Does a Bigger Document Set Always Mean a Longer Review?

The boxes set one clock. The index sets the other.

There are two clocks on a document review. One runs on reading speed. The other runs on the index. Ask which one your last discovery schedule was written for.

Most people only hear the first one. More data, more reading, more weeks. It sounds right. It was right, for a long time, and somewhere along the way it stopped being the whole story.

The money shows it. A June 2025 review update built on ComplexDiscovery market data put review at approximately 73% of eDiscovery task spending in 2012 and 64% in 2024, with a projection of 52% by 2029. Still the largest share. But shrinking, and the same update tied the drop to AI-driven efficiencies and to investment in upstream tasks such as collection and processing.

Two terms carry this article. Issue-coding means tagging every document with the legal issues it touches. Hybrid retrieval means searching by exact keyword and by meaning in the same pass. Put both across 100% of a collection and the question changes. Not how many documents. How well they are indexed.

An EDRM commentary in August 2026 argued that discovery is quietly reorganizing itself around AI. I think that is right. But most explanations of volume and timelines still do not separate the human-only review from the AI-assisted one, and that is where the answer goes wrong. So start with the old clock, the one that still counts every page.

Why Does Volume Drive Time in Human-Only Review?

Count equals time under linear review because every search-term hit gets human eyes at an hourly rate. At one review shop in 2025, that rate had dropped from 40-50 documents to 25.

Two clocks. One runs on reading speed and the other runs on the index, and this section is about the first. Linear review is the method where a person opens each document in the queue, one after another, and codes it. To tell whether that clock is running your matter, ask three things:

1. Does a person open every document that hits on a search term?
2. Is the work staffed by the hour or priced by the document?
3. Has culling already pulled the easy documents out?

Three yeses, and the count is your calendar.

Conventional wisdom says more documents means more weeks. It is right, but only here, only in the queue. A 2022 Nextpoint post on JD Supra called review "the most time consuming, error prone, and costly part of ediscovery," and it said documents that hit on search terms must be reviewed. Must be. So the hits pile up and somebody reads one. Then the next one.

The rate is the ceiling. A vendor employee writing on r/ediscovery in 2025 said the shop's in-person teams once ran "consistently above 40-50 docs/hr." By 2025, with 90%+ remote reviewers, the shop was "fortunate to achieve 25 docs/hr and ecstatic when we hit 30." A buyer in the same thread gave a reason that had nothing to do with effort: threading, tighter search terms and privilege screens strip out the easy documents, so what is left reads slow.

That is the part I keep going back to. So when a vendor quotes a price per document, ask what reading time per document sits under that price. Per-document pricing in that 2025 thread assumed about 2 minutes a document, even for a 70 page email, and a 70 page email cannot be read in 2 minutes.

Across the 5 sources behind this section, the pattern is the same: where a person opens each file, hours follow the count. A solo practitioner wrote in 2025 that reviewing a set alone "would take an absurd amount of time." What the queue buys is a defined finish date. Defined because it is arithmetic, documents over rate. For a small firm, the schedule is set the day the hit count comes back. Add documents and you add hours. Nothing else moves.

So the rule is real. It is also narrow. Before you plan around it, work the sum for your own matter: hits, over the rate, over the number of readers you can pay for. The machine brings its own questions, including whether a planted document can fool an AI review tool. At Relevant e-Discovery, our proof is a demo: bring a messy collection and a hard question, then watch it read, code, and cite your own evidence instead of reading a case study about someone else's. That is where the second clock starts.

What Still Slows an AI-Assisted Review as the Collection Grows?

Three things do not shrink with better indexing: files a machine cannot read, the attorney's relevance call and sign-off, and the validation a court can ask about.

The objection is fair. Take the reading away and something else fills the time. It always does. So here is what fills it, piece by piece.

1. Unreadable files. OCR, optical character recognition, is the software that turns a picture of a page into searchable text. Where it fails, a person reads. Wider search terms sweep in more of those files.
2. The attorney's call. An attorney still decides which documents answer the request, and a licensed attorney still signs off that the process meets the standards of the jurisdiction. The tool signs nothing. Practitioners point to documented cases of attorneys disciplined for an AI's errors.
3. The check. The ailegalauthority.com guide to AI in eDiscovery says it flat: "TAR reduces the volume of documents requiring human review; it does not eliminate it." It lists a statistically validated recall estimate and elusion testing, which means sampling the documents nobody reviewed to see what was missed.

The check gets harder with the newer tools, not easier. In one 2024 experiment, generative AI found 96% of the relevant documents where human reviewers found 54%. In that 2024 experiment, its precision, the share of flagged documents that were truly relevant, was 60% against the humans' 91%. More found. More junk swept in with it, and somebody pays to sort that. The same guide warns that an LLM's errors may not be spread evenly and are harder to catch with standard elusion sampling, which is the reason to read up on where AI relevance scores come from and what they hide.

