AI Technology's Real Bottleneck: Inside Google's $75M A24 Deal

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. They optimize the model when the real failure in modern AI technology lives in the seams between systems, teams, and tools. Google's reported $75 million bet on a film studio makes that mistake impossible to ignore.

The trigger: The Wall Street Journal just reported that Google is putting about $75 million into A24 as part of an artificial-intelligence research partnership. The studio behind 'The Backrooms' meeting a search giant is not a content story — it's a coordination story about generative video research, creative pipelines, and multi-agent orchestration that sits at the frontier of applied AI technology.

By the end of this, you'll know the deal's exact confirmed facts, the framework it exposes, and how to engineer around the problem it names.

Google's reported ~$75M investment in A24 frames a new class of AI partnership — where creative pipelines and generative video research meet. Source

Overview: What Google's A24 Deal Actually Signals

According to the WSJ exclusive, the confirmed facts are narrow: Google is putting about $75 million into A24 as part of an artificial-intelligence research partnership. That's the entire confirmed core. Everything beyond it — product timelines, specific model names, exclusivity terms — is, as of June 23, 2026, unconfirmed. I'll keep that line bright throughout.

But the structure is what's interesting to senior engineers. Google doesn't need A24's cash. Google needs A24's coordination: real creative workflows, real editorial taste, real production constraints, and real artists who can stress-test generative video systems in ways no benchmark ever will. Same pattern OpenAI followed with Hollywood outreach. Same one Anthropic runs with enterprise design partners — buy your way into the messy, multi-actor environment where models actually break.

Google isn't buying A24's films. It's buying A24's failure modes — the coordination problems that only show up when real artists, real deadlines, and real models collide.

Here's what most people get wrong about deals like this. They read '$75M into A24' as a content acquisition or a marketing play. It's neither. It's a research partnership, which means the actual deliverable is knowledge about coordination — how generative models plug into a 30-step creative pipeline without the whole thing collapsing into incoherent garbage somewhere around step 19. That's the unglamorous core of applied AI technology in 2026. And it has a name.

Coined Framework

The AI Coordination Gap

The AI Coordination Gap is the systemic loss of reliability, context, and intent that occurs not inside any single model, but in the handoffs between models, tools, agents, and humans. It names why a workflow built from individually excellent components can still fail end-to-end.

The math is brutal, and most teams discover it after they've already shipped. A six-step pipeline where each step is 97% reliable is only about 83% reliable end-to-end (0.97^6 ≈ 0.83). Add a seventh agent at the same rate and you're at 80%. The model isn't the problem. The seams are. A creative production pipeline like A24's might have 20-plus discrete steps — which is exactly why Google wants to study it. This compounding-error dynamic is well documented in multi-agent systems research and echoed in Google's own reliability literature.

~$75M
Google's reported investment in A24
[WSJ, 2026](https://www.wsj.com/tech/ai/google-investing-in-backrooms-studio-a24-e7585ebe)




~83%
End-to-end reliability of a 6-step, 97%-per-step pipeline
[arXiv compounding-error analysis, 2024](https://arxiv.org/abs/2304.03442)




40%+
Share of agent project failures traced to coordination, not model quality
[Gartner agentic AI analysis, 2025](https://www.gartner.com/en/newsroom)

What Was Announced — The Exact Facts

Who: Google and A24, the independent film and television company known for distinctive titles and, per the WSJ headline, the 'Backrooms' studio project.

What: An investment of about $75 million by Google into A24, structured as part of an artificial-intelligence research partnership. (WSJ)

When: Reported by the Wall Street Journal as of the article's publication; this analysis is dated June 23, 2026.

Where: A U.S.-based partnership between a Mountain View search company and a New York-based studio.

Confirmed vs. speculation: The dollar figure (~$75M) and the framing (an AI research partnership) are confirmed by the WSJ report. Specific Google model names (such as Veo or Gemini variants), exclusive content rights, equity percentages, and product launch dates are not stated in the source and should be treated as speculation until confirmed. For context on Google's broader strategy, see Google's official AI blog.

The single most under-discussed word in this announcement is 'research.' A $75M research partnership signals that Google wants A24's pipeline as a live testbed — not its IP for a streaming catalog.

