), over-relying on a single mega-prompt instead of decomposed steps, and skipping observability so failures are invisible until users complain. The lesson behind Google's A24 bet: capability rarely fails — coordination does. Build for the gap. See AI agents case studies for detailed post-mortems.

What is MCP in AI?

MCP (Model Context Protocol) is an open standard, introduced by Anthropic, that defines how AI models connect to external tools, data sources, and services — like a universal adapter for agents. Instead of writing custom integrations for every tool, you expose them through MCP and any compatible model can use them. See the official MCP site and Anthropic docs. For coordination-heavy systems like creative pipelines, MCP matters because it standardizes the tool-and-data handoffs that otherwise become brittle one-off integrations — directly reducing one source of the AI Coordination Gap.

About the Author

Rushil Shah

AI Systems Builder & Founder, Twarx

Rushil Shah is the founder of Twarx and an AI systems builder who has spent years designing autonomous workflows, multi-agent architectures, and AI-powered business tools. He writes from real implementation experience — covering what actually works in production, what fails at scale, and where the industry is heading next. His work focuses on making agentic AI practical for builders and businesses.

LinkedIn · Full Profile

---

This article was originally published on Twarx. Follow for daily deep dives on AI agents and automation.