With pairwise summation, how many terms do I need to get an appreciably wrong result?

Depth 1432 (so 2^1432 terms) suffices for the true sum to exceed the computed sum by a factor of two.

I had an idea for how to determine the number of terms needed to less than a factor of two.

We use dynamic programming to answer the following question: given a depth d and a target floating point sum s, what is the largest true sum of 2^d nonnegative float16s with pairwise sum s?

Let that quantity be T(d, s). We get a recurrence

T(0, s) = s,    for all s.
T(d, s) =            max            (T(d-1, a) + T(d-1, b)),    for all d, s.
          a, b : float16(a + b) = s

Each step of the recurrence will involve looping over about 2^29 combinations (since we can assume a ≤ b, and negative floats and special values are off limits), and the depth required won't exceed 10^4 or so by Hans's and your answer. Seems feasible to me.

DP code:

#include <algorithm>
#include <cstdio>
#include <vector>

using Float16 = int;
using Fixed = unsigned long long;

static constexpr int kExponentBits = 5;
static constexpr int kFractionBits = 10;
static constexpr Float16 kInfinity = ((1 << kExponentBits) - 1)
                                     << kFractionBits;

Fixed FixedFromFloat16(Float16 a) {
  int exponent = a >> kFractionBits;
  if (exponent == 0) {
    return a;
  }
  Float16 fraction = a - (exponent << kFractionBits);
  Float16 significand = (1 << kFractionBits) + fraction;
  return static_cast<Fixed>(significand) << (exponent - 1);
}

bool Plus(Float16 a, Float16 b, Float16* c) {
  Fixed exact_sum = FixedFromFloat16(a) + FixedFromFloat16(b);
  int exponent = 64 - kFractionBits - __builtin_clzll(exact_sum);
  if (exponent <= 0) {
    *c = static_cast<Float16>(exact_sum);
    return true;
  }
  Fixed ulp = Fixed{1} << (exponent - 1);
  Fixed remainder = exact_sum & (ulp - 1);
  Fixed rounded_sum = exact_sum - remainder;
  if (2 * remainder > ulp ||
      (2 * remainder == ulp && (rounded_sum & ulp) != 0)) {
    rounded_sum += ulp;
  }
  exponent = 64 - kFractionBits - __builtin_clzll(rounded_sum);
  if (exponent >= (1 << kExponentBits) - 1) {
    return false;
  }
  Float16 significand = rounded_sum >> (exponent - 1);
  Float16 fraction = significand - (Float16{1} << kFractionBits);
  *c = (exponent << kFractionBits) + fraction;
  return true;
}

int main() {
  std::vector<Fixed> greatest0(kInfinity);
  for (Float16 a = 0; a < kInfinity; a++) {
    greatest0[a] = FixedFromFloat16(a);
  }
  for (int depth = 1; true; depth++) {
    auto greatest1 = greatest0;
    for (Float16 a = 1; a < kInfinity; a++) {
      Fixed greatest0_a = greatest0[a];
      for (Float16 b = a; b < kInfinity; b++) {
        Float16 c;
        if (!Plus(a, b, &c)) {
          continue;
        }
        Fixed& value = greatest1[c];
        value = std::max(value, greatest0_a + greatest0[b]);
      }
    }

    std::vector<double> ratios;
    ratios.reserve(kInfinity - 1);
    for (Float16 a = 1; a < kInfinity; a++) {
      ratios.push_back(greatest1[a] / static_cast<double>(FixedFromFloat16(a)));
    }
    std::printf("depth %d, ratio = %.17g\n", depth,
                *std::max_element(ratios.begin(), ratios.end()));
    greatest0.swap(greatest1);
  }
}

I'll run this and post an update when it's done.

It would take such a large number of terms that it's effectively impossible (if zeros are allowed) or actually impossible (if zeros are not allowed, due to overflow). Wikipedia summarizes some error bounds due to Nicolas Higham. Since all of the terms are nonnegative, the condition number is 1, hence the relative error for n terms is bounded as |E_n|/|S_n| ≤ ε log₂ n / (1 - ε log₂ n), where ε is the machine epsilon. To be off by a factor of two, we would need |E_n| ≥ |S_n|, which is possible only if ε log₂ n ≥ 1/2, which is equivalent to n ≥ 2^{1/(2 ε)} = 2¹⁰²⁴ for float16.