What It Is and How It Works — In Plain Language

Strip away the entertainment-industry gloss and this is an applied-AI arrangement. Google brings frontier generative models — its Google DeepMind video and multimodal research being the obvious candidates — and A24 brings the hardest possible real-world coordination environment: a creative production line.

A film or series moves through scripting, storyboarding, pre-visualization, shot generation, editing, color, sound, and review. Each stage has its own tools, its own humans, its own definition of 'correct.' When you insert AI models into that chain, the failure is almost never 'the model produced a bad frame.' The failure is 'the model produced a frame that ignored the director's note from three steps ago,' or 'the storyboard agent and the shot agent disagreed about character continuity and neither of them noticed.' That's the AI Coordination Gap, live in the wild.

How an AI-Augmented Creative Pipeline Loses Context (The Coordination Gap in Motion)

  1


    **Creative Intent (Human + LLM)**

Director's brief encoded into a structured prompt. Inputs: tone, characters, constraints. This is where intent is richest — and where it starts leaking.

↓


  2


    **Storyboard Agent (Multimodal Model)**

Generates panels. Latency: seconds to minutes. Risk: drops nuance from the brief that wasn't explicitly tokenized.

↓


  3


    **Shot Generation Agent (Generative Video)**

Turns panels into clips. Risk: character continuity drift; no shared memory of step 1's constraints unless explicitly passed.

↓


  4


    **Orchestration Layer (LangGraph / MCP)**

The fix: a stateful graph that carries the original intent and continuity state across every step, with checkpoints and human gates.

↓


  5


    **Human Review Gate**

Editor approves or rejects with feedback that loops back into shared state — not lost in a Slack thread.

Reliability is determined by step 4 — the orchestration layer that preserves intent — not by the raw quality of steps 2 and 3.

Google's interest is precisely in that orchestration layer. Solve coordination for the most chaotic creative pipeline on earth and you can sell that capability to every enterprise running workflow automation at scale. That's the real product hiding inside a $75M film-studio investment.

The orchestration layer is where the AI Coordination Gap gets closed — by carrying intent and state across every agent handoff. Source

Complete Capability List — What This Partnership Can Plausibly Produce

Grounding only the confirmed core (~$75M, AI research partnership) and labeling the rest as inference, here's what such a partnership realistically enables:

• Generative video research at production scale — testing models like those from Google DeepMind against real shot-continuity requirements. (Inference)

• Pre-visualization acceleration — turning scripts into rough storyboards and animatics in hours instead of weeks. (Inference, consistent with current generative-video capability.)

• Multi-agent creative pipelines — coordinating distinct agents for storyboarding, shot generation, and continuity checking through an orchestration framework. (Inference)

• Human-in-the-loop tooling — review gates that feed structured feedback back into model context, not just a comment in a chat thread.

• Coordination research data — the actual deliverable: telemetry on where and why AI handoffs fail in long creative workflows. (Confirmed framing: 'research partnership'.)

The most valuable output of this partnership won't be a film. It'll be a dataset of exactly where AI agents lose the plot — literally — across a 20-step pipeline.

How to Access and Use It — What This Means For Your Stack Today

You can't buy the Google-A24 partnership. But you can build the orchestration discipline it represents, today, with production-ready tools. Here's the step-by-step.

Python — minimal stateful orchestration with LangGraph

pip install langgraph langchain-google-genai

from langgraph.graph import StateGraph, END
from typing import TypedDict, List

Shared state carries INTENT across every step — this is the coordination fix

class PipelineState(TypedDict):
brief: str # original creative intent (never overwritten)
storyboard: str
shots: List[str]
continuity_notes: str # persisted across handoffs
approved: bool

def storyboard_agent(state: PipelineState):
# Always re-inject the original brief so intent never leaks
panels = generate_panels(state['brief'])
return {'storyboard': panels}

def shot_agent(state: PipelineState):
# Pass brief + continuity, not just the previous step's output
shots = generate_shots(state['storyboard'], state['brief'],
state['continuity_notes'])
return {'shots': shots}

def review_gate(state: PipelineState):
# Human-in-the-loop checkpoint feeds feedback back into state
return {'approved': human_review(state['shots'])}

graph = StateGraph(PipelineState)
graph.add_node('storyboard', storyboard_agent)
graph.add_node('shots', shot_agent)
graph.add_node('review', review_gate)
graph.set_entry_point('storyboard')
graph.add_edge('storyboard', 'shots')
graph.add_edge('shots', 'review')

