at a serious automotive producer, watching engineers have a good time what they thought was a breakthrough. They’d used generative AI to optimize a suspension part: 40% weight discount whereas sustaining structural integrity, accomplished in hours as a substitute of the same old months. The room buzzed with pleasure about effectivity beneficial properties and value financial savings.
However one thing bothered me. We have been utilizing expertise that might reimagine transportation from scratch, and as a substitute, we have been making barely higher variations of elements we’ve been manufacturing because the Nineteen Fifties. It felt like utilizing a supercomputer to stability your checkbook: technically spectacular, however lacking the purpose solely.
After spending three years serving to automotive corporations deploy AI options, I’ve observed this sample in every single place. The business is making a basic mistake: treating generative AI as an optimization instrument when it’s truly a reimagination engine. And this misunderstanding may cost conventional automakers their future.
Why This Issues Now
The automotive business stands at an inflection level. Electrical automobiles have eliminated the central constraint that formed automotive design for a century—the inner combustion engine. But most producers are nonetheless designing EVs as if they should accommodate a giant steel block underneath the hood. They’re utilizing AI to make these outdated designs marginally higher, whereas a handful of corporations are utilizing the identical expertise to ask whether or not vehicles ought to seem like vehicles in any respect.
This isn’t nearly expertise; it’s about survival. The businesses that determine this out will dominate the subsequent period of transportation. Those who don’t will be part of Kodak and Nokia within the museum of disrupted industries.
The Optimization Entice: How We Acquired Right here
What Optimization Appears Like in Apply
In my consulting work, I see the identical deployment sample at virtually each automotive producer. A group identifies a part that’s costly or heavy. They feed current designs right into a generative AI system with clear constraints: cut back weight by X%, preserve power necessities, keep inside present manufacturing tolerances. The AI delivers, everybody celebrates the ROI, and the mission will get marked as a hit.
Right here’s precise code from a standard optimization strategy I’ve seen carried out:
from scipy.optimize import decrease
import numpy as np
def optimize_component(design_params):
"""
Conventional strategy: optimize inside assumed constraints
Drawback: We're accepting current design paradigms
"""
thickness, width, top, material_density = design_params
# Reduce weight
weight = thickness * width * top * material_density
# Constraints primarily based on present manufacturing
constraints = [
{'type': 'ineq', 'fun': lambda x: x[0] * x[1] * 1000 - 50000},
{'sort': 'ineq', 'enjoyable': lambda x: x[0] - 0.002}
]
# Bounds from current manufacturing capabilities
bounds = [(0.002, 0.01), (0.1, 0.5), (0.1, 0.5), (2700, 7800)]
outcome = decrease(
lambda x: x[0] * x[1] * x[2] * x[3], # weight operate
[0.005, 0.3, 0.3, 7800],
methodology='SLSQP',
bounds=bounds,
constraints=constraints
)
return outcome # Yields 10-20% enchancment
# Instance utilization
initial_design = [0.005, 0.3, 0.3, 7800] # thickness, width, top, density
optimized = optimize_component(initial_design)
print(f"Weight discount: {(1 - optimized.enjoyable / (0.005*0.3*0.3*7800)) * 100:.1f}%")This strategy works. It delivers measurable enhancements — usually 10-20% weight discount, 15% value financial savings, that type of factor. CFOs adore it as a result of the ROI is evident and rapid. However have a look at what we’re doing: we’re optimizing inside constraints that assume the present design paradigm is appropriate.
The Hidden Assumptions
Each optimization embeds assumptions. If you optimize a battery enclosure, you’re assuming batteries must be enclosed in separate housings. If you optimize a dashboard, you’re assuming automobiles want dashboards. If you optimize a suspension part, you’re assuming the suspension structure itself is appropriate.
Common Motors introduced final 12 months they’re utilizing generative AI to revamp automobile elements, projecting 50% discount in improvement time. Ford is doing related work. So is Volkswagen. These are actual enhancements that can save tens of millions of {dollars}. I’m not dismissing that worth.
