Not because of Wright's Law.
Wright's Law is based on the premise that you can justify automating more of your production, the higher the volume you get. But the Model 3 was designed for the beginning for automated production. The increases of production aren't simply from "duplicating lines", they're from getting lines to run faster and more automated, as was intended from the beginning. It's extra output with proportionally very little cost added.
Which is why Tesla's COGS reduction has been way faster than Wright's Law, and can be expected to continue as such.
Clarifying note.
Here's my understanding of Wrights Law, or learning curve rate of 15%. It's based on doubling, so 100%, not 200%. And it's based on cumulative production volume, not volume per period.
To anyone citing Wright's law to predict a 1-2% COGS reduction, let me remind of the old adage...when a theory conflicts with reality, it's the theory that has to change.
Each quarter, Wright's Law would say that COGS should go down several hundred to a thousand or so dollars. Except that's not what we see. Every quarter it goes down by thousands.
Wright's Law is for mature production processes - not for fixing systems that aren't running as well as they were supposed to.
Now you don't have to agree with my reasons for why things are what they are. But the reality is what it is. And if your theory conflicts with reality, your theory needs to change.
Moore's Law vs. Wright's Law
Not sure what your point is, are you saying the Law is wrong, is not applicable, or that people are using the wrong percentage?
Wright's Law, or Learning Curve, or Experience Curve
Moore's Law vs. Wright's Law says that for every doubling of total production, the costs involved drop by the same percentage. The initial doublings correspond to the larger improvements as a new product is developed.
The 3 has gone through many doublings since introduction in July 2017. The laws also does not say what the percentage is, as it varies per industry. From
https://en.m.wikipedia.org/wiki/Experience_curve_effects Repetitive machining could as low as a 5% improvement, repetitive electrical operations as high as 25%.
Wright’s Law/Learning Curves/Experience Curves are extremely important to understand when considering technological progress and are particularly key to what Elon’s companies are trying to achieve. Wright's Law is often misunderstood however so I think it’s worth explaining some of my thoughts about it.
We have found pretty consistently over the past 100 years that as the cumulative historic production of a product doubles, the production cost reduces by a roughly fixed percentage. This is Wright’s Law and it is a rule of thumb which works over long time periods over multiple product and factory generations, but is not likely to be very accurate when making short term predictions.
Wright’s Law works because of three different underlying mechanisms:
1) R&D spend roughly scales with an industry’s revenue, so cumulative R&D spend scales with cumulative production. If you have a larger industry, more resources are spent on technological progress and these breakthroughs drive down the cost. R&D appears to be the largest driver of experience curves but it doesn’t work on short time periods because it could be several years before new R&D is put into production. It can also be quite lumpy with cost breakthroughs coming in fits and starts when a new generation of technology is introduced.
2) For every product you produce the staff and company “learn” how to do their task better. This is a combination of more productive staff and better production methods introduced after fixing the bottlenecks and problems previously experienced. In other words, to some extent all Cost of Goods Sold of a product can double up as R&D. Possibly the key driver of success in Elon’s companies is his push to maximize the R&D value of all expenditure – whether that is a COGs cost, capex cost or R&D cost. For example much of SpaceX’s R&D was done via COGs as it tested new hardware on customer missions. Similarly, Tesla’s factory is set up to rapidly upgrade car designs once lessons are learnt from the production staff and the fleet (which are both expensed as COGs). He also experiments with multiple new production techniques when investing in capex for a new factory – if this works he will use it again, if it doesn’t work he can always return to traditional process for the next factory – either way he has learnt something valuable and capex has worked as R&D.
3) Increased economies of scale. These are actually a function of annual production rather than cumulative production. These scale advantages could be higher staff productivity, more fixed cost and depreciation leverage or better purchasing power from suppliers (plus suppliers passing on their own scale savings). Economies of scale can actually cap out fairly early – you get huge benefit from 1 production line producing 500k cars vs 100k cars, but two 500k factories only has limited increased economies of scale vs one 500k factory.
There is some nuance to these mechanisms though. Experience curves only really work on unique components – when looking at a system with multiple sub components you are really looking at an average experience curve over the whole product. You can’t expect an entire EV to follow the same experience curves – some of its components have already gone through 50 years of learning in ICE cars. Most of an EV Powertrain should follow steep experience curves to some extent - this includes Battery Cells, Packs, BMS, chargers, converters, motors, inverters, cooling etc. But only for costs which are not for off the shelf components or commodities (most battery “commodities” are actually highly value add products which can themselves follow experience curves as EVs increase their cumulative production – but this can also be cancelled out in the short term by supply demand pressures if raw material production expansion is lagging behind end product capacity/demand). Many of the components supplied to Tesla are actually designed by Tesla in-house with just manufacturing outsourced – so many supplier components can also follow experience curves the same as components manufactured in-house.
Another important note on Wright’s Law is that cost reductions at times can rapidly outpace the growth in cumulative production if R&D is suddenly scaled up significantly as a % of an industry’s revenues (or even just if R&D productivity per $ is suddenly increased). This means that Tesla can still achieve significant cost reductions in many of the car components common to both EVs and ICEs. R&D in real manufacturing innovation for car production has been very low for decades as the global auto industry settled into a comfortable oligarchy with no culture of progress or innovation. This means the industry has not been learning enough from the hundreds of millions of cars it has produced over recent decades and there is a lot of slack for Tesla to apply new technology to reenergize innovation in components which are already extremely high volume. This is similar to the Boring Company (almost no R&D for 50 years has left a lot of slack) and SpaceX (corrupt cost plus pricing to lobbyists had stifled innovation in rocket development). For this reason there is some argument that even non EV unique components in Tesla cars could follow quite steep learning curves in line with Tesla’s cumulative production (rather than tracking the much smaller increases in cumulative historical car volume from the past 100 years). But these components will still likely have a much lower learning rate than for the EV component alone.