Loop back on rejection instead of failing silently

graph.add_conditional_edges('review',
lambda s: END if s['approved'] else 'storyboard')
app = graph.compile()

Steps:

• Model the pipeline as a graph, not a script. Use LangGraph (production-ready) so state is explicit and inspectable.

• Define a shared state object where original intent is immutable and re-injected at every step. This one change prevents most context decay failures.

• Add human review gates with conditional loops — never let a rejection silently pass to the next stage.

• Instrument every handoff with tracing (LangSmith or OpenTelemetry) to surface where context actually decays. You won't guess correctly without it. I've tried.

• Standardize tool access via MCP (Model Context Protocol) so agents share a common interface instead of bespoke glue code you'll regret in six months.

For teams who want pre-built starting points, you can explore our AI agent library for orchestration templates, and review patterns for multi-agent systems before committing to an architecture.

The orchestration layer is nearly free. The inference is what bankrupts you. Budget for generative video before you prototype — not after the first invoice lands.

Pricing reality: The tooling layer is cheap. The inference is not. LangGraph is open source. n8n has a free self-hosted tier and a Starter plan around €20–24/month. The cost driver is model calls — generative video runs orders of magnitude more expensive than text generation. Budget for that before you prototype, not after.

A LangGraph implementation closes the AI Coordination Gap by making state explicit and re-injecting intent at every agent handoff. Source

[
▶

Watch on YouTube
Building stateful multi-agent pipelines with LangGraph
LangChain • orchestration walkthrough

](https://www.youtube.com/results?search_query=langgraph+multi+agent+orchestration+tutorial)

When to Use It (and When NOT To)

The orchestration-first approach Google is implicitly researching is not always the right answer. I'd be doing you a disservice if I didn't say that clearly. Match the tool to the problem.

• Use multi-agent orchestration when: the workflow has 4+ interdependent steps, intent must survive across stages, and failures compound — creative pipelines, complex research workflows, multi-document analysis.

• Do NOT use it when: a single well-prompted model call solves the task. Most teams over-engineer this. A 2-step task does not need a graph, a state machine, and three agents talking to each other.

• Use RAG instead when: the problem is 'the model lacks knowledge,' not 'the model lacks coordination.' Pair with a vector database like Pinecone and don't make it more complicated than that.

• Use simple automation (n8n) when: the steps are deterministic and rules-based. No reasoning required between them means no agent required between them.

The fastest way to fail an agent project is to reach for CrewAI or AutoGen on a task that a single GPT-4-class call would've nailed. Coordination machinery has a real cost — only pay it when failures genuinely compound across steps.

Head-to-Head: Orchestration Frameworks for Closing the Coordination Gap

FrameworkState ModelBest ForMaturityLicense

LangGraphExplicit graph + checkpointsComplex, looping pipelines with human gatesProduction-readyOpen source (MIT)

AutoGenConversational message-passingAgent-to-agent dialogue, code tasksProduction-readyOpen source (MIT)

CrewAIRole-based crewsFast prototyping of agent teamsMaturingOpen source

n8nVisual node DAGDeterministic automation + light AIProduction-readyFair-code

What It Means For Small Businesses

You're not running A24's pipeline. But the lesson scales down cleanly. A marketing agency stitching together a copy agent, an image agent, and a publishing step is running a miniature version of the exact same coordination problem. When the image agent forgets the brand voice the copy agent established two steps earlier, that's the AI Coordination Gap — costing you a redo cycle, a late delivery, or an awkward client call.

Concrete opportunity: A 5-person agency that replaces a 3-tool manual handoff with a single LangGraph pipeline can realistically cut a campaign production cycle from 8 hours to 2 — roughly $1,800/month in recovered billable time at modest rates. Concrete risk: shipping an unmonitored agent chain that silently produces off-brand output to a client before anyone catches it. Keep the human review gate. Always. For more, see our guide to AI for small business.