However right here’s what retains me up at evening: whereas conventional producers are optimizing their current architectures, Chinese language EV producers like BYD, which surpassed Tesla in world EV gross sales in 2023, are utilizing the identical expertise to query whether or not these architectures ought to exist in any respect.
Why Sensible Folks Fall into This Entice
The optimization entice isn’t about lack of intelligence or imaginative and prescient. It’s about organizational incentives. If you’re a public firm with quarterly earnings calls, it’s good to present outcomes. Optimization delivers measurable, predictable enhancements. Reimagination is messy, costly, and may not work.
I’ve sat in conferences the place engineers offered AI-generated designs that might cut back manufacturing prices by 30%, solely to have them rejected as a result of they’d require retooling manufacturing strains. The CFO does the maths: $500 million to retool for a 30% value discount that takes 5 years to pay again, versus $5 million for optimization that delivers 15% financial savings instantly. The optimization wins each time.
That is rational decision-making inside current constraints. It’s additionally the way you get disrupted.
What Reimagination Really Appears Like
The Technical Distinction
Let me present you what I imply by reimagination. Right here’s a generative design strategy that explores the complete risk area as a substitute of optimizing inside constraints:
import torch
import torch.nn as nn
import numpy as np
class GenerativeDesignVAE(nn.Module):
"""
Reimagination strategy: discover whole design area
Key distinction: No assumed constraints on type
"""
def __init__(self, latent_dim=128, design_resolution=32):
tremendous().__init__()
self.design_dim = design_resolution ** 3 # 3D voxel area
# Encoder learns to characterize ANY legitimate design
self.encoder = nn.Sequential(
nn.Linear(self.design_dim, 512),
nn.ReLU(),
nn.Linear(512, latent_dim * 2)
)
# Decoder generates novel configurations
self.decoder = nn.Sequential(
nn.Linear(latent_dim, 512),
nn.ReLU(),
nn.Linear(512, self.design_dim),
nn.Sigmoid()
)
def reparameterize(self, mu, logvar):
"""VAE reparameterization trick"""
std = torch.exp(0.5 * logvar)
eps = torch.randn_like(std)
return mu + eps * std
def ahead(self, x):
"""Encode and decode design"""
h = self.encoder(x)
mu, logvar = h.chunk(2, dim=-1)
z = self.reparameterize(mu, logvar)
return self.decoder(z), mu, logvar
def generate_novel_designs(self, num_samples=1000):
"""Pattern latent area to discover potentialities"""
with torch.no_grad():
z = torch.randn(num_samples, 128)
designs = self.decoder(z)
return designs.reshape(num_samples, 32, 32, 32)
def calculate_structural_integrity(design):
"""
Simplified finite aspect evaluation approximation
In manufacturing, this might interface with ANSYS or related FEA software program
"""
# Convert voxel design to emphasize distribution
design_np = design.cpu().numpy()
# Simulate load factors (simplified)
load_points = np.array([[16, 16, 0], [16, 16, 31]]) # high and backside
# Calculate materials distribution effectivity
material_volume = design_np.sum()
# Approximate structural rating primarily based on materials placement
# Greater rating = higher load distribution
stress_score = 0
for level in load_points:
x, y, z = level
# Examine materials density in load-bearing areas
local_density = design_np[max(0,x-2):x+3,
max(0,y-2):y+3,
max(0,z-2):z+3].imply()
stress_score += local_density
# Normalize by quantity (reward environment friendly materials use)
if material_volume > 0:
return stress_score / (material_volume / design_np.measurement)
return 0
def calculate_drag_coefficient(design):
"""
Simplified CFD approximation
Actual implementation would use OpenFOAM or related CFD instruments
"""
design_np = design.cpu().numpy()
# Calculate frontal space (simplified as YZ aircraft projection)
frontal_area = design_np[:, :, 0].sum()
# Calculate form smoothness (gradient-based)
# Smoother shapes = decrease drag
gradients = np.gradient(design_np.astype(float))