Then the part that bothers me more. A commenter in the 2024 r/ediscovery thread "Are Reviewers Done?" ran a hypothetical: if defending a case was viable at effort X and technology cuts that effort by 75%, courts may tolerate wider search terms that bring it back to ".5-.8 X." The same commenter said the wider terms pick up more "noOCR-able docs" requiring human review. So before you agree to wider terms, ask how many of the new hits have no readable text. Rule 26(b)(1) sets the proportionality standard, and a lighter burden makes a thinner argument for a narrow scope.

A candid note, and then back. That range is one practitioner's guess, not a measurement, and nothing on record here settles whether courts have in fact widened scope as review got cheaper. It could go the other way. Nobody knows yet.

What is fixed is this. People stay at every step: defining the collection, negotiating custodians, date ranges, file types and keyword filters, then certifying the result. A hands-off process was described in 2024 as a long way out, and I would not bet against that.

In practice, a bigger set adds exception handling and checking, not a matching block of reading hours. The first item grows with the share of bad files, not with the total. The other two are work somebody has to schedule either way. So the floor is not zero. It is just not the count, and that leaves the practical question for a small firm holding a big, messy set and a date that will not move.

How Can a Small Firm Cut Review Cost and Time on a Large, Messy Collection?

Scope by what the files are, not how many. AI-assisted review runs cents per document, against dollars per document and roughly $19K/GB for manual review.

Verification, privilege, defensibility. Those were the real time costs. So they become the questions you ask before the matter is scoped and before anything is bought. Run these five on one matter:

1. Estimate by composition, not count. What share of the files can a machine not read? How thick does privilege run? How many issues are in play?
2. Ask whether every answer links back to its source. If it does not, you are checking summaries, and checking summaries is reading all over again.
3. Ask where privileged material is processed. Who holds the keys, who keeps a copy, and whether anything trains a model.
4. Ask what record the process leaves. If opposing counsel challenges the method, something has to be there to show.
5. Ask how exceptions are priced. The files that fail are where the hours hide.

I would start with the first one and stay there a while. The share of unreadable files is settled at the document processing stage, before anyone reviews anything, and it is the one input that still grows with the set. Count tells you nothing about it. A quiet set of clean email is one job. A smaller set of scans and tangled privilege is another, and it can be the longer one.

On price, the outside numbers point the same way. The Winter 2025 eDiscovery Pricing Survey, run by ComplexDiscovery and EDRM, found GenAI-assisted review most often quoted at $0.26 to $0.50 per document, while remote traditional review split between $0.50 to $1.00 and above $1.00. The same 2025 survey found about 50% of respondents were uncertain how special processing and exception handling were priced in GenAI-assisted review. About half of those asked could not say. That is question five, and it is why you ask it out loud.

The figure in the opening line is Relevant e-Discovery's own, and the company's position is that the economics are demonstrable, not asserted: a delta the buyer can see on their own matter. Two more of our answers map straight onto the list. Outputs link back to the exact exhibit they came from, so an attorney can verify in one click before filing, which is the structural answer to the Mata v. Avianca sanctions risk. And processing runs single-tenant, or inside your own AWS account under your own keys, with no vendor retention and no model training.

A source link turns verification from reading again into checking. Your own keys take the privilege argument off the schedule. That is how a small firm manages a large volume of electronic documents under deadline pressure: the first days go to the index, not the queue.

One thing I cannot hand you is a chart of time-to-first-fact against volume across matters. That data is not on record. Collected, it would show whether the first cited answer arrives at about the same point on a small set and a large one, and until it exists the honest test is your own collection and your own hard question.

So Does a Bigger Document Set Mean a Longer Review?

Not by itself. Under human-only review, count still sets the calendar. Under full issue-coding and hybrid retrieval, the calendar follows how well the record is indexed.

Two clocks, still. The old one has not gone anywhere. A 2025 projection still had review as the largest slice of eDiscovery spending in 2029, at 52%. So people will keep reading. Just less of the pile, and later in the job.

What moves is where the time gets decided. Lenora Gray, a data scientist at Redgrave Data, said on the Emerging Litigation Podcast in 2024 that "the machine isn't going to do everything," and that negotiating custodians, date ranges, file types, and keyword filters is "supremely important." I read that as a schedule. The weeks get set before anyone opens a document.

One comparison I cannot give you: index quality set against review time, matter by matter. It is not on record. When it is collected, I expect it to show what this piece leans on, that a clean index shortens the wait for the first fact and a bad one stretches it at any size. Until then, treat that as my reading and not as a measurement.

So on your next matter, ask one thing before you ask how many documents there are. Ask what shape the index is in on day one.