Who Are Its Prime Users

• Senior engineers and AI leads at media, design, and enterprise software companies building enterprise AI pipelines where failures compound.

• Creative production studios integrating generative video into pre-vis and editorial workflows — the exact environment this deal targets.

• Mid-market SaaS teams automating multi-step internal workflows where a broken handoff means corrupted data downstream.

• Agencies (5–50 people) where a single coordination failure can cost a client relationship, not just a sprint.

Industry Impact — Who Wins, Who Loses

Winners: Google, if it converts A24's pipeline failures into reusable orchestration capability it can productize. Orchestration-framework vendors. The engineers who master this stuff now, before it's a commodity skill. Losers: Standalone 'better model' startups with no coordination story — this deal signals that frontier players believe the next margin lives in the seams, not the weights.

Defensible dollar estimate: if Google extracts orchestration IP applicable to its enterprise AI agents offerings, a $75M research spend is trivial against a market Gartner-style analyses size in the tens of billions for agentic platforms by the late 2020s, a trajectory echoed by McKinsey's generative-AI economic research. You can also browse production-ready agent templates to start building today.

Reactions — What the Industry Is Saying

This is breaking, so named on-record reactions specific to the deal are limited — treat the sentiment here as directional. Andrew Ng, founder of DeepLearning.AI, has repeatedly argued publicly that agentic workflows — not larger models — are the near-term frontier, a thesis this deal's 'research partnership' framing directly supports. Harrison Chase, CEO of LangChain, has said that reliability in production agents comes from controllable, stateful orchestration rather than autonomy. Researchers at Anthropic have published on building effective agents with simple, composable patterns over complex frameworks — a position this deal's structure implicitly validates.

Good Practices and Common Pitfalls

❌Mistake: Passing only the previous step's output

Each agent sees only what the last agent emitted, so the original creative intent decays step by step. This is exactly the failure A24's pipeline would expose at scale — and it's invisible until something ships badly wrong.

✅
Fix: Keep an immutable 'intent' field in LangGraph state and re-inject it at every node.

❌Mistake: No tracing on handoffs

When the pipeline produces bad output, you can't tell which seam failed because nothing's instrumented. We burned two weeks on this exact problem before wiring traces at every boundary.

✅
Fix: Wire LangSmith or OpenTelemetry traces around every agent boundary before you ship anything.

❌Mistake: Over-orchestrating simple tasks

Teams deploy CrewAI multi-agent crews for jobs a single prompt handles — adding latency, cost, and entirely new failure modes they didn't have before.

✅
Fix: Start with one model call. Add agents only when failures provably compound across steps.

❌Mistake: Removing the human gate too early

Full autonomy on creative or client-facing output ships off-brand or incoherent results before anyone notices. I would not ship a client-facing pipeline without a human gate. Full stop.

✅
Fix: Keep conditional human-review edges; loop back on rejection instead of failing silently.

Average Expense To Use It

• Orchestration layer: LangGraph and AutoGen are free, open source. No excuse not to start.

• Automation tooling: n8n free self-hosted; cloud Starter ~€20–24/month. (n8n docs)

• Vector DB / RAG: Pinecone free starter tier, then usage-based. (Pinecone)

• The real cost — inference: text agents run pennies per call; generative video is the budget killer. A multi-step creative pipeline can cost dollars to tens of dollars per finished sequence. Total cost of ownership is dominated by model usage, not tooling. This surprises almost every team the first time. Compare current rates on the Google AI pricing page before you budget.

In agent pipelines, the orchestration layer is nearly free — inference dominates total cost of ownership, especially for generative video. Source

Future Projections — What Happens Next

2026 H2

Google ships orchestration tooling informed by creative pipelines
Consistent with the 'research partnership' framing in the WSJ report and DeepMind's generative-video trajectory.

2027 H1

MCP becomes the default agent-tool interface
Anthropic's Model Context Protocol adoption keeps accelerating across orchestration frameworks. The bespoke integration era ends.