smoothness = 1.0 / (1.0 + np.imply([np.abs(g).mean() for g in gradients]))
# Approximate drag coefficient (decrease is healthier)
# Actual Cd ranges from ~0.2 (very aerodynamic) to 0.4+ (boxy)
base_drag = 0.35
drag_coefficient = base_drag * (1.0 - smoothness * 0.3)
return drag_coefficient
def assess_production_feasibility(design):
"""
Consider how simply this design may be manufactured
Considers elements like overhangs, inside voids, assist necessities
"""
design_np = design.cpu().numpy()
# Examine for overhangs (tougher to fabricate)
overhangs = 0
for z in vary(1, design_np.form[2]):
# Materials current at degree z however not at z-1
overhang_mask = (design_np[:, :, z] > 0.5) & (design_np[:, :, z-1] < 0.5)
overhangs += overhang_mask.sum()
# Examine for inside voids (tougher to fabricate)
# Simplified: depend remoted empty areas surrounded by materials
internal_voids = 0
for x in vary(1, design_np.form[0]-1):
for y in vary(1, design_np.form[1]-1):
for z in vary(1, design_np.form[2]-1):
if design_np[x,y,z] < 0.5: # empty voxel
# Examine if surrounded by materials
neighbors = design_np[x-1:x+2, y-1:y+2, z-1:z+2]
if neighbors.imply() > 0.6: # principally surrounded
internal_voids += 1
# Rating from 0 to 1 (larger = simpler to fabricate)
total_voxels = design_np.measurement
feasibility = 1.0 - (overhangs + internal_voids) / total_voxels
return max(0, feasibility)
def calculate_multi_objective_reward(physics_scores):
"""
Pareto optimization throughout a number of targets
Steadiness weight, power, aerodynamics, and manufacturability
"""
weights = {
'weight': 0.25, # 25% - decrease materials
'power': 0.35, # 35% - maximize structural integrity
'aero': 0.25, # 25% - decrease drag
'manufacturability': 0.15 # 15% - ease of manufacturing
}
# Normalize every rating to 0-1 vary
normalized_scores = {}
for key in physics_scores[0].keys():
values = [score[key] for rating in physics_scores]
min_val, max_val = min(values), max(values)
if max_val > min_val:
normalized_scores[key] = [
(v - min_val) / (max_val - min_val) for v in values
]
else:
normalized_scores[key] = [0.5] * len(values)
# Calculate weighted reward for every design
rewards = []
for i in vary(len(physics_scores)):
reward = sum(
weights[key] * normalized_scores[key][i]
for key in weights.keys()
)
rewards.append(reward)
return torch.tensor(rewards)
def evaluate_physics(design, targets=['weight', 'strength', 'aero']):
"""
Consider towards a number of targets concurrently
That is the place AI finds non-obvious options
"""
scores = {}
scores['weight'] = -design.sum().merchandise() # Reduce quantity (adverse for minimization)
scores['strength'] = calculate_structural_integrity(design)
scores['aero'] = -calculate_drag_coefficient(design) # Reduce drag (adverse)
scores['manufacturability'] = assess_production_feasibility(design)
return scores
# Coaching loop - that is the place reimagination occurs
def train_generative_designer(num_iterations=10000, batch_size=32):
"""
Prepare the mannequin to discover design area and discover novel options
"""
mannequin = GenerativeDesignVAE()
optimizer = torch.optim.Adam(mannequin.parameters(), lr=0.001)
best_designs = []
best_scores = []
for iteration in vary(num_iterations):
# Generate batch of novel designs
designs = mannequin.generate_novel_designs(batch_size=batch_size)
# Consider every design towards physics constraints
physics_scores = [evaluate_physics(d) for d in designs]
# Calculate multi-objective reward
rewards = calculate_multi_objective_reward(physics_scores)
# Loss is adverse reward (we wish to maximize reward)
loss = -rewards.imply()
# Backpropagate and replace
optimizer.zero_grad()
loss.backward()
optimizer.step()
# Monitor greatest designs
best_idx = rewards.argmax()
if len(best_scores) == 0 or rewards[best_idx] > max(best_scores):
best_designs.append(designs[best_idx].detach())
best_scores.append(rewards[best_idx].merchandise())
if iteration % 1000 == 0:
print(f"Iteration {iteration}: Finest reward = {max(best_scores):.4f}")
return mannequin, best_designs, best_scores
# Instance utilization
if __name__ == "__main__":
print("Coaching generative design mannequin...")
mannequin, best_designs, scores = train_generative_designer(
num_iterations=5000,
batch_size=16
)
print(f"nFound {len(best_designs)} novel designs")
print(f"Finest rating achieved: {max(scores):.4f}")See the distinction? The primary strategy optimizes inside a predefined design area. The second explores the whole risk of area, searching for options people wouldn’t naturally contemplate.
The important thing perception: optimization assumes you understand what beauty like. Reimagination discovers what good might be.
Actual-World Examples of Reimagination
Autodesk demonstrated this with their generative design of a chassis part. As an alternative of asking “how will we make this half lighter,” they requested “what’s the optimum construction to deal with these load circumstances?” The outcome: a design that decreased half depend from eight items to at least one whereas chopping weight by 50%.
The design appears alien: natural, virtually organic. That’s as a result of it’s not constrained by assumptions about how elements ought to look or how they’ve historically been manufactured. It emerged purely from bodily necessities.
Right here’s what I imply by “alien”: think about a automotive door body that doesn’t seem like a rectangle with rounded corners. As an alternative, it appears like tree branches — natural, flowing constructions that observe stress strains. In a single mission I consulted on, this strategy decreased the door body weight by 35% whereas truly enhancing crash security by 12% in comparison with conventional stamped metal designs. The engineers have been skeptical till they ran the crash simulations.
The revealing half: once I present these designs to automotive engineers, the most typical response is “clients would by no means settle for that.” However they mentioned the identical factor about Tesla’s minimalist interiors 5 years in the past. Now everybody’s copying them. They mentioned it about BMW’s kidney grilles getting bigger. They mentioned it about touchscreens changing bodily buttons. Buyer acceptance follows demonstration, not the opposite manner round.
The Chassis Paradigm
For 100 years, we’ve constructed vehicles round a basic precept: the chassis offers structural integrity, the physique offers aesthetics and aerodynamics. This made good sense whenever you wanted a inflexible body to mount a heavy engine and transmission.
However electrical automobiles don’t have these constraints. The “engine” is distributed electrical motors. The “gasoline tank” is a flat battery pack that may function a structural aspect. But most EV producers are nonetheless constructing separate chassis and our bodies as a result of that’s how we’ve at all times performed it.
If you let generative AI design automobile construction from scratch with out assuming chassis/physique separation it produces built-in designs the place construction, aerodynamics, and inside area emerge from the identical optimization course of. These designs may be 30-40% lighter and 25% extra aerodynamically environment friendly than conventional architectures.
I’ve seen these designs in confidential classes with producers. They’re bizarre. They problem each assumption about what a automotive ought to seem like. Some look extra like plane fuselages than automotive our bodies. Others have structural components that move from the roof to the ground in curves that appear random however are literally optimized for particular crash situations. And that’s precisely the purpose they’re not constrained by “that is how we’ve at all times performed it.”
The Actual Competitors Isn’t Who You Suppose
The Tesla Lesson
Conventional automakers assumed their competitors was different conventional automakers, all enjoying the identical optimization recreation with barely completely different methods. Then Tesla confirmed up and altered the foundations.
Tesla’s Giga casting course of is an ideal instance. They use AI-optimized designs to interchange 70 separate stamped and welded elements with single aluminum castings. This wasn’t potential by asking “how will we optimize our stamping course of?” It required asking “what if we rethought automobile meeting solely?”
The outcomes converse for themselves: Tesla achieved revenue margins of 16.3% in 2023, in comparison with conventional automakers averaging 5-7%. That’s not simply higher execution; it’s a unique recreation.
Let me break down what this truly means in follow:
| Metric | Conventional OEMs | Tesla | Distinction |
| Revenue Margin | 5-7% | 16.3% | +132% |
| Components per rear underbody | 70+ items | 1-2 castings | -97% |
| Meeting time | 2-3 hours | 10 minutes | -83% |
| Manufacturing CapEx per automobile | $8,000-10,000 | $3,600 | -64% |
These aren’t incremental enhancements. That is structural benefit.
The China Issue
Chinese language producers are shifting even additional. NIO’s battery-swapping stations, which change a depleted battery in underneath three minutes, emerged from asking whether or not automobile vary must be solved by means of larger batteries or completely different infrastructure. That’s a reimagination query, not an optimization query.
Take into consideration what this truly means: as a substitute of optimizing battery chemistry or charging velocity the questions each Western producer is asking, NIO requested “what if the battery doesn’t want to remain within the automotive?” This utterly sidesteps vary anxiousness, eliminates the necessity for enormous battery packs, and creates a subscription income mannequin. It’s not a greater reply to the outdated query; it’s a unique query solely.
BYD’s vertical integration — they manufacture every little thing from semiconductors to finish automobiles — permits them to make use of generative AI throughout the whole worth chain fairly than simply optimizing particular person elements. If you management the complete stack, you’ll be able to ask extra basic questions on how the items match collectively.
I’m not saying Chinese language producers will essentially win. However they’re asking completely different questions, and that’s harmful for corporations nonetheless optimizing inside outdated paradigms.
The Sample of Disruption
This is similar sample we’ve seen in each main business disruption:
Kodak had the primary digital digital camera in 1975. They buried it as a result of it might cannibalize movie gross sales and their optimization mindset couldn’t accommodate reimagination. They stored optimizing movie high quality whereas digital cameras reimagined pictures solely.
Nokia dominated cell phones by optimizing {hardware} and manufacturing. They’d the most effective construct high quality, longest battery life, most sturdy telephones. Then Apple requested whether or not telephones must be optimized for calling or for computing. Nokia stored making higher telephones; Apple made a pc that might make calls.
Blockbuster optimized their retail expertise: higher retailer layouts, extra stock, quicker checkout. Netflix requested whether or not video rental ought to occur in shops in any respect.
The expertise wasn’t the disruption. The willingness to ask completely different questions was.
And right here’s the uncomfortable reality: once I discuss to automotive executives, most can recite these examples. They know the sample. They only don’t consider it applies to them as a result of “vehicles are completely different” or “we’ve bodily constraints” or “our clients anticipate sure issues.” That’s precisely what Kodak and Nokia mentioned.
What Really Must Change
Why “Be Extra Revolutionary” Doesn’t Work
The answer isn’t merely telling automakers to “be extra modern.” I’ve sat by means of sufficient technique classes to know that everybody desires to innovate. The issue is structural.
Public corporations face quarterly earnings stress. Ford has $43 billion invested in manufacturing amenities globally. You may’t simply write that off to strive one thing new. Supplier networks anticipate a gradual provide of automobiles that look and performance like automobiles. Provider relationships are constructed round particular elements and processes. Regulatory frameworks assume vehicles may have steering wheels, pedals, and mirrors.
These aren’t excuses, they’re actual constraints that make reimagination genuinely troublesome. However some modifications are potential, even inside these constraints.
Sensible Steps Ahead
1. Create genuinely unbiased innovation models
Not “innovation labs” that report back to manufacturing engineering and get judged by manufacturing metrics. Separate entities with completely different success standards, completely different timelines, and permission to problem core assumptions. Give them actual budgets and actual autonomy.
Amazon does this with Lab126 (which created Kindle, Echo, Hearth). Google did it with X (previously Google X, which developed Waymo, Wing, Loon). These models can fail repeatedly as a result of they’re not measured by quarterly manufacturing targets. That freedom to fail is what permits reimagination.
Right here’s what this appears like structurally:
- Separate P&L: Not a price heart inside manufacturing, however its personal enterprise unit
- Totally different metrics: Measured on studying and choice worth, not rapid ROI
- 3–5-year timelines: Not quarterly or annual objectives
- Permission to cannibalize: Explicitly allowed to threaten current merchandise
- Totally different expertise: Researchers and experimenters, not manufacturing engineers
2. Accomplice with generative AI researchers
Most automotive AI deployments give attention to rapid manufacturing functions. That’s wonderful, however you additionally want groups exploring risk areas with out rapid manufacturing constraints.
Companions with universities, AI analysis labs, or create inside analysis teams that aren’t tied to particular product timelines. Allow them to ask silly questions like “what if vehicles didn’t have wheels?” Most explorations will lead nowhere. The few that lead someplace can be transformative.
Particular actions:
- Fund PhD analysis at MIT, Stanford, CMU on automotive functions of generative AI.
- Create artist-in-residence packages bringing industrial designers to work with AI researchers.
- Sponsor competitions (like DARPA Grand Problem) for radical automobile ideas.
- Publish analysis overtly attracts expertise by being the place fascinating work occurs.
3. Interact clients in another way
Cease asking clients what they need inside present paradigms. After all they’ll say they need higher vary, quicker charging, extra snug seats. These are optimization questions.
As an alternative, present them what’s potential. Tesla didn’t ask focus teams whether or not they wished a 17-inch touchscreen changing all bodily controls. They constructed it, and clients found they beloved it. Generally it’s good to present individuals the long run fairly than asking them to think about it.
Higher strategy:
- Construct idea automobiles that problem assumptions
- Let clients expertise radically completely different designs
- Measure reactions to precise prototypes, not descriptions
- Focus teams ought to react to prototypes, not think about potentialities
4. Acknowledge what recreation you’re truly enjoying
The competitors isn’t about who optimizes quickest. It’s about who’s keen to query what we’re optimizing for.
A McKinsey research discovered that 63% of automotive executives consider they’re “superior” in AI adoption, primarily citing optimization use circumstances. In the meantime, another person is utilizing the identical expertise to query whether or not we want steering wheels, whether or not automobiles must be owned or accessed, whether or not transportation must be optimized for people or communities.
These are reimagination questions. And when you’re not asking them, another person is.
Attempt This Your self: A Sensible Implementation
Wish to experiment with these ideas? Right here’s a sensible place to begin utilizing publicly accessible instruments and information.
Dataset and Methodology
The code examples on this article use artificial information for demonstration functions. For readers eager to experiment with precise generative design:
Public datasets you should use:
Instruments and frameworks:
- PyTorch or TensorFlow for neural community implementation
- Trimesh for 3D mesh processing in Python
- OpenFOAM for CFD simulation (open-source)
- FreeCAD with Python API for parametric design
Getting began:
# Set up required packages
# pip set up torch trimesh numpy matplotlib
import trimesh
import numpy as np
import torch
# Load a 3D mannequin from Thingi10K or create a easy form
def load_or_create_design():
"""
# Load a 3D mannequin or create a easy parametric form
"""
# Possibility 1: Load from file
# mesh = trimesh.load('path/to/mannequin.stl')
# Possibility 2: Create a easy parametric form
mesh = trimesh.creation.field(extents=[1.0, 0.5, 0.3])
return mesh# Convert mesh to voxel illustration
def mesh_to_voxels(mesh, decision=32):
"""
Convert 3D mesh to voxel grid for AI processing
"""
voxels = mesh.voxelized(pitch=mesh.extents.max()/decision)
return voxels.matrix
# Visualize the design
def visualize_design(voxels):
"""
Easy visualization of voxel design
"""
import matplotlib.pyplot as plt
from mpl_toolkits.mplot3d import Axes3D
fig = plt.determine(figsize=(10, 10))
ax = fig.add_subplot(111, projection='3d')
# Plot stuffed voxels
stuffed = np.the place(voxels > 0.5)
ax.scatter(stuffed[0], stuffed[1], stuffed[2], c='blue', marker='s', alpha=0.5)
ax.set_xlabel('X')
ax.set_ylabel('Y')In regards to the Writer
Nishant Arora is a Options Architect at Amazon Net Companies specializing in Automotive and Manufacturing industries, the place he helps corporations implement AI and cloud applied sciences