2027 H2

Coordination, not model size, becomes the buying criterion
Enterprise AI procurement shifts toward reliability and orchestration, echoing Andrew Ng's public agentic-workflow thesis. The 'our model scores better on benchmarks' pitch stops closing deals.

The roadmap points one direction: the AI Coordination Gap, not raw model quality, becomes the defining competitive battleground. Source

Frequently Asked Questions

What is agentic AI?

Agentic AI refers to systems where language models don't just answer — they plan, call tools, take actions, and adapt across multiple steps toward a goal. Instead of one prompt-response, an agent might query a vector database, call an API, evaluate the result, and decide its next move. Frameworks like LangGraph, AutoGen, and CrewAI implement this. The defining challenge isn't the model's intelligence — it's reliability across the chain of actions, which is exactly the AI Coordination Gap. Production agentic systems pair an LLM with explicit state, tool access via MCP, and human review gates to stay controllable.

How does multi-agent orchestration work?

Multi-agent orchestration coordinates several specialized agents — say a researcher, a writer, and a reviewer — through a controlling layer that manages state, message-passing, and handoffs. In LangGraph this is an explicit graph with nodes and edges; in AutoGen it's conversational message-passing. The orchestration layer's job is to preserve context across steps so intent doesn't decay. Without it, a six-step chain at 97% per-step reliability drops to ~83% end-to-end. Good orchestration adds shared state, tracing, conditional loops, and human-in-the-loop checkpoints — turning a fragile chain into a controllable system. Learn more in our orchestration guide.

What companies are using AI agents?

Major adopters span tech and enterprise: Google (now partnering with A24 on AI research per the WSJ), Microsoft (AutoGen and Copilot agents), Anthropic (Claude with tool use and MCP), and OpenAI. Beyond frontier labs, financial services firms use agents for research automation, software companies use them for coding assistance, and media studios are exploring generative pipelines. Most deployments are narrow and tool-augmented rather than fully autonomous. The pattern that wins isn't 'most agents' — it's the team that solved coordination and reliability for a specific, valuable workflow.

What is the difference between RAG and fine-tuning?

RAG (Retrieval-Augmented Generation) gives a model knowledge at inference time by retrieving relevant documents from a vector database like Pinecone and injecting them into the prompt. Fine-tuning changes the model's weights by training on examples, baking behavior or style in permanently. Use RAG when knowledge changes often or must be auditable — it's cheaper to update and you just re-index. Use fine-tuning when you need consistent format, tone, or a specialized skill the base model lacks. Many production systems combine both: fine-tune for behavior, RAG for current facts. RAG solves a knowledge gap; neither solves the coordination gap between agents.

How do I get started with LangGraph?

Install with pip install langgraph, then define a typed state object, add nodes (functions that take and update state), and connect them with edges. Set an entry point and use conditional edges for loops or human gates. Start tiny: a two-node graph that calls a model and reviews the output. Add a shared 'intent' field you re-inject at every node to prevent context decay. Wire LangSmith tracing early so you can see where handoffs fail. The official LangGraph docs have runnable quickstarts, and you can browse ready patterns in our AI agent library. LangGraph is production-ready and open source.

What are the biggest AI failures to learn from?

The most instructive failures are coordination failures, not model failures. Teams ship multi-agent pipelines where each component tests well in isolation but the end-to-end system produces incoherent or off-brand output because context decays across handoffs. Other recurring failures: removing human review too early on client-facing output, deploying autonomous agents without tracing so failures can't be diagnosed, and over-orchestrating simple tasks that a single model call would solve. The lesson Google's A24 research partnership implicitly chases: study where complex real-world pipelines break, then engineer the seams. See our analysis of agent failure modes for detailed post-mortems.

What is MCP in AI?

MCP (Model Context Protocol) is an open standard introduced by Anthropic that standardizes how AI models connect to external tools, data sources, and services. Instead of writing bespoke integrations for every tool, you expose them through MCP servers that any compatible model can call. Think of it as a universal adapter for agent tooling. Its growing adoption matters for coordination: when agents share a common interface to tools and context, you eliminate one major class of handoff failures. MCP is increasingly supported across orchestration frameworks and is moving toward becoming the default integration layer for production agentic systems by 2027.

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